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Sample records for random models estimated

  1. Nonparametric Estimation of Distributions in Random Effects Models

    KAUST Repository

    Hart, Jeffrey D.

    2011-01-01

    We propose using minimum distance to obtain nonparametric estimates of the distributions of components in random effects models. A main setting considered is equivalent to having a large number of small datasets whose locations, and perhaps scales, vary randomly, but which otherwise have a common distribution. Interest focuses on estimating the distribution that is common to all datasets, knowledge of which is crucial in multiple testing problems where a location/scale invariant test is applied to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article, including R-code and a dataset, are available online. © 2011 American Statistical Association.

  2. Estimating overall exposure effects for the clustered and censored outcome using random effect Tobit regression models.

    Science.gov (United States)

    Wang, Wei; Griswold, Michael E

    2016-11-30

    The random effect Tobit model is a regression model that accommodates both left- and/or right-censoring and within-cluster dependence of the outcome variable. Regression coefficients of random effect Tobit models have conditional interpretations on a constructed latent dependent variable and do not provide inference of overall exposure effects on the original outcome scale. Marginalized random effects model (MREM) permits likelihood-based estimation of marginal mean parameters for the clustered data. For random effect Tobit models, we extend the MREM to marginalize over both the random effects and the normal space and boundary components of the censored response to estimate overall exposure effects at population level. We also extend the 'Average Predicted Value' method to estimate the model-predicted marginal means for each person under different exposure status in a designated reference group by integrating over the random effects and then use the calculated difference to assess the overall exposure effect. The maximum likelihood estimation is proposed utilizing a quasi-Newton optimization algorithm with Gauss-Hermite quadrature to approximate the integration of the random effects. We use these methods to carefully analyze two real datasets. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  3. Simple, efficient estimators of treatment effects in randomized trials using generalized linear models to leverage baseline variables.

    Science.gov (United States)

    Rosenblum, Michael; van der Laan, Mark J

    2010-04-01

    Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation.

  4. Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

    Science.gov (United States)

    Rosenblum, Michael; van der Laan, Mark J.

    2010-01-01

    Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

  5. Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

    Science.gov (United States)

    Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

    2016-01-01

    Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

  6. Random Decrement Based FRF Estimation

    DEFF Research Database (Denmark)

    Brincker, Rune; Asmussen, J. C.

    to speed and quality. The basis of the new method is the Fourier transformation of the Random Decrement functions which can be used to estimate the frequency response functions. The investigations are based on load and response measurements of a laboratory model of a 3 span bridge. By applying both methods...... that the Random Decrement technique is based on a simple controlled averaging of time segments of the load and response processes. Furthermore, the Random Decrement technique is expected to produce reliable results. The Random Decrement technique will reduce leakage, since the Fourier transformation...

  7. Random Decrement Based FRF Estimation

    DEFF Research Database (Denmark)

    Brincker, Rune; Asmussen, J. C.

    1997-01-01

    to speed and quality. The basis of the new method is the Fourier transformation of the Random Decrement functions which can be used to estimate the frequency response functions. The investigations are based on load and response measurements of a laboratory model of a 3 span bridge. By applying both methods...... that the Random Decrement technique is based on a simple controlled averaging of time segments of the load and response processes. Furthermore, the Random Decrement technique is expected to produce reliable results. The Random Decrement technique will reduce leakage, since the Fourier transformation...

  8. Semi-parametric estimation of random effects in a logistic regression model using conditional inference

    DEFF Research Database (Denmark)

    Petersen, Jørgen Holm

    2016-01-01

    This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied...

  9. The limiting behavior of the estimated parameters in a misspecified random field regression model

    DEFF Research Database (Denmark)

    Dahl, Christian Møller; Qin, Yu

    This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection of n...

  10. Stable Graphical Model Estimation with Random Forests for Discrete, Continuous, and Mixed Variables

    OpenAIRE

    Fellinghauer, Bernd; Bühlmann, Peter; Ryffel, Martin; von Rhein, Michael; Reinhardt, Jan D.

    2011-01-01

    A conditional independence graph is a concise representation of pairwise conditional independence among many variables. Graphical Random Forests (GRaFo) are a novel method for estimating pairwise conditional independence relationships among mixed-type, i.e. continuous and discrete, variables. The number of edges is a tuning parameter in any graphical model estimator and there is no obvious number that constitutes a good choice. Stability Selection helps choosing this parameter with respect to...

  11. Estimation of genetic parameters related to eggshell strength using random regression models.

    Science.gov (United States)

    Guo, J; Ma, M; Qu, L; Shen, M; Dou, T; Wang, K

    2015-01-01

    This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen's laying life using random regression. The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens. Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week. The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials. The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67. The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues. The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

  12. Spectral Estimation by the Random Dec Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Jensen, Jacob L.; Krenk, Steen

    1990-01-01

    This paper contains an empirical study of the accuracy of the Random Dec (RDD) technique. Realizations of the response from a single-degree-of-freedom system loaded by white noise are simulated using an ARMA model. The Autocorrelation function is estimated using the RDD technique and the estimated...

  13. Spectral Estimation by the Random DEC Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Jensen, J. Laigaard; Krenk, S.

    This paper contains an empirical study of the accuracy of the Random Dec (RDD) technique. Realizations of the response from a single-degree-of-freedom system loaded by white noise are simulated using an ARMA model. The Autocorrelation function is estimated using the RDD technique and the estimated...

  14. Nonparametric estimation of the heterogeneity of a random medium using compound Poisson process modeling of wave multiple scattering.

    Science.gov (United States)

    Le Bihan, Nicolas; Margerin, Ludovic

    2009-07-01

    In this paper, we present a nonparametric method to estimate the heterogeneity of a random medium from the angular distribution of intensity of waves transmitted through a slab of random material. Our approach is based on the modeling of forward multiple scattering using compound Poisson processes on compact Lie groups. The estimation technique is validated through numerical simulations based on radiative transfer theory.

  15. A Structural Modeling Approach to a Multilevel Random Coefficients Model.

    Science.gov (United States)

    Rovine, Michael J.; Molenaar, Peter C. M.

    2000-01-01

    Presents a method for estimating the random coefficients model using covariance structure modeling and allowing one to estimate both fixed and random effects. The method is applied to real and simulated data, including marriage data from J. Belsky and M. Rovine (1990). (SLD)

  16. Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag

    2016-10-06

    In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and, hence, the solution of the estimation problem. Relying on the randomness of the model matrix, a number of deterministic equivalents from random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various estimated signal characteristics. In addition, simulations show that our approach is robust in the presence of model uncertainty.

  17. Note on an Identity Between Two Unbiased Variance Estimators for the Grand Mean in a Simple Random Effects Model.

    Science.gov (United States)

    Levin, Bruce; Leu, Cheng-Shiun

    2013-01-01

    We demonstrate the algebraic equivalence of two unbiased variance estimators for the sample grand mean in a random sample of subjects from an infinite population where subjects provide repeated observations following a homoscedastic random effects model.

  18. Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

    Directory of Open Access Journals (Sweden)

    Maria Gabriela Campolina Diniz Peixoto

    2014-05-01

    Full Text Available The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524 of test-day milk yield (TDMY from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects, whereas the contemporary group, calving age (linear and quadratic effects and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

  19. Estimating safety effects of pavement management factors utilizing Bayesian random effect models.

    Science.gov (United States)

    Jiang, Ximiao; Huang, Baoshan; Zaretzki, Russell L; Richards, Stephen; Yan, Xuedong

    2013-01-01

    Previous studies of pavement management factors that relate to the occurrence of traffic-related crashes are rare. Traditional research has mostly employed summary statistics of bidirectional pavement quality measurements in extended longitudinal road segments over a long time period, which may cause a loss of important information and result in biased parameter estimates. The research presented in this article focuses on crash risk of roadways with overall fair to good pavement quality. Real-time and location-specific data were employed to estimate the effects of pavement management factors on the occurrence of crashes. This research is based on the crash data and corresponding pavement quality data for the Tennessee state route highways from 2004 to 2009. The potential temporal and spatial correlations among observations caused by unobserved factors were considered. Overall 6 models were built accounting for no correlation, temporal correlation only, and both the temporal and spatial correlations. These models included Poisson, negative binomial (NB), one random effect Poisson and negative binomial (OREP, ORENB), and two random effect Poisson and negative binomial (TREP, TRENB) models. The Bayesian method was employed to construct these models. The inference is based on the posterior distribution from the Markov chain Monte Carlo (MCMC) simulation. These models were compared using the deviance information criterion. Analysis of the posterior distribution of parameter coefficients indicates that the pavement management factors indexed by Present Serviceability Index (PSI) and Pavement Distress Index (PDI) had significant impacts on the occurrence of crashes, whereas the variable rutting depth was not significant. Among other factors, lane width, median width, type of terrain, and posted speed limit were significant in affecting crash frequency. The findings of this study indicate that a reduction in pavement roughness would reduce the likelihood of traffic

  20. Optimal covariance selection for estimation using graphical models

    OpenAIRE

    Vichik, Sergey; Oshman, Yaakov

    2011-01-01

    We consider a problem encountered when trying to estimate a Gaussian random field using a distributed estimation approach based on Gaussian graphical models. Because of constraints imposed by estimation tools used in Gaussian graphical models, the a priori covariance of the random field is constrained to embed conditional independence constraints among a significant number of variables. The problem is, then: given the (unconstrained) a priori covariance of the random field, and the conditiona...

  1. ESTIMATION OF GENETIC PARAMETERS IN TROPICARNE CATTLE WITH RANDOM REGRESSION MODELS USING B-SPLINES

    Directory of Open Access Journals (Sweden)

    Joel Domínguez Viveros

    2015-04-01

    Full Text Available The objectives were to estimate variance components, and direct (h2 and maternal (m2 heritability in the growth of Tropicarne cattle based on a random regression model using B-Splines for random effects modeling. Information from 12 890 monthly weightings of 1787 calves, from birth to 24 months old, was analyzed. The pedigree included 2504 animals. The random effects model included genetic and permanent environmental (direct and maternal of cubic order, and residuals. The fixed effects included contemporaneous groups (year – season of weighed, sex and the covariate age of the cow (linear and quadratic. The B-Splines were defined in four knots through the growth period analyzed. Analyses were performed with the software Wombat. The variances (phenotypic and residual presented a similar behavior; of 7 to 12 months of age had a negative trend; from birth to 6 months and 13 to 18 months had positive trend; after 19 months were maintained constant. The m2 were low and near to zero, with an average of 0.06 in an interval of 0.04 to 0.11; the h2 also were close to zero, with an average of 0.10 in an interval of 0.03 to 0.23.

  2. A Two-Stage Estimation Method for Random Coefficient Differential Equation Models with Application to Longitudinal HIV Dynamic Data.

    Science.gov (United States)

    Fang, Yun; Wu, Hulin; Zhu, Li-Xing

    2011-07-01

    We propose a two-stage estimation method for random coefficient ordinary differential equation (ODE) models. A maximum pseudo-likelihood estimator (MPLE) is derived based on a mixed-effects modeling approach and its asymptotic properties for population parameters are established. The proposed method does not require repeatedly solving ODEs, and is computationally efficient although it does pay a price with the loss of some estimation efficiency. However, the method does offer an alternative approach when the exact likelihood approach fails due to model complexity and high-dimensional parameter space, and it can also serve as a method to obtain the starting estimates for more accurate estimation methods. In addition, the proposed method does not need to specify the initial values of state variables and preserves all the advantages of the mixed-effects modeling approach. The finite sample properties of the proposed estimator are studied via Monte Carlo simulations and the methodology is also illustrated with application to an AIDS clinical data set.

  3. A Note on the Correlated Random Coefficient Model

    DEFF Research Database (Denmark)

    Kolodziejczyk, Christophe

    In this note we derive the bias of the OLS estimator for a correlated random coefficient model with one random coefficient, but which is correlated with a binary variable. We provide set-identification to the parameters of interest of the model. We also show how to reduce the bias of the estimator...

  4. A random effects meta-analysis model with Box-Cox transformation.

    Science.gov (United States)

    Yamaguchi, Yusuke; Maruo, Kazushi; Partlett, Christopher; Riley, Richard D

    2017-07-19

    In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and a wrongly symmetric prediction interval. We focus on problems caused by an inappropriate normality assumption of the random effects distribution, and propose a novel random effects meta-analysis model where a Box-Cox transformation is applied to the observed treatment effect estimates. The proposed model aims to normalise an overall distribution of observed treatment effect estimates, which is sum of the within-study sampling distributions and the random effects distribution. When sampling distributions are approximately normal, non-normality in the overall distribution will be mainly due to the random effects distribution, especially when the between-study variation is large relative to the within-study variation. The Box-Cox transformation addresses this flexibly according to the observed departure from normality. We use a Bayesian approach for estimating parameters in the proposed model, and suggest summarising the meta-analysis results by an overall median, an interquartile range and a prediction interval. The model can be applied for any kind of variables once the treatment effect estimate is defined from the variable. A simulation study suggested that when the overall distribution of treatment effect estimates are skewed, the overall mean and conventional I 2 from the normal random effects model could be inappropriate summaries, and the proposed model helped reduce this issue. We illustrated the proposed model using two examples, which revealed some important differences on summary results, heterogeneity measures and prediction intervals from the normal random effects model. The

  5. A random effects meta-analysis model with Box-Cox transformation

    Directory of Open Access Journals (Sweden)

    Yusuke Yamaguchi

    2017-07-01

    Full Text Available Abstract Background In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and a wrongly symmetric prediction interval. Methods We focus on problems caused by an inappropriate normality assumption of the random effects distribution, and propose a novel random effects meta-analysis model where a Box-Cox transformation is applied to the observed treatment effect estimates. The proposed model aims to normalise an overall distribution of observed treatment effect estimates, which is sum of the within-study sampling distributions and the random effects distribution. When sampling distributions are approximately normal, non-normality in the overall distribution will be mainly due to the random effects distribution, especially when the between-study variation is large relative to the within-study variation. The Box-Cox transformation addresses this flexibly according to the observed departure from normality. We use a Bayesian approach for estimating parameters in the proposed model, and suggest summarising the meta-analysis results by an overall median, an interquartile range and a prediction interval. The model can be applied for any kind of variables once the treatment effect estimate is defined from the variable. Results A simulation study suggested that when the overall distribution of treatment effect estimates are skewed, the overall mean and conventional I 2 from the normal random effects model could be inappropriate summaries, and the proposed model helped reduce this issue. We illustrated the proposed model using two examples, which revealed some important differences on summary results, heterogeneity measures and

  6. Genetic Parameters for Body condition score, Body weigth, Milk yield and Fertility estimated using random regression models

    NARCIS (Netherlands)

    Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.

    2003-01-01

    Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields

  7. Simulating WTP Values from Random-Coefficient Models

    OpenAIRE

    Maurus Rischatsch

    2009-01-01

    Discrete Choice Experiments (DCEs) designed to estimate willingness-to-pay (WTP) values are very popular in health economics. With increased computation power and advanced simulation techniques, random-coefficient models have gained an increasing importance in applied work as they allow for taste heterogeneity. This paper discusses the parametrical derivation of WTP values from estimated random-coefficient models and shows how these values can be simulated in cases where they do not have a kn...

  8. Nonlinear Estimation of Discrete-Time Signals Under Random Observation Delay

    International Nuclear Information System (INIS)

    Caballero-Aguila, R.; Jimenez-Lopez, J. D.; Hermoso-Carazo, A.; Linares-Perez, J.; Nakamori, S.

    2008-01-01

    This paper presents an approximation to the nonlinear least-squares estimation problem of discrete-time stochastic signals using nonlinear observations with additive white noise which can be randomly delayed by one sampling time. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values, zero or one, indicate that the real observation arrives on time or it is delayed and, hence, the available measurement to estimate the signal is not up-to-date. Assuming that the state-space model generating the signal is unknown and only the covariance functions of the processes involved in the observation equation are ready for use, a filtering algorithm based on linear approximations of the real observations is proposed.

  9. Assessing the potential of random forest method for estimating solar radiation using air pollution index

    International Nuclear Information System (INIS)

    Sun, Huaiwei; Gui, Dongwei; Yan, Baowei; Liu, Yi; Liao, Weihong; Zhu, Yan; Lu, Chengwei; Zhao, Na

    2016-01-01

    Highlights: • Models based on random forests for daily solar radiation estimation are proposed. • Three sites within different air pollution index conditions are considered. • Performance of random forests is better than that of empirical methodologies. • Special attention is given to the use of air pollution index. • The potential of air pollution index is assessed by random forest models. - Abstract: Simulations of solar radiation have become increasingly common in recent years because of the rapid global development and deployment of solar energy technologies. The effect of air pollution on solar radiation is well known. However, few studies have attempting to evaluate the potential of the air pollution index in estimating solar radiation. In this study, meteorological data, solar radiation, and air pollution index data from three sites having different air pollution index conditions are used to develop random forest models. We propose different random forest models with and without considering air pollution index data, and then compare their respective performance with that of empirical methodologies. In addition, a variable importance approach based on random forest is applied in order to assess input variables. The results show that the performance of random forest models with air pollution index data is better than that of the empirical methodologies, generating 9.1–17.0% lower values of root-mean-square error in a fitted period and 2.0–17.4% lower values of root-mean-square error in a predicted period. Both the comparative results of different random forest models and variance importance indicate that applying air pollution index data is improves estimation of solar radiation. Also, although the air pollution index values varied largely from season to season, the random forest models appear more robust performances in different seasons than different models. The findings can act as a guide in selecting used variables to estimate daily solar

  10. Estimation of Correlation Functions by Random Decrement

    DEFF Research Database (Denmark)

    Asmussen, J. C.; Brincker, Rune

    This paper illustrates how correlation functions can be estimated by the random decrement technique. Several different formulations of the random decrement technique, estimating the correlation functions are considered. The speed and accuracy of the different formulations of the random decrement...... and the length of the correlation functions. The accuracy of the estimates with respect to the theoretical correlation functions and the modal parameters are both investigated. The modal parameters are extracted from the correlation functions using the polyreference time domain technique....

  11. Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

    Directory of Open Access Journals (Sweden)

    C. I. Cho

    2016-05-01

    Full Text Available The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs, and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK, fat yield (FAT, protein yield (PROT, and solids-not-fat yield (SNF. The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP of the third to fifth order (L3–L5, fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order. The residual variances in the models were either homogeneous (HOM or heterogeneous (15 classes, HET15; 60 classes, HET60. A total of nine models (3 orders of polynomials×3 types of residual variance including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC and/or Schwarz Bayesian information criteria (BIC statistics to identify the model(s of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF and L4-HET15 (FAT, which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first

  12. Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages

    Science.gov (United States)

    Kim, Yoonsang; Emery, Sherry

    2013-01-01

    Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods’ performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages—SAS GLIMMIX Laplace and SuperMix Gaussian quadrature—perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes. PMID:24288415

  13. Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

    Science.gov (United States)

    Kim, Yoonsang; Choi, Young-Ku; Emery, Sherry

    2013-08-01

    Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.

  14. Some Limits Using Random Slope Models to Measure Academic Growth

    Directory of Open Access Journals (Sweden)

    Daniel B. Wright

    2017-11-01

    Full Text Available Academic growth is often estimated using a random slope multilevel model with several years of data. However, if there are few time points, the estimates can be unreliable. While using random slope multilevel models can lower the variance of the estimates, these procedures can produce more highly erroneous estimates—zero and negative correlations with the true underlying growth—than using ordinary least squares estimates calculated for each student or school individually. An example is provided where schools with increasing graduation rates are estimated to have negative growth and vice versa. The estimation is worse when the underlying data are skewed. It is recommended that there are at least six time points for estimating growth if using a random slope model. A combination of methods can be used to avoid some of the aberrant results if it is not possible to have six or more time points.

  15. Multilevel variance estimators in MLMC and application for random obstacle problems

    KAUST Repository

    Chernov, Alexey

    2014-01-06

    The Multilevel Monte Carlo Method (MLMC) is a recently established sampling approach for uncertainty propagation for problems with random parameters. In this talk we present new convergence theorems for the multilevel variance estimators. As a result, we prove that under certain assumptions on the parameters, the variance can be estimated at essentially the same cost as the mean, and consequently as the cost required for solution of one forward problem for a fixed deterministic set of parameters. We comment on fast and stable evaluation of the estimators suitable for parallel large scale computations. The suggested approach is applied to a class of scalar random obstacle problems, a prototype of contact between deformable bodies. In particular, we are interested in rough random obstacles modelling contact between car tires and variable road surfaces. Numerical experiments support and complete the theoretical analysis.

  16. Multilevel variance estimators in MLMC and application for random obstacle problems

    KAUST Repository

    Chernov, Alexey; Bierig, Claudio

    2014-01-01

    The Multilevel Monte Carlo Method (MLMC) is a recently established sampling approach for uncertainty propagation for problems with random parameters. In this talk we present new convergence theorems for the multilevel variance estimators. As a result, we prove that under certain assumptions on the parameters, the variance can be estimated at essentially the same cost as the mean, and consequently as the cost required for solution of one forward problem for a fixed deterministic set of parameters. We comment on fast and stable evaluation of the estimators suitable for parallel large scale computations. The suggested approach is applied to a class of scalar random obstacle problems, a prototype of contact between deformable bodies. In particular, we are interested in rough random obstacles modelling contact between car tires and variable road surfaces. Numerical experiments support and complete the theoretical analysis.

  17. Zero-inflated count models for longitudinal measurements with heterogeneous random effects.

    Science.gov (United States)

    Zhu, Huirong; Luo, Sheng; DeSantis, Stacia M

    2017-08-01

    Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions. Zero-inflated count models (e.g. zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data. In random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects). However, in many situations this matrix may be heterogeneous (differ by measured covariates). In this paper, we extend zero-inflated count models to account for random effects heterogeneity by modeling their variance as a function of covariates. We show via simulation that ignoring intervention and covariate-specific heterogeneity can produce biased estimates of covariate and random effect estimates. Moreover, those biased estimates can be rectified by correctly modeling the random effects covariance structure. The methodological development is motivated by and applied to the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, the largest clinical trial of alcohol dependence performed in United States with 1383 individuals.

  18. The Effect of Some Estimators of Between-Study Variance on Random

    African Journals Online (AJOL)

    Samson Henry Dogo

    the first step to such objectivity (Schmidt, 1992), allows to combine results from many studies and accurately ... Schmidt, 2000) due to its ability to account for variation in effects across the studies. Random-effects model ... (2015), and each of the estimators differs in terms of their bias and precision in estimation. By definition ...

  19. Least squares estimation in a simple random coefficient autoregressive model

    DEFF Research Database (Denmark)

    Johansen, S; Lange, T

    2013-01-01

    The question we discuss is whether a simple random coefficient autoregressive model with infinite variance can create the long swings, or persistence, which are observed in many macroeconomic variables. The model is defined by yt=stρyt−1+εt,t=1,…,n, where st is an i.i.d. binary variable with p...... we prove the curious result that View the MathML source. The proof applies the notion of a tail index of sums of positive random variables with infinite variance to find the order of magnitude of View the MathML source and View the MathML source and hence the limit of View the MathML source...

  20. Covariance-Based Estimation from Multisensor Delayed Measurements with Random Parameter Matrices and Correlated Noises

    Directory of Open Access Journals (Sweden)

    R. Caballero-Águila

    2014-01-01

    Full Text Available The optimal least-squares linear estimation problem is addressed for a class of discrete-time multisensor linear stochastic systems subject to randomly delayed measurements with different delay rates. For each sensor, a different binary sequence is used to model the delay process. The measured outputs are perturbed by both random parameter matrices and one-step autocorrelated and cross correlated noises. Using an innovation approach, computationally simple recursive algorithms are obtained for the prediction, filtering, and smoothing problems, without requiring full knowledge of the state-space model generating the signal process, but only the information provided by the delay probabilities and the mean and covariance functions of the processes (signal, random parameter matrices, and noises involved in the observation model. The accuracy of the estimators is measured by their error covariance matrices, which allow us to analyze the estimator performance in a numerical simulation example that illustrates the feasibility of the proposed algorithms.

  1. A MOSUM procedure for the estimation of multiple random change points

    OpenAIRE

    Eichinger, Birte; Kirch, Claudia

    2018-01-01

    In this work, we investigate statistical properties of change point estimators based on moving sum statistics. We extend results for testing in a classical situation with multiple deterministic change points by allowing for random exogenous change points that arise in Hidden Markov or regime switching models among others. To this end, we consider a multiple mean change model with possible time series errors and prove that the number and location of change points are estimated consistently by ...

  2. Estimation of Correlation Functions by the Random Decrement Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Krenk, Steen; Jensen, Jakob Laigaard

    responses simulated by two SDOF ARMA models loaded by the same bandlimited white noise. The speed and the accuracy of the RDD technique is compared to the Fast Fourier Transform (FFT) technique. The RDD technique does not involve multiplications, but only additions. Therefore, the technique is very fast......The Random Decrement (RDD) Technique is a versatile technique for characterization of random signals in the time domain. In this paper a short review of the theoretical basis is given, and the technique is illustrated by estimating auto-correlation functions and cross-correlation functions on modal...

  3. Estimation of Correlation Functions by the Random Decrement Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Krenk, Steen; Jensen, Jacob Laigaard

    1991-01-01

    responses simulated by two SDOF ARMA models loaded by the same band-limited white noise. The speed and the accuracy of the RDD technique is compared to the Fast Fourier Transform (FFT) technique. The RDD technique does not involve multiplications, but only additions. Therefore, the technique is very fast......The Random Decrement (RDD) Technique is a versatile technique for characterization of random signals in the time domain. In this paper a short review of the theoretical basis is given, and the technique is illustrated by estimating auto-correlation functions and cross-correlation functions on modal...

  4. Estimation of Correlation Functions by the Random Decrement Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Krenk, Steen; Jensen, Jakob Laigaard

    1992-01-01

    responses simulated by two SDOF ARMA models loaded by the same bandlimited white noise. The speed and the accuracy of the RDD technique is compared to the Fast Fourier Transform (FFT) technique. The RDD technique does not involve multiplications, but only additions. Therefore, the technique is very fast......The Random Decrement (RDD) Technique is a versatile technique for characterization of random signals in the time domain. In this paper a short review of the theoretical basis is given, and the technique is illustrated by estimating auto-correlation functions and cross-correlation functions on modal...

  5. Cover estimation and payload location using Markov random fields

    Science.gov (United States)

    Quach, Tu-Thach

    2014-02-01

    Payload location is an approach to find the message bits hidden in steganographic images, but not necessarily their logical order. Its success relies primarily on the accuracy of the underlying cover estimators and can be improved if more estimators are used. This paper presents an approach based on Markov random field to estimate the cover image given a stego image. It uses pairwise constraints to capture the natural two-dimensional statistics of cover images and forms a basis for more sophisticated models. Experimental results show that it is competitive against current state-of-the-art estimators and can locate payload embedded by simple LSB steganography and group-parity steganography. Furthermore, when combined with existing estimators, payload location accuracy improves significantly.

  6. Estimation of beam material random field properties via sensitivity-based model updating using experimental frequency response functions

    Science.gov (United States)

    Machado, M. R.; Adhikari, S.; Dos Santos, J. M. C.; Arruda, J. R. F.

    2018-03-01

    Structural parameter estimation is affected not only by measurement noise but also by unknown uncertainties which are present in the system. Deterministic structural model updating methods minimise the difference between experimentally measured data and computational prediction. Sensitivity-based methods are very efficient in solving structural model updating problems. Material and geometrical parameters of the structure such as Poisson's ratio, Young's modulus, mass density, modal damping, etc. are usually considered deterministic and homogeneous. In this paper, the distributed and non-homogeneous characteristics of these parameters are considered in the model updating. The parameters are taken as spatially correlated random fields and are expanded in a spectral Karhunen-Loève (KL) decomposition. Using the KL expansion, the spectral dynamic stiffness matrix of the beam is expanded as a series in terms of discretized parameters, which can be estimated using sensitivity-based model updating techniques. Numerical and experimental tests involving a beam with distributed bending rigidity and mass density are used to verify the proposed method. This extension of standard model updating procedures can enhance the dynamic description of structural dynamic models.

  7. Ant-inspired density estimation via random walks.

    Science.gov (United States)

    Musco, Cameron; Su, Hsin-Hao; Lynch, Nancy A

    2017-10-03

    Many ant species use distributed population density estimation in applications ranging from quorum sensing, to task allocation, to appraisal of enemy colony strength. It has been shown that ants estimate local population density by tracking encounter rates: The higher the density, the more often the ants bump into each other. We study distributed density estimation from a theoretical perspective. We prove that a group of anonymous agents randomly walking on a grid are able to estimate their density within a small multiplicative error in few steps by measuring their rates of encounter with other agents. Despite dependencies inherent in the fact that nearby agents may collide repeatedly (and, worse, cannot recognize when this happens), our bound nearly matches what would be required to estimate density by independently sampling grid locations. From a biological perspective, our work helps shed light on how ants and other social insects can obtain relatively accurate density estimates via encounter rates. From a technical perspective, our analysis provides tools for understanding complex dependencies in the collision probabilities of multiple random walks. We bound the strength of these dependencies using local mixing properties of the underlying graph. Our results extend beyond the grid to more general graphs, and we discuss applications to size estimation for social networks, density estimation for robot swarms, and random walk-based sampling for sensor networks.

  8. Stable Parameter Estimation for Autoregressive Equations with Random Coefficients

    Directory of Open Access Journals (Sweden)

    V. B. Goryainov

    2014-01-01

    Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross

  9. Linear minimax estimation for random vectors with parametric uncertainty

    KAUST Repository

    Bitar, E

    2010-06-01

    In this paper, we take a minimax approach to the problem of computing a worst-case linear mean squared error (MSE) estimate of X given Y , where X and Y are jointly distributed random vectors with parametric uncertainty in their distribution. We consider two uncertainty models, PA and PB. Model PA represents X and Y as jointly Gaussian whose covariance matrix Λ belongs to the convex hull of a set of m known covariance matrices. Model PB characterizes X and Y as jointly distributed according to a Gaussian mixture model with m known zero-mean components, but unknown component weights. We show: (a) the linear minimax estimator computed under model PA is identical to that computed under model PB when the vertices of the uncertain covariance set in PA are the same as the component covariances in model PB, and (b) the problem of computing the linear minimax estimator under either model reduces to a semidefinite program (SDP). We also consider the dynamic situation where x(t) and y(t) evolve according to a discrete-time LTI state space model driven by white noise, the statistics of which is modeled by PA and PB as before. We derive a recursive linear minimax filter for x(t) given y(t).

  10. Estimation of Rice Crop Yields Using Random Forests in Taiwan

    Science.gov (United States)

    Chen, C. F.; Lin, H. S.; Nguyen, S. T.; Chen, C. R.

    2017-12-01

    Rice is globally one of the most important food crops, directly feeding more people than any other crops. Rice is not only the most important commodity, but also plays a critical role in the economy of Taiwan because it provides employment and income for large rural populations. The rice harvested area and production are thus monitored yearly due to the government's initiatives. Agronomic planners need such information for more precise assessment of food production to tackle issues of national food security and policymaking. This study aimed to develop a machine-learning approach using physical parameters to estimate rice crop yields in Taiwan. We processed the data for 2014 cropping seasons, following three main steps: (1) data pre-processing to construct input layers, including soil types and weather parameters (e.g., maxima and minima air temperature, precipitation, and solar radiation) obtained from meteorological stations across the country; (2) crop yield estimation using the random forests owing to its merits as it can process thousands of variables, estimate missing data, maintain the accuracy level when a large proportion of the data is missing, overcome most of over-fitting problems, and run fast and efficiently when handling large datasets; and (3) error verification. To execute the model, we separated the datasets into two groups of pixels: group-1 (70% of pixels) for training the model and group-2 (30% of pixels) for testing the model. Once the model is trained to produce small and stable out-of-bag error (i.e., the mean squared error between predicted and actual values), it can be used for estimating rice yields of cropping seasons. The results obtained from the random forests-based regression were compared with the actual yield statistics indicated the values of root mean square error (RMSE) and mean absolute error (MAE) achieved for the first rice crop were respectively 6.2% and 2.7%, while those for the second rice crop were 5.3% and 2

  11. Maximum Simulated Likelihood and Expectation-Maximization Methods to Estimate Random Coefficients Logit with Panel Data

    DEFF Research Database (Denmark)

    Cherchi, Elisabetta; Guevara, Cristian

    2012-01-01

    with cross-sectional or with panel data, and (d) EM systematically attained more efficient estimators than the MSL method. The results imply that if the purpose of the estimation is only to determine the ratios of the model parameters (e.g., the value of time), the EM method should be preferred. For all......The random coefficients logit model allows a more realistic representation of agents' behavior. However, the estimation of that model may involve simulation, which may become impractical with many random coefficients because of the curse of dimensionality. In this paper, the traditional maximum...... simulated likelihood (MSL) method is compared with the alternative expectation- maximization (EM) method, which does not require simulation. Previous literature had shown that for cross-sectional data, MSL outperforms the EM method in the ability to recover the true parameters and estimation time...

  12. Optimal state estimation over communication channels with random delays

    KAUST Repository

    Mahmoud, Magdi S.; Liu, Bo

    2013-01-01

    This paper is concerned with the optimal estimation of linear systems over unreliable communication channels with random delays. The measurements are delivered without time stamp, and the probabilities of time delays are assumed to be known. Since the estimation is time-driven, the actual time delays are converted into virtual time delays among the formulation. The receiver of estimation node stores the sum of arrived measurements between two adjacent processing time instants and also counts the number of arrived measurements. The original linear system is modeled as an extended system with uncertain observation to capture the feature of communication, then the optimal estimation algorithm of systems with uncertain observations is proposed. Additionally, a numerical simulation is presented to show the performance of this work. © 2013 The Franklin Institute.

  13. Optimal state estimation over communication channels with random delays

    KAUST Repository

    Mahmoud, Magdi S.

    2013-04-01

    This paper is concerned with the optimal estimation of linear systems over unreliable communication channels with random delays. The measurements are delivered without time stamp, and the probabilities of time delays are assumed to be known. Since the estimation is time-driven, the actual time delays are converted into virtual time delays among the formulation. The receiver of estimation node stores the sum of arrived measurements between two adjacent processing time instants and also counts the number of arrived measurements. The original linear system is modeled as an extended system with uncertain observation to capture the feature of communication, then the optimal estimation algorithm of systems with uncertain observations is proposed. Additionally, a numerical simulation is presented to show the performance of this work. © 2013 The Franklin Institute.

  14. The estimation of branching curves in the presence of subject-specific random effects.

    Science.gov (United States)

    Elmi, Angelo; Ratcliffe, Sarah J; Guo, Wensheng

    2014-12-20

    Branching curves are a technique for modeling curves that change trajectory at a change (branching) point. Currently, the estimation framework is limited to independent data, and smoothing splines are used for estimation. This article aims to extend the branching curve framework to the longitudinal data setting where the branching point varies by subject. If the branching point is modeled as a random effect, then the longitudinal branching curve framework is a semiparametric nonlinear mixed effects model. Given existing issues with using random effects within a smoothing spline, we express the model as a B-spline based semiparametric nonlinear mixed effects model. Simple, clever smoothness constraints are enforced on the B-splines at the change point. The method is applied to Women's Health data where we model the shape of the labor curve (cervical dilation measured longitudinally) before and after treatment with oxytocin (a labor stimulant). Copyright © 2014 John Wiley & Sons, Ltd.

  15. Parameter estimation in fractional diffusion models

    CERN Document Server

    Kubilius, Kęstutis; Ralchenko, Kostiantyn

    2017-01-01

    This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is “white,” i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides s...

  16. A dynamic random effects multinomial logit model of household car ownership

    DEFF Research Database (Denmark)

    Bue Bjørner, Thomas; Leth-Petersen, Søren

    2007-01-01

    Using a large household panel we estimate demand for car ownership by means of a dynamic multinomial model with correlated random effects. Results suggest that the persistence in car ownership observed in the data should be attributed to both true state dependence and to unobserved heterogeneity...... (random effects). It also appears that random effects related to single and multiple car ownership are correlated, suggesting that the IIA assumption employed in simple multinomial models of car ownership is invalid. Relatively small elasticities with respect to income and car costs are estimated...

  17. Estimating required information size by quantifying diversity in random-effects model meta-analyses

    DEFF Research Database (Denmark)

    Wetterslev, Jørn; Thorlund, Kristian; Brok, Jesper

    2009-01-01

    an intervention effect suggested by trials with low-risk of bias. METHODS: Information size calculations need to consider the total model variance in a meta-analysis to control type I and type II errors. Here, we derive an adjusting factor for the required information size under any random-effects model meta......-analysis. RESULTS: We devise a measure of diversity (D2) in a meta-analysis, which is the relative variance reduction when the meta-analysis model is changed from a random-effects into a fixed-effect model. D2 is the percentage that the between-trial variability constitutes of the sum of the between...... and interpreted using several simulations and clinical examples. In addition we show mathematically that diversity is equal to or greater than inconsistency, that is D2 >or= I2, for all meta-analyses. CONCLUSION: We conclude that D2 seems a better alternative than I2 to consider model variation in any random...

  18. Random effects coefficient of determination for mixed and meta-analysis models.

    Science.gov (United States)

    Demidenko, Eugene; Sargent, James; Onega, Tracy

    2012-01-01

    The key feature of a mixed model is the presence of random effects. We have developed a coefficient, called the random effects coefficient of determination, [Formula: see text], that estimates the proportion of the conditional variance of the dependent variable explained by random effects. This coefficient takes values from 0 to 1 and indicates how strong the random effects are. The difference from the earlier suggested fixed effects coefficient of determination is emphasized. If [Formula: see text] is close to 0, there is weak support for random effects in the model because the reduction of the variance of the dependent variable due to random effects is small; consequently, random effects may be ignored and the model simplifies to standard linear regression. The value of [Formula: see text] apart from 0 indicates the evidence of the variance reduction in support of the mixed model. If random effects coefficient of determination is close to 1 the variance of random effects is very large and random effects turn into free fixed effects-the model can be estimated using the dummy variable approach. We derive explicit formulas for [Formula: see text] in three special cases: the random intercept model, the growth curve model, and meta-analysis model. Theoretical results are illustrated with three mixed model examples: (1) travel time to the nearest cancer center for women with breast cancer in the U.S., (2) cumulative time watching alcohol related scenes in movies among young U.S. teens, as a risk factor for early drinking onset, and (3) the classic example of the meta-analysis model for combination of 13 studies on tuberculosis vaccine.

  19. Fatigue life estimation of MD36 and MD523 bogies based on damage accumulation and random fatigue theory

    International Nuclear Information System (INIS)

    Younesian, Davood; Solhmirzaei, Ali; Gachloo, Alireza

    2009-01-01

    Bogies are one of the multifunctional parts of trains which are extremely subjected to random loads. This type of oscillating and random excitation arises from irregularities of the track including rail surface vertical roughness, rail joints, variance in super-elevation, and also wheel imperfections like wheel flats and unbalancy. Since most of the prementioned sources have random nature, a random based theory should be applied for fatigue life estimation of the bogie frame. Two methods of fatigue life estimation are investigated in this paper. The first approach which is being implemented in time domain is based on the damage accumulation (DA) approach. Using Monte-Carlo simulation algorithm, the rail surface roughness is generated. Finite element (FE) model of the bogie is subjected to the generated random excitation in the first approach and the stress time histories are obtained, and consequently the fatigue life is estimated by using the rain-flow algorithm. In the second approach, the fatigue life is estimated in frequency domain. Power spectral density (PSD) of the stress is obtained by using the FE model of the bogie frame and the fatigue life is estimated using Rayleigh technique in random fatigue theory. A comprehensive parametric study is carried out and effects of different parameters like the train speeds and level of the rail surface vertical roughness on the estimated fatigue life are investigated

  20. ON THE ESTIMATION OF DISTANCE DISTRIBUTION FUNCTIONS FOR POINT PROCESSES AND RANDOM SETS

    Directory of Open Access Journals (Sweden)

    Dietrich Stoyan

    2011-05-01

    Full Text Available This paper discusses various estimators for the nearest neighbour distance distribution function D of a stationary point process and for the quadratic contact distribution function Hq of a stationary random closed set. It recommends the use of Hanisch's estimator of D, which is of Horvitz-Thompson type, and the minussampling estimator of Hq. This recommendation is based on simulations for Poisson processes and Boolean models.

  1. Particle filters for random set models

    CERN Document Server

    Ristic, Branko

    2013-01-01

    “Particle Filters for Random Set Models” presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based  on the Monte Carlo statistical method. The resulting  algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from  navigation and autonomous vehicles to bio-informatics and finance. While particle filters have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models. This book...

  2. NONLINEAR PLANT PIECEWISE-CONTINUOUS MODEL MATRIX PARAMETERS ESTIMATION

    Directory of Open Access Journals (Sweden)

    Roman L. Leibov

    2017-09-01

    Full Text Available This paper presents a nonlinear plant piecewise-continuous model matrix parameters estimation technique using nonlinear model time responses and random search method. One of piecewise-continuous model application areas is defined. The results of proposed approach application for aircraft turbofan engine piecewisecontinuous model formation are presented

  3. An unbiased estimator of the variance of simple random sampling using mixed random-systematic sampling

    OpenAIRE

    Padilla, Alberto

    2009-01-01

    Systematic sampling is a commonly used technique due to its simplicity and ease of implementation. The drawback of this simplicity is that it is not possible to estimate the design variance without bias. There are several ways to circumvent this problem. One method is to suppose that the variable of interest has a random order in the population, so the sample variance of simple random sampling without replacement is used. By means of a mixed random - systematic sample, an unbiased estimator o...

  4. RMBNToolbox: random models for biochemical networks

    Directory of Open Access Journals (Sweden)

    Niemi Jari

    2007-05-01

    Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

  5. Random walk-percolation-based modeling of two-phase flow in porous media: Breakthrough time and net to gross ratio estimation

    Science.gov (United States)

    Ganjeh-Ghazvini, Mostafa; Masihi, Mohsen; Ghaedi, Mojtaba

    2014-07-01

    Fluid flow modeling in porous media has many applications in waste treatment, hydrology and petroleum engineering. In any geological model, flow behavior is controlled by multiple properties. These properties must be known in advance of common flow simulations. When uncertainties are present, deterministic modeling often produces poor results. Percolation and Random Walk (RW) methods have recently been used in flow modeling. Their stochastic basis is useful in dealing with uncertainty problems. They are also useful in finding the relationship between porous media descriptions and flow behavior. This paper employs a simple methodology based on random walk and percolation techniques. The method is applied to a well-defined model reservoir in which the breakthrough time distributions are estimated. The results of this method and the conventional simulation are then compared. The effect of the net to gross ratio on the breakthrough time distribution is studied in terms of Shannon entropy. Use of the entropy plot allows one to assign the appropriate net to gross ratio to any porous medium.

  6. Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials

    Directory of Open Access Journals (Sweden)

    Nils Ternès

    2017-05-01

    Full Text Available Abstract Background Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions. Methods Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso, we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation; estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap; and visualize them graphically (pointwise or smoothed with spline. We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured. Results In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4

  7. Estimation of Genetic Parameters for First Lactation Monthly Test-day Milk Yields using Random Regression Test Day Model in Karan Fries Cattle

    Directory of Open Access Journals (Sweden)

    Ajay Singh

    2016-06-01

    Full Text Available A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM considering different order of Legendre polynomial for the additive genetic effect (4th order and the permanent environmental effect (5th order. Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11 to 0.99 (TD-4 and TD-5. The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields.

  8. Estimation of genotype X environment interactions, in a grassbased system, for milk yield, body condition score,and body weight using random regression models

    NARCIS (Netherlands)

    Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.

    2003-01-01

    (Co)variance components for milk yield, body condition score (BCS), body weight (BW), BCS change and BW change over different herd-year mean milk yields (HMY) and nutritional environments (concentrate feeding level, grazing severity and silage quality) were estimated using a random regression model.

  9. An alternative procedure for estimating the population mean in simple random sampling

    Directory of Open Access Journals (Sweden)

    Housila P. Singh

    2012-03-01

    Full Text Available This paper deals with the problem of estimating the finite population mean using auxiliary information in simple random sampling. Firstly we have suggested a correction to the mean squared error of the estimator proposed by Gupta and Shabbir [On improvement in estimating the population mean in simple random sampling. Jour. Appl. Statist. 35(5 (2008, pp. 559-566]. Later we have proposed a ratio type estimator and its properties are studied in simple random sampling. Numerically we have shown that the proposed class of estimators is more efficient than different known estimators including Gupta and Shabbir (2008 estimator.

  10. A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution.

    Science.gov (United States)

    Harrison, Xavier A

    2015-01-01

    Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling overdispersion when it is present is therefore critical for robust biological inference. One means to account for overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (model types under a range of random effect sample sizes when overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the overdispersion; OLRE failed to cope with overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non-Beta-Binomial data. Finally, both OLRE and Beta-Binomial models performed

  11. Random forests of interaction trees for estimating individualized treatment effects in randomized trials.

    Science.gov (United States)

    Su, Xiaogang; Peña, Annette T; Liu, Lei; Levine, Richard A

    2018-04-29

    Assessing heterogeneous treatment effects is a growing interest in advancing precision medicine. Individualized treatment effects (ITEs) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees. To this end, we propose a smooth sigmoid surrogate method, as an alternative to greedy search, to speed up tree construction. The RFIT outperforms the "separate regression" approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT are obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial. Copyright © 2018 John Wiley & Sons, Ltd.

  12. An efficient estimator for Gibbs random fields

    Czech Academy of Sciences Publication Activity Database

    Janžura, Martin

    2014-01-01

    Roč. 50, č. 6 (2014), s. 883-895 ISSN 0023-5954 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : Gibbs random field * efficient estimator * empirical estimator Subject RIV: BA - General Mathematics Impact factor: 0.541, year: 2014 http://library.utia.cas.cz/separaty/2015/SI/janzura-0441325.pdf

  13. Simulating intrafraction prostate motion with a random walk model.

    Science.gov (United States)

    Pommer, Tobias; Oh, Jung Hun; Munck Af Rosenschöld, Per; Deasy, Joseph O

    2017-01-01

    Prostate motion during radiation therapy (ie, intrafraction motion) can cause unwanted loss of radiation dose to the prostate and increased dose to the surrounding organs at risk. A compact but general statistical description of this motion could be useful for simulation of radiation therapy delivery or margin calculations. We investigated whether prostate motion could be modeled with a random walk model. Prostate motion recorded during 548 radiation therapy fractions in 17 patients was analyzed and used for input in a random walk prostate motion model. The recorded motion was categorized on the basis of whether any transient excursions (ie, rapid prostate motion in the anterior and superior direction followed by a return) occurred in the trace and transient motion. This was separately modeled as a large step in the anterior/superior direction followed by a returning large step. Random walk simulations were conducted with and without added artificial transient motion using either motion data from all observed traces or only traces without transient excursions as model input, respectively. A general estimate of motion was derived with reasonable agreement between simulated and observed traces, especially during the first 5 minutes of the excursion-free simulations. Simulated and observed diffusion coefficients agreed within 0.03, 0.2 and 0.3 mm 2 /min in the left/right, superior/inferior, and anterior/posterior directions, respectively. A rapid increase in variance at the start of observed traces was difficult to reproduce and seemed to represent the patient's need to adjust before treatment. This could be estimated somewhat using artificial transient motion. Random walk modeling is feasible and recreated the characteristics of the observed prostate motion. Introducing artificial transient motion did not improve the overall agreement, although the first 30 seconds of the traces were better reproduced. The model provides a simple estimate of prostate motion during

  14. CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS.

    Science.gov (United States)

    Shalizi, Cosma Rohilla; Rinaldo, Alessandro

    2013-04-01

    The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are models for the entire network, while the data consists only of a sampled sub-network. Parameters for the whole network, which is what is of interest, are estimated by applying the model to the sub-network. This assumes that the model is consistent under sampling , or, in terms of the theory of stochastic processes, that it defines a projective family. Focusing on the popular class of exponential random graph models (ERGMs), we show that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM's expressive power. These results are actually special cases of more general results about exponential families of dependent random variables, which we also prove. Using such results, we offer easily checked conditions for the consistency of maximum likelihood estimation in ERGMs, and discuss some possible constructive responses.

  15. A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution

    Directory of Open Access Journals (Sweden)

    Xavier A. Harrison

    2015-07-01

    Full Text Available Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling overdispersion when it is present is therefore critical for robust biological inference. One means to account for overdispersion is to add an observation-level random effect (OLRE to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (<5 levels, I investigate the performance of both model types under a range of random effect sample sizes when overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the overdispersion; OLRE failed to cope with overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non

  16. Evolution of the concentration PDF in random environments modeled by global random walk

    Science.gov (United States)

    Suciu, Nicolae; Vamos, Calin; Attinger, Sabine; Knabner, Peter

    2013-04-01

    The evolution of the probability density function (PDF) of concentrations of chemical species transported in random environments is often modeled by ensembles of notional particles. The particles move in physical space along stochastic-Lagrangian trajectories governed by Ito equations, with drift coefficients given by the local values of the resolved velocity field and diffusion coefficients obtained by stochastic or space-filtering upscaling procedures. A general model for the sub-grid mixing also can be formulated as a system of Ito equations solving for trajectories in the composition space. The PDF is finally estimated by the number of particles in space-concentration control volumes. In spite of their efficiency, Lagrangian approaches suffer from two severe limitations. Since the particle trajectories are constructed sequentially, the demanded computing resources increase linearly with the number of particles. Moreover, the need to gather particles at the center of computational cells to perform the mixing step and to estimate statistical parameters, as well as the interpolation of various terms to particle positions, inevitably produce numerical diffusion in either particle-mesh or grid-free particle methods. To overcome these limitations, we introduce a global random walk method to solve the system of Ito equations in physical and composition spaces, which models the evolution of the random concentration's PDF. The algorithm consists of a superposition on a regular lattice of many weak Euler schemes for the set of Ito equations. Since all particles starting from a site of the space-concentration lattice are spread in a single numerical procedure, one obtains PDF estimates at the lattice sites at computational costs comparable with those for solving the system of Ito equations associated to a single particle. The new method avoids the limitations concerning the number of particles in Lagrangian approaches, completely removes the numerical diffusion, and

  17. Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution

    Directory of Open Access Journals (Sweden)

    Hare Krishna

    2017-01-01

    Full Text Available In this article, we study the geometric distribution under randomly censored data. Maximum likelihood estimators and confidence intervals based on Fisher information matrix are derived for the unknown parameters with randomly censored data. Bayes estimators are also developed using beta priors under generalized entropy and LINEX loss functions. Also, Bayesian credible and highest posterior density (HPD credible intervals are obtained for the parameters. Expected time on test and reliability characteristics are also analyzed in this article. To compare various estimates developed in the article, a Monte Carlo simulation study is carried out. Finally, for illustration purpose, a randomly censored real data set is discussed.

  18. Investigating Facebook Groups through a Random Graph Model

    OpenAIRE

    Dinithi Pallegedara; Lei Pan

    2014-01-01

    Facebook disseminates messages for billions of users everyday. Though there are log files stored on central servers, law enforcement agencies outside of the U.S. cannot easily acquire server log files from Facebook. This work models Facebook user groups by using a random graph model. Our aim is to facilitate detectives quickly estimating the size of a Facebook group with which a suspect is involved. We estimate this group size according to the number of immediate friends and the number of ext...

  19. Application of random regression models to the genetic evaluation ...

    African Journals Online (AJOL)

    The model included fixed regression on AM (range from 30 to 138 mo) and the effect of herd-measurement date concatenation. Random parts of the model were RRM coefficients for additive and permanent environmental effects, while residual effects were modelled to account for heterogeneity of variance by AY. Estimates ...

  20. Relative efficiency of unequal versus equal cluster sizes in cluster randomized trials using generalized estimating equation models.

    Science.gov (United States)

    Liu, Jingxia; Colditz, Graham A

    2018-05-01

    There is growing interest in conducting cluster randomized trials (CRTs). For simplicity in sample size calculation, the cluster sizes are assumed to be identical across all clusters. However, equal cluster sizes are not guaranteed in practice. Therefore, the relative efficiency (RE) of unequal versus equal cluster sizes has been investigated when testing the treatment effect. One of the most important approaches to analyze a set of correlated data is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which the "working correlation structure" is introduced and the association pattern depends on a vector of association parameters denoted by ρ. In this paper, we utilize GEE models to test the treatment effect in a two-group comparison for continuous, binary, or count data in CRTs. The variances of the estimator of the treatment effect are derived for the different types of outcome. RE is defined as the ratio of variance of the estimator of the treatment effect for equal to unequal cluster sizes. We discuss a commonly used structure in CRTs-exchangeable, and derive the simpler formula of RE with continuous, binary, and count outcomes. Finally, REs are investigated for several scenarios of cluster size distributions through simulation studies. We propose an adjusted sample size due to efficiency loss. Additionally, we also propose an optimal sample size estimation based on the GEE models under a fixed budget for known and unknown association parameter (ρ) in the working correlation structure within the cluster. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Random regression models for detection of gene by environment interaction

    Directory of Open Access Journals (Sweden)

    Meuwissen Theo HE

    2007-02-01

    Full Text Available Abstract Two random regression models, where the effect of a putative QTL was regressed on an environmental gradient, are described. The first model estimates the correlation between intercept and slope of the random regression, while the other model restricts this correlation to 1 or -1, which is expected under a bi-allelic QTL model. The random regression models were compared to a model assuming no gene by environment interactions. The comparison was done with regards to the models ability to detect QTL, to position them accurately and to detect possible QTL by environment interactions. A simulation study based on a granddaughter design was conducted, and QTL were assumed, either by assigning an effect independent of the environment or as a linear function of a simulated environmental gradient. It was concluded that the random regression models were suitable for detection of QTL effects, in the presence and absence of interactions with environmental gradients. Fixing the correlation between intercept and slope of the random regression had a positive effect on power when the QTL effects re-ranked between environments.

  2. Accumulator and random-walk models of psychophysical discrimination: a counter-evaluation.

    Science.gov (United States)

    Vickers, D; Smith, P

    1985-01-01

    In a recent assessment of models of psychophysical discrimination, Heath criticises the accumulator model for its reliance on computer simulation and qualitative evidence, and contrasts it unfavourably with a modified random-walk model, which yields exact predictions, is susceptible to critical test, and is provided with simple parameter-estimation techniques. A counter-evaluation is presented, in which the approximations employed in the modified random-walk analysis are demonstrated to be seriously inaccurate, the resulting parameter estimates to be artefactually determined, and the proposed test not critical. It is pointed out that Heath's specific application of the model is not legitimate, his data treatment inappropriate, and his hypothesis concerning confidence inconsistent with experimental results. Evidence from adaptive performance changes is presented which shows that the necessary assumptions for quantitative analysis in terms of the modified random-walk model are not satisfied, and that the model can be reconciled with data at the qualitative level only by making it virtually indistinguishable from an accumulator process. A procedure for deriving exact predictions for an accumulator process is outlined.

  3. Simulating intrafraction prostate motion with a random walk model

    Directory of Open Access Journals (Sweden)

    Tobias Pommer, PhD

    2017-07-01

    Conclusions: Random walk modeling is feasible and recreated the characteristics of the observed prostate motion. Introducing artificial transient motion did not improve the overall agreement, although the first 30 seconds of the traces were better reproduced. The model provides a simple estimate of prostate motion during delivery of radiation therapy.

  4. Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

    Science.gov (United States)

    Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

    2003-11-01

    Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

  5. Functional Mixed Effects Model for Small Area Estimation.

    Science.gov (United States)

    Maiti, Tapabrata; Sinha, Samiran; Zhong, Ping-Shou

    2016-09-01

    Functional data analysis has become an important area of research due to its ability of handling high dimensional and complex data structures. However, the development is limited in the context of linear mixed effect models, and in particular, for small area estimation. The linear mixed effect models are the backbone of small area estimation. In this article, we consider area level data, and fit a varying coefficient linear mixed effect model where the varying coefficients are semi-parametrically modeled via B-splines. We propose a method of estimating the fixed effect parameters and consider prediction of random effects that can be implemented using a standard software. For measuring prediction uncertainties, we derive an analytical expression for the mean squared errors, and propose a method of estimating the mean squared errors. The procedure is illustrated via a real data example, and operating characteristics of the method are judged using finite sample simulation studies.

  6. Random balance designs for the estimation of first order global sensitivity indices

    International Nuclear Information System (INIS)

    Tarantola, S.; Gatelli, D.; Mara, T.A.

    2006-01-01

    We present two methods for the estimation of main effects in global sensitivity analysis. The methods adopt Satterthwaite's application of random balance designs in regression problems, and extend it to sensitivity analysis of model output for non-linear, non-additive models. Finite as well as infinite ranges for model input factors are allowed. The methods are easier to implement than any other method available for global sensitivity analysis, and reduce significantly the computational cost of the analysis. We test their performance on different test cases, including an international benchmark on safety assessment for nuclear waste disposal originally carried out by OECD/NEA

  7. Genetic evaluation of European quails by random regression models

    Directory of Open Access Journals (Sweden)

    Flaviana Miranda Gonçalves

    2012-09-01

    Full Text Available The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (covariance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6 to determine which model had the best fit to describe the (covariance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT, the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day to 0.16 (42 days. Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.

  8. Estimation of the Coefficient of Restitution of Rocking Systems by the Random Decrement Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Demosthenous, Milton; Manos, George C.

    1994-01-01

    The aim of this paper is to investigate the possibility of estimating an average damping parameter for a rocking system due to impact, the so-called coefficient of restitution, from the random response, i.e. when the loads are random and unknown, and the response is measured. The objective...... is to obtain an estimate of the free rocking response from the measured random response using the Random Decrement (RDD) Technique, and then estimate the coefficient of restitution from this free response estimate. In the paper this approach is investigated by simulating the response of a single degree...

  9. Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

    Science.gov (United States)

    Veerkamp, R F; Koenen, E P; De Jong, G

    2001-10-01

    Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.

  10. Estimating the Multilevel Rasch Model: With the lme4 Package

    Directory of Open Access Journals (Sweden)

    Harold Doran

    2007-02-01

    Full Text Available Traditional Rasch estimation of the item and student parameters via marginal maximum likelihood, joint maximum likelihood or conditional maximum likelihood, assume individuals in clustered settings are uncorrelated and items within a test that share a grouping structure are also uncorrelated. These assumptions are often violated, particularly in educational testing situations, in which students are grouped into classrooms and many test items share a common grouping structure, such as a content strand or a reading passage. Consequently, one possible approach is to explicitly recognize the clustered nature of the data and directly incorporate random effects to account for the various dependencies. This article demonstrates how the multilevel Rasch model can be estimated using the functions in R for mixed-effects models with crossed or partially crossed random effects. We demonstrate how to model the following hierarchical data structures: a individuals clustered in similar settings (e.g., classrooms, schools, b items nested within a particular group (such as a content strand or a reading passage, and c how to estimate a teacher × content strand interaction.

  11. A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus

    2006-01-01

    The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...... distribution of the simulated random effects coincides with the assumed random effects distribution. In practice, the specified model depends on some unknown parameter which is replaced by an estimate. We obtain a correction for this by deriving the asymptotic distribution of the empirical distribution...

  12. A simulation-based goodness-of-fit test for random effects in generalized linear mixed models

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge

    The goodness-of-fit of the distribution of random effects in a generalized linear mixed model is assessed using a conditional simulation of the random effects conditional on the observations. Provided that the specified joint model for random effects and observations is correct, the marginal...... distribution of the simulated random effects coincides with the assumed random effects distribution. In practice the specified model depends on some unknown parameter which is replaced by an estimate. We obtain a correction for this by deriving the asymptotic distribution of the empirical distribution function...

  13. Random regression models for daily feed intake in Danish Duroc pigs

    DEFF Research Database (Denmark)

    Strathe, Anders Bjerring; Mark, Thomas; Jensen, Just

    The objective of this study was to develop random regression models and estimate covariance functions for daily feed intake (DFI) in Danish Duroc pigs. A total of 476201 DFI records were available on 6542 Duroc boars between 70 to 160 days of age. The data originated from the National test station......-year-season, permanent, and animal genetic effects. The functional form was based on Legendre polynomials. A total of 64 models for random regressions were initially ranked by BIC to identify the approximate order for the Legendre polynomials using AI-REML. The parsimonious model included Legendre polynomials of 2nd...... order for genetic and permanent environmental curves and a heterogeneous residual variance, allowing the daily residual variance to change along the age trajectory due to scale effects. The parameters of the model were estimated in a Bayesian framework, using the RJMC module of the DMU package, where...

  14. Remote optimal state estimation over communication channels with random delays

    KAUST Repository

    Mahmoud, Magdi S.; Al-Sunni, Fouad; Liu, Bo

    2014-01-01

    This paper considers the optimal estimation of linear systems over unreliable communication channels with random delays. In this work, it is assumed that the system to be estimated is far away from the filter. The observations of the system

  15. Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

    Science.gov (United States)

    Huang, Lei

    2015-01-01

    To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying estimators of observation noise are used to achieve the estimated mean and variance of the observation noise. Using the robust Kalman filtering, the ARMA model parameters are estimated accurately. The developed ARMA modeling method has the advantages of a rapid convergence and high accuracy. Thus, the required sample size is reduced. It can be applied to modeling applications for gyro random noise in which a fast and accurate ARMA modeling method is required. PMID:26437409

  16. A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix

    DEFF Research Database (Denmark)

    Cherchi, Elisabetta; Guevara, Cristian Angelo

    2012-01-01

    of parameters increases is usually known as the “curse of dimensionality” in the simulation methods. We investigate this problem in the case of the random coefficients Logit model. We compare the traditional Maximum Simulated Likelihood (MSL) method with two alternative estimation methods: the Expectation......–Maximization (EM) and the Laplace Approximation (HH) methods that do not require simulation. We use Monte Carlo experimentation to investigate systematically the performance of the methods under different circumstances, including different numbers of variables, sample sizes and structures of the variance...

  17. Random balance designs for the estimation of first order global sensitivity indices

    Energy Technology Data Exchange (ETDEWEB)

    Tarantola, S. [Joint Research Centre, European Commission, Institute of the Protection and Security of the Citizen, TP 361, Via E. Fermi 1, 21020 Ispra (Vatican City State, Holy See,) (Italy)]. E-mail: stefano.tarantola@jrc.it; Gatelli, D. [Joint Research Centre, European Commission, Institute of the Protection and Security of the Citizen, TP 361, Via E. Fermi 1, 21020 Ispra (VA) (Italy); Mara, T.A. [Laboratory of Industrial engineering, University of Reunion Island, BP 7151, 15 avenue Rene Cassin, 97 715 Saint-Denis (France)

    2006-06-15

    We present two methods for the estimation of main effects in global sensitivity analysis. The methods adopt Satterthwaite's application of random balance designs in regression problems, and extend it to sensitivity analysis of model output for non-linear, non-additive models. Finite as well as infinite ranges for model input factors are allowed. The methods are easier to implement than any other method available for global sensitivity analysis, and reduce significantly the computational cost of the analysis. We test their performance on different test cases, including an international benchmark on safety assessment for nuclear waste disposal originally carried out by OECD/NEA.

  18. Bias correction for the estimation of sensitivity indices based on random balance designs

    International Nuclear Information System (INIS)

    Tissot, Jean-Yves; Prieur, Clémentine

    2012-01-01

    This paper deals with the random balance design method (RBD) and its hybrid approach, RBD-FAST. Both these global sensitivity analysis methods originate from Fourier amplitude sensitivity test (FAST) and consequently face the main problems inherent to discrete harmonic analysis. We present here a general way to correct a bias which occurs when estimating sensitivity indices (SIs) of any order – except total SI of single factor or group of factors – by the random balance design method (RBD) and its hybrid version, RBD-FAST. In the RBD case, this positive bias has been recently identified in a paper by Xu and Gertner [1]. Following their work, we propose a bias correction method for first-order SIs estimates in RBD. We then extend the correction method to the SIs of any order in RBD-FAST. At last, we suggest an efficient strategy to estimate all the first- and second-order SIs using RBD-FAST. - Highlights: ► We provide a bias correction method for the global sensitivity analysis methods: RBD and RBD-FAST. ► In RBD, first-order sensitivity estimates are corrected. ► In RBD-FAST, sensitivity indices of any order and closed sensitivity indices are corrected. ► We propose an efficient strategy to estimate all the first- and second-order indices of a model.

  19. Efficient and robust estimation for longitudinal mixed models for binary data

    DEFF Research Database (Denmark)

    Holst, René

    2009-01-01

    This paper proposes a longitudinal mixed model for binary data. The model extends the classical Poisson trick, in which a binomial regression is fitted by switching to a Poisson framework. A recent estimating equations method for generalized linear longitudinal mixed models, called GEEP, is used...... as a vehicle for fitting the conditional Poisson regressions, given a latent process of serial correlated Tweedie variables. The regression parameters are estimated using a quasi-score method, whereas the dispersion and correlation parameters are estimated by use of bias-corrected Pearson-type estimating...... equations, using second moments only. Random effects are predicted by BLUPs. The method provides a computationally efficient and robust approach to the estimation of longitudinal clustered binary data and accommodates linear and non-linear models. A simulation study is used for validation and finally...

  20. Conditional estimation of exponential random graph models from snowball sampling designs

    NARCIS (Netherlands)

    Pattison, Philippa E.; Robins, Garry L.; Snijders, Tom A. B.; Wang, Peng

    2013-01-01

    A complete survey of a network in a large population may be prohibitively difficult and costly. So it is important to estimate models for networks using data from various network sampling designs, such as link-tracing designs. We focus here on snowball sampling designs, designs in which the members

  1. Errors and parameter estimation in precipitation-runoff modeling: 1. Theory

    Science.gov (United States)

    Troutman, Brent M.

    1985-01-01

    Errors in complex conceptual precipitation-runoff models may be analyzed by placing them into a statistical framework. This amounts to treating the errors as random variables and defining the probabilistic structure of the errors. By using such a framework, a large array of techniques, many of which have been presented in the statistical literature, becomes available to the modeler for quantifying and analyzing the various sources of error. A number of these techniques are reviewed in this paper, with special attention to the peculiarities of hydrologic models. Known methodologies for parameter estimation (calibration) are particularly applicable for obtaining physically meaningful estimates and for explaining how bias in runoff prediction caused by model error and input error may contribute to bias in parameter estimation.

  2. Criticality of the random-site Ising model: Metropolis, Swendsen-Wang and Wolff Monte Carlo algorithms

    Directory of Open Access Journals (Sweden)

    D.Ivaneyko

    2005-01-01

    Full Text Available We apply numerical simulations to study of the criticality of the 3D Ising model with random site quenched dilution. The emphasis is given to the issues not being discussed in detail before. In particular, we attempt a comparison of different Monte Carlo techniques, discussing regions of their applicability and advantages/disadvantages depending on the aim of a particular simulation set. Moreover, besides evaluation of the critical indices we estimate the universal ratio Γ+/Γ- for the magnetic susceptibility critical amplitudes. Our estimate Γ+/Γ- = 1.67 ± 0.15 is in a good agreement with the recent MC analysis of the random-bond Ising model giving further support that both random-site and random-bond dilutions lead to the same universality class.

  3. On adjustment for auxiliary covariates in additive hazard models for the analysis of randomized experiments

    DEFF Research Database (Denmark)

    Vansteelandt, S.; Martinussen, Torben; Tchetgen, E. J Tchetgen

    2014-01-01

    We consider additive hazard models (Aalen, 1989) for the effect of a randomized treatment on a survival outcome, adjusting for auxiliary baseline covariates. We demonstrate that the Aalen least-squares estimator of the treatment effect parameter is asymptotically unbiased, even when the hazard...... that, in view of its robustness against model misspecification, Aalen least-squares estimation is attractive for evaluating treatment effects on a survival outcome in randomized experiments, and the primary reasons to consider baseline covariate adjustment in such settings could be interest in subgroup......'s dependence on time or on the auxiliary covariates is misspecified, and even away from the null hypothesis of no treatment effect. We furthermore show that adjustment for auxiliary baseline covariates does not change the asymptotic variance of the estimator of the effect of a randomized treatment. We conclude...

  4. Contributions in Radio Channel Sounding, Modeling, and Estimation

    DEFF Research Database (Denmark)

    Pedersen, Troels

    2009-01-01

    This thesis spans over three strongly related topics in wireless communication: channel-sounding, -modeling, and -estimation. Three main problems are addressed: optimization of spatio-temporal apertures for channel sounding; estimation of per-path power spectral densities (psds); and modeling...... relies on a ``propagation graph'' where vertices  represent scatterers and edges represent the wave propagation conditions between scatterers.  The graph has a recursive structure, which permits modeling of the transfer function of the graph. We derive a closed-form expression of the infinite......-bounce impulse response. This expression is used for simulation of the impulse response of randomly generated propagation graphs. The obtained realizations exhibit the well-observed  exponential power decay versus delay and specular-to-diffuse transition....

  5. Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster-randomized trials with missing outcomes.

    Science.gov (United States)

    Prague, Melanie; Wang, Rui; Stephens, Alisa; Tchetgen Tchetgen, Eric; DeGruttola, Victor

    2016-12-01

    Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR); it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method. © 2016, The International Biometric Society.

  6. Device-independent randomness generation from several Bell estimators

    Science.gov (United States)

    Nieto-Silleras, Olmo; Bamps, Cédric; Silman, Jonathan; Pironio, Stefano

    2018-02-01

    Device-independent randomness generation and quantum key distribution protocols rely on a fundamental relation between the non-locality of quantum theory and its random character. This relation is usually expressed in terms of a trade-off between the probability of guessing correctly the outcomes of measurements performed on quantum systems and the amount of violation of a given Bell inequality. However, a more accurate assessment of the randomness produced in Bell experiments can be obtained if the value of several Bell expressions is simultaneously taken into account, or if the full set of probabilities characterizing the behavior of the device is considered. We introduce protocols for device-independent randomness generation secure against classical side information, that rely on the estimation of an arbitrary number of Bell expressions or even directly on the experimental frequencies of measurement outcomes. Asymptotically, this results in an optimal generation of randomness from experimental data (as measured by the min-entropy), without having to assume beforehand that the devices violate a specific Bell inequality.

  7. Entropy estimates for simple random fields

    DEFF Research Database (Denmark)

    Forchhammer, Søren; Justesen, Jørn

    1995-01-01

    We consider the problem of determining the maximum entropy of a discrete random field on a lattice subject to certain local constraints on symbol configurations. The results are expected to be of interest in the analysis of digitized images and two dimensional codes. We shall present some examples...... of binary and ternary fields with simple constraints. Exact results on the entropies are known only in a few cases, but we shall present close bounds and estimates that are computationally efficient...

  8. Smooth random change point models.

    Science.gov (United States)

    van den Hout, Ardo; Muniz-Terrera, Graciela; Matthews, Fiona E

    2011-03-15

    Change point models are used to describe processes over time that show a change in direction. An example of such a process is cognitive ability, where a decline a few years before death is sometimes observed. A broken-stick model consists of two linear parts and a breakpoint where the two lines intersect. Alternatively, models can be formulated that imply a smooth change between the two linear parts. Change point models can be extended by adding random effects to account for variability between subjects. A new smooth change point model is introduced and examples are presented that show how change point models can be estimated using functions in R for mixed-effects models. The Bayesian inference using WinBUGS is also discussed. The methods are illustrated using data from a population-based longitudinal study of ageing, the Cambridge City over 75 Cohort Study. The aim is to identify how many years before death individuals experience a change in the rate of decline of their cognitive ability. Copyright © 2010 John Wiley & Sons, Ltd.

  9. Leaf area index uncertainty estimates for model-data fusion applications

    Science.gov (United States)

    Andrew D. Richardson; D. Bryan Dail; D.Y. Hollinger

    2011-01-01

    Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux...

  10. Damping Estimation of Friction Systems in Random Vibrations

    DEFF Research Database (Denmark)

    Friis, Tobias; Katsanos, Evangelos; Amador, Sandro

    Friction is one of the most efficient and economical mechanisms to reduce vibrations in structural mechanics. However, the estimation of the equivalent linear damping of the friction damped systems in experimental modal analysis and operational modal analysis can be adversely affected by several...... assumptions regarding the definition of the linear damping and the identification methods or may be lacking a meaningful interpretation of the damping. Along these lines, this project focuses on assessing the potential to estimate efficiently the equivalent linear damping of friction systems in random...

  11. Generalized Dynamic Panel Data Models with Random Effects for Cross-Section and Time

    NARCIS (Netherlands)

    Mesters, G.; Koopman, S.J.

    2014-01-01

    An exact maximum likelihood method is developed for the estimation of parameters in a nonlinear non-Gaussian dynamic panel data model with unobserved random individual-specific and time-varying effects. We propose an estimation procedure based on the importance sampling technique. In particular, a

  12. A Fay-Herriot Model with Different Random Effect Variances

    Czech Academy of Sciences Publication Activity Database

    Hobza, Tomáš; Morales, D.; Herrador, M.; Esteban, M.D.

    2011-01-01

    Roč. 40, č. 5 (2011), s. 785-797 ISSN 0361-0926 R&D Projects: GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : small area estimation * Fay-Herriot model * Linear mixed model * Labor Force Survey Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.274, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/hobza-a%20fay-herriot%20model%20with%20different%20random%20effect%20variances.pdf

  13. Statistical parameters of random heterogeneity estimated by analysing coda waves based on finite difference method

    Science.gov (United States)

    Emoto, K.; Saito, T.; Shiomi, K.

    2017-12-01

    Short-period (2 s) seismograms. We found that the energy of the coda of long-period seismograms shows a spatially flat distribution. This phenomenon is well known in short-period seismograms and results from the scattering by small-scale heterogeneities. We estimate the statistical parameters that characterize the small-scale random heterogeneity by modelling the spatiotemporal energy distribution of long-period seismograms. We analyse three moderate-size earthquakes that occurred in southwest Japan. We calculate the spatial distribution of the energy density recorded by a dense seismograph network in Japan at the period bands of 8-16 s, 4-8 s and 2-4 s and model them by using 3-D finite difference (FD) simulations. Compared to conventional methods based on statistical theories, we can calculate more realistic synthetics by using the FD simulation. It is not necessary to assume a uniform background velocity, body or surface waves and scattering properties considered in general scattering theories. By taking the ratio of the energy of the coda area to that of the entire area, we can separately estimate the scattering and the intrinsic absorption effects. Our result reveals the spectrum of the random inhomogeneity in a wide wavenumber range including the intensity around the corner wavenumber as P(m) = 8πε2a3/(1 + a2m2)2, where ε = 0.05 and a = 3.1 km, even though past studies analysing higher-frequency records could not detect the corner. Finally, we estimate the intrinsic attenuation by modelling the decay rate of the energy. The method proposed in this study is suitable for quantifying the statistical properties of long-wavelength subsurface random inhomogeneity, which leads the way to characterizing a wider wavenumber range of spectra, including the corner wavenumber.

  14. A special covariance structure for random coefficient models with both between and within covariates

    International Nuclear Information System (INIS)

    Riedel, K.S.

    1990-07-01

    We review random coefficient (RC) models in linear regression and propose a bias correction to the maximum likelihood (ML) estimator. Asymmptotic expansion of the ML equations are given when the between individual variance is much larger or smaller than the variance from within individual fluctuations. The standard model assumes all but one covariate varies within each individual, (we denote the within covariates by vector χ 1 ). We consider random coefficient models where some of the covariates do not vary in any single individual (we denote the between covariates by vector χ 0 ). The regression coefficients, vector β k , can only be estimated in the subspace X k of X. Thus the number of individuals necessary to estimate vector β and the covariance matrix Δ of vector β increases significantly in the presence of more than one between covariate. When the number of individuals is sufficient to estimate vector β but not the entire matrix Δ , additional assumptions must be imposed on the structure of Δ. A simple reduced model is that the between component of vector β is fixed and only the within component varies randomly. This model fails because it is not invariant under linear coordinate transformations and it can significantly overestimate the variance of new observations. We propose a covariance structure for Δ without these difficulties by first projecting the within covariates onto the space perpendicular to be between covariates. (orig.)

  15. A review of instrumental variable estimators for Mendelian randomization.

    Science.gov (United States)

    Burgess, Stephen; Small, Dylan S; Thompson, Simon G

    2017-10-01

    Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.

  16. Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors

    Science.gov (United States)

    Chung, Yeojin; Rabe-Hesketh, Sophia; Gelman, Andrew; Dorie, Vincent; Liu, Jinchen

    2012-01-01

    Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for variation in treatment effects, as well as…

  17. A single-level random-effects cross-lagged panel model for longitudinal mediation analysis.

    Science.gov (United States)

    Wu, Wei; Carroll, Ian A; Chen, Po-Yi

    2017-12-06

    Cross-lagged panel models (CLPMs) are widely used to test mediation with longitudinal panel data. One major limitation of the CLPMs is that the model effects are assumed to be fixed across individuals. This assumption is likely to be violated (i.e., the model effects are random across individuals) in practice. When this happens, the CLPMs can potentially yield biased parameter estimates and misleading statistical inferences. This article proposes a model named a random-effects cross-lagged panel model (RE-CLPM) to account for random effects in CLPMs. Simulation studies show that the RE-CLPM outperforms the CLPM in recovering the mean indirect and direct effects in a longitudinal mediation analysis when random effects exist in the population. The performance of the RE-CLPM is robust to a certain degree, even when the random effects are not normally distributed. In addition, the RE-CLPM does not produce harmful results when the model effects are in fact fixed in the population. Implications of the simulation studies and potential directions for future research are discussed.

  18. Random Intercept and Random Slope 2-Level Multilevel Models

    Directory of Open Access Journals (Sweden)

    Rehan Ahmad Khan

    2012-11-01

    Full Text Available Random intercept model and random intercept & random slope model carrying two-levels of hierarchy in the population are presented and compared with the traditional regression approach. The impact of students’ satisfaction on their grade point average (GPA was explored with and without controlling teachers influence. The variation at level-1 can be controlled by introducing the higher levels of hierarchy in the model. The fanny movement of the fitted lines proves variation of student grades around teachers.

  19. A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution.

    Science.gov (United States)

    Lee, Duncan; Rushworth, Alastair; Sahu, Sujit K

    2014-06-01

    Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models. © 2014, The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.

  20. Development and estimation of a semi-compensatory model with flexible error structure

    DEFF Research Database (Denmark)

    Kaplan, Sigal; Shiftan, Yoram; Bekhor, Shlomo

    2009-01-01

    , and utility-based choice accommodating alternatively nested substitution patterns across the alternatives and random taste variation across the population. The proposed model is applied to off-campus rental apartment choice of students. Results show (i) the estimated model for a universal realm of 200...

  1. Stochastic Optimal Estimation with Fuzzy Random Variables and Fuzzy Kalman Filtering

    Institute of Scientific and Technical Information of China (English)

    FENG Yu-hu

    2005-01-01

    By constructing a mean-square performance index in the case of fuzzy random variable, the optimal estimation theorem for unknown fuzzy state using the fuzzy observation data are given. The state and output of linear discrete-time dynamic fuzzy system with Gaussian noise are Gaussian fuzzy random variable sequences. An approach to fuzzy Kalman filtering is discussed. Fuzzy Kalman filtering contains two parts: a real-valued non-random recurrence equation and the standard Kalman filtering.

  2. Using observation-level random effects to model overdispersion in count data in ecology and evolution

    Directory of Open Access Journals (Sweden)

    Xavier A. Harrison

    2014-10-01

    Full Text Available Overdispersion is common in models of count data in ecology and evolutionary biology, and can occur due to missing covariates, non-independent (aggregated data, or an excess frequency of zeroes (zero-inflation. Accounting for overdispersion in such models is vital, as failing to do so can lead to biased parameter estimates, and false conclusions regarding hypotheses of interest. Observation-level random effects (OLRE, where each data point receives a unique level of a random effect that models the extra-Poisson variation present in the data, are commonly employed to cope with overdispersion in count data. However studies investigating the efficacy of observation-level random effects as a means to deal with overdispersion are scarce. Here I use simulations to show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdispersion is simply ignored. Conversely, OLRE fail to reduce bias in zero-inflated data, and in some cases increase bias at high levels of overdispersion. There was a positive relationship between the magnitude of overdispersion and the degree of bias in parameter estimates. Critically, the simulations reveal that failing to account for overdispersion in mixed models can erroneously inflate measures of explained variance (r2, which may lead to researchers overestimating the predictive power of variables of interest. This work suggests use of observation-level random effects provides a simple and robust means to account for overdispersion in count data, but also that their ability to minimise bias is not uniform across all types of overdispersion and must be applied judiciously.

  3. A note on the conditional density estimate in single functional index model

    OpenAIRE

    2010-01-01

    Abstract In this paper, we consider estimation of the conditional density of a scalar response variable Y given a Hilbertian random variable X when the observations are linked with a single-index structure. We establish the pointwise and the uniform almost complete convergence (with the rate) of the kernel estimate of this model. As an application, we show how our result can be applied in the prediction problem via the conditional mode estimate. Finally, the estimation of the funct...

  4. Random-growth urban model with geographical fitness

    Science.gov (United States)

    Kii, Masanobu; Akimoto, Keigo; Doi, Kenji

    2012-12-01

    This paper formulates a random-growth urban model with a notion of geographical fitness. Using techniques of complex-network theory, we study our system as a type of preferential-attachment model with fitness, and we analyze its macro behavior to clarify the properties of the city-size distributions it predicts. First, restricting the geographical fitness to take positive values and using a continuum approach, we show that the city-size distributions predicted by our model asymptotically approach Pareto distributions with coefficients greater than unity. Then, allowing the geographical fitness to take negative values, we perform local coefficient analysis to show that the predicted city-size distributions can deviate from Pareto distributions, as is often observed in actual city-size distributions. As a result, the model we propose can generate a generic class of city-size distributions, including but not limited to Pareto distributions. For applications to city-population projections, our simple model requires randomness only when new cities are created, not during their subsequent growth. This property leads to smooth trajectories of city population growth, in contrast to other models using Gibrat’s law. In addition, a discrete form of our dynamical equations can be used to estimate past city populations based on present-day data; this fact allows quantitative assessment of the performance of our model. Further study is needed to determine appropriate formulas for the geographical fitness.

  5. Conditional random slope: A new approach for estimating individual child growth velocity in epidemiological research.

    Science.gov (United States)

    Leung, Michael; Bassani, Diego G; Racine-Poon, Amy; Goldenberg, Anna; Ali, Syed Asad; Kang, Gagandeep; Premkumar, Prasanna S; Roth, Daniel E

    2017-09-10

    Conditioning child growth measures on baseline accounts for regression to the mean (RTM). Here, we present the "conditional random slope" (CRS) model, based on a linear-mixed effects model that incorporates a baseline-time interaction term that can accommodate multiple data points for a child while also directly accounting for RTM. In two birth cohorts, we applied five approaches to estimate child growth velocities from 0 to 12 months to assess the effect of increasing data density (number of measures per child) on the magnitude of RTM of unconditional estimates, and the correlation and concordance between the CRS and four alternative metrics. Further, we demonstrated the differential effect of the choice of velocity metric on the magnitude of the association between infant growth and stunting at 2 years. RTM was minimally attenuated by increasing data density for unconditional growth modeling approaches. CRS and classical conditional models gave nearly identical estimates with two measures per child. Compared to the CRS estimates, unconditional metrics had moderate correlation (r = 0.65-0.91), but poor agreement in the classification of infants with relatively slow growth (kappa = 0.38-0.78). Estimates of the velocity-stunting association were the same for CRS and classical conditional models but differed substantially between conditional versus unconditional metrics. The CRS can leverage the flexibility of linear mixed models while addressing RTM in longitudinal analyses. © 2017 The Authors American Journal of Human Biology Published by Wiley Periodicals, Inc.

  6. Oracle Efficient Variable Selection in Random and Fixed Effects Panel Data Models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl

    This paper generalizes the results for the Bridge estimator of Huang et al. (2008) to linear random and fixed effects panel data models which are allowed to grow in both dimensions. In particular we show that the Bridge estimator is oracle efficient. It can correctly distinguish between relevant...... and irrelevant variables and the asymptotic distribution of the estimators of the coefficients of the relevant variables is the same as if only these had been included in the model, i.e. as if an oracle had revealed the true model prior to estimation. In the case of more explanatory variables than observations......, we prove that the Marginal Bridge estimator can asymptotically correctly distinguish between relevant and irrelevant explanatory variables. We do this without restricting the dependence between covariates and without assuming sub Gaussianity of the error terms thereby generalizing the results...

  7. Random Forests (RFs) for Estimation, Uncertainty Prediction and Interpretation of Monthly Solar Potential

    Science.gov (United States)

    Assouline, Dan; Mohajeri, Nahid; Scartezzini, Jean-Louis

    2017-04-01

    Solar energy is clean, widely available, and arguably the most promising renewable energy resource. Taking full advantage of solar power, however, requires a deep understanding of its patterns and dependencies in space and time. The recent advances in Machine Learning brought powerful algorithms to estimate the spatio-temporal variations of solar irradiance (the power per unit area received from the Sun, W/m2), using local weather and terrain information. Such algorithms include Deep Learning (e.g. Artificial Neural Networks), or kernel methods (e.g. Support Vector Machines). However, most of these methods have some disadvantages, as they: (i) are complex to tune, (ii) are mainly used as a black box and offering no interpretation on the variables contributions, (iii) often do not provide uncertainty predictions (Assouline et al., 2016). To provide a reasonable solar mapping with good accuracy, these gaps would ideally need to be filled. We present here simple steps using one ensemble learning algorithm namely, Random Forests (Breiman, 2001) to (i) estimate monthly solar potential with good accuracy, (ii) provide information on the contribution of each feature in the estimation, and (iii) offer prediction intervals for each point estimate. We have selected Switzerland as an example. Using a Digital Elevation Model (DEM) along with monthly solar irradiance time series and weather data, we build monthly solar maps for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (GHI), and Extraterrestrial Irradiance (EI). The weather data include monthly values for temperature, precipitation, sunshine duration, and cloud cover. In order to explain the impact of each feature on the solar irradiance of each point estimate, we extend the contribution method (Kuz'min et al., 2011) to a regression setting. Contribution maps for all features can then be computed for each solar map. This provides precious information on the spatial variation of the features impact all

  8. A General Model for Estimating Macroevolutionary Landscapes.

    Science.gov (United States)

    Boucher, Florian C; Démery, Vincent; Conti, Elena; Harmon, Luke J; Uyeda, Josef

    2018-03-01

    The evolution of quantitative characters over long timescales is often studied using stochastic diffusion models. The current toolbox available to students of macroevolution is however limited to two main models: Brownian motion and the Ornstein-Uhlenbeck process, plus some of their extensions. Here, we present a very general model for inferring the dynamics of quantitative characters evolving under both random diffusion and deterministic forces of any possible shape and strength, which can accommodate interesting evolutionary scenarios like directional trends, disruptive selection, or macroevolutionary landscapes with multiple peaks. This model is based on a general partial differential equation widely used in statistical mechanics: the Fokker-Planck equation, also known in population genetics as the Kolmogorov forward equation. We thus call the model FPK, for Fokker-Planck-Kolmogorov. We first explain how this model can be used to describe macroevolutionary landscapes over which quantitative traits evolve and, more importantly, we detail how it can be fitted to empirical data. Using simulations, we show that the model has good behavior both in terms of discrimination from alternative models and in terms of parameter inference. We provide R code to fit the model to empirical data using either maximum-likelihood or Bayesian estimation, and illustrate the use of this code with two empirical examples of body mass evolution in mammals. FPK should greatly expand the set of macroevolutionary scenarios that can be studied since it opens the way to estimating macroevolutionary landscapes of any conceivable shape. [Adaptation; bounds; diffusion; FPK model; macroevolution; maximum-likelihood estimation; MCMC methods; phylogenetic comparative data; selection.].

  9. Coupling Hydrologic and Hydrodynamic Models to Estimate PMF

    Science.gov (United States)

    Felder, G.; Weingartner, R.

    2015-12-01

    Most sophisticated probable maximum flood (PMF) estimations derive the PMF from the probable maximum precipitation (PMP) by applying deterministic hydrologic models calibrated with observed data. This method is based on the assumption that the hydrological system is stationary, meaning that the system behaviour during the calibration period or the calibration event is presumed to be the same as it is during the PMF. However, as soon as a catchment-specific threshold is reached, the system is no longer stationary. At or beyond this threshold, retention areas, new flow paths, and changing runoff processes can strongly affect downstream peak discharge. These effects can be accounted for by coupling hydrologic and hydrodynamic models, a technique that is particularly promising when the expected peak discharge may considerably exceed the observed maximum discharge. In such cases, the coupling of hydrologic and hydraulic models has the potential to significantly increase the physical plausibility of PMF estimations. This procedure ensures both that the estimated extreme peak discharge does not exceed the physical limit based on riverbed capacity and that the dampening effect of inundation processes on peak discharge is considered. Our study discusses the prospect of considering retention effects on PMF estimations by coupling hydrologic and hydrodynamic models. This method is tested by forcing PREVAH, a semi-distributed deterministic hydrological model, with randomly generated, physically plausible extreme precipitation patterns. The resulting hydrographs are then used to externally force the hydraulic model BASEMENT-ETH (riverbed in 1D, potential inundation areas in 2D). Finally, the PMF estimation results obtained using the coupled modelling approach are compared to the results obtained using ordinary hydrologic modelling.

  10. Deriving Genomic Breeding Values for Residual Feed Intake from Covariance Functions of Random Regression Models

    DEFF Research Database (Denmark)

    Strathe, Anders B; Mark, Thomas; Nielsen, Bjarne

    2014-01-01

    Random regression models were used to estimate covariance functions between cumulated feed intake (CFI) and body weight (BW) in 8424 Danish Duroc pigs. Random regressions on second order Legendre polynomials of age were used to describe genetic and permanent environmental curves in BW and CFI...

  11. Estimation of the Coefficient of Restitution of Rocking Systems by the Random Decrement Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Demosthenous, M.; Manos, G. C.

    The aim of this paper is to investigate the possibility of estimating an average damping parameter for a rocking system due to impact, the so-called coefficient of restitution, from the random response, i.e. when the loads are random and unknown, and the response is measured. The objective is to ...... of freedom system loaded by white noise, estimating the coefficient of restitution as explained, and comparing the estimates with the value used in the simulations. Several estimates for the coefficient of restitution are considered, and reasonable results are achieved....

  12. Optimal estimations of random fields using kriging

    International Nuclear Information System (INIS)

    Barua, G.

    2004-01-01

    Kriging is a statistical procedure of estimating the best weights of a linear estimator. Suppose there is a point or an area or a volume of ground over which we do not know a hydrological variable and wish to estimate it. In order to produce an estimator, we need some information to work on, usually available in the form of samples. There can, be an infinite number of linear unbiased estimators for which the weights sum up to one. The problem is how to determine the best weights for which the estimation variance is the least. The system of equations as shown above is generally known as the kriging system and the estimator produced is the kriging estimator. The variance of the kriging estimator can be found by substitution of the weights in the general estimation variance equation. We assume here a linear model for the semi-variogram. Applying the model to the equation, we obtain a set of kriging equations. By solving these equations, we obtain the kriging variance. Thus, for the one-dimensional problem considered, kriging definitely gives a better estimation variance than the extension variance

  13. Analysis, Simulation and Prediction of Multivariate Random Fields with Package RandomFields

    Directory of Open Access Journals (Sweden)

    Martin Schlather

    2015-02-01

    Full Text Available Modeling of and inference on multivariate data that have been measured in space, such as temperature and pressure, are challenging tasks in environmental sciences, physics and materials science. We give an overview over and some background on modeling with cross- covariance models. The R package RandomFields supports the simulation, the parameter estimation and the prediction in particular for the linear model of coregionalization, the multivariate Matrn models, the delay model, and a spectrum of physically motivated vector valued models. An example on weather data is considered, illustrating the use of RandomFields for parameter estimation and prediction.

  14. Estimating the State of Aerodynamic Flows in the Presence of Modeling Errors

    Science.gov (United States)

    da Silva, Andre F. C.; Colonius, Tim

    2017-11-01

    The ensemble Kalman filter (EnKF) has been proven to be successful in fields such as meteorology, in which high-dimensional nonlinear systems render classical estimation techniques impractical. When the model used to forecast state evolution misrepresents important aspects of the true dynamics, estimator performance may degrade. In this work, parametrization and state augmentation are used to track misspecified boundary conditions (e.g., free stream perturbations). The resolution error is modeled as a Gaussian-distributed random variable with the mean (bias) and variance to be determined. The dynamics of the flow past a NACA 0009 airfoil at high angles of attack and moderate Reynolds number is represented by a Navier-Stokes equations solver with immersed boundaries capabilities. The pressure distribution on the airfoil or the velocity field in the wake, both randomized by synthetic noise, are sampled as measurement data and incorporated into the estimated state and bias following Kalman's analysis scheme. Insights about how to specify the modeling error covariance matrix and its impact on the estimator performance are conveyed. This work has been supported in part by a Grant from AFOSR (FA9550-14-1-0328) with Dr. Douglas Smith as program manager, and by a Science without Borders scholarship from the Ministry of Education of Brazil (Capes Foundation - BEX 12966/13-4).

  15. A Comparison of Three Random Number Generators for Aircraft Dynamic Modeling Applications

    Science.gov (United States)

    Grauer, Jared A.

    2017-01-01

    Three random number generators, which produce Gaussian white noise sequences, were compared to assess their suitability in aircraft dynamic modeling applications. The first generator considered was the MATLAB (registered) implementation of the Mersenne-Twister algorithm. The second generator was a website called Random.org, which processes atmospheric noise measured using radios to create the random numbers. The third generator was based on synthesis of the Fourier series, where the random number sequences are constructed from prescribed amplitude and phase spectra. A total of 200 sequences, each having 601 random numbers, for each generator were collected and analyzed in terms of the mean, variance, normality, autocorrelation, and power spectral density. These sequences were then applied to two problems in aircraft dynamic modeling, namely estimating stability and control derivatives from simulated onboard sensor data, and simulating flight in atmospheric turbulence. In general, each random number generator had good performance and is well-suited for aircraft dynamic modeling applications. Specific strengths and weaknesses of each generator are discussed. For Monte Carlo simulation, the Fourier synthesis method is recommended because it most accurately and consistently approximated Gaussian white noise and can be implemented with reasonable computational effort.

  16. A random utility model of delay discounting and its application to people with externalizing psychopathology.

    Science.gov (United States)

    Dai, Junyi; Gunn, Rachel L; Gerst, Kyle R; Busemeyer, Jerome R; Finn, Peter R

    2016-10-01

    Previous studies have demonstrated that working memory capacity plays a central role in delay discounting in people with externalizing psychopathology. These studies used a hyperbolic discounting model, and its single parameter-a measure of delay discounting-was estimated using the standard method of searching for indifference points between intertemporal options. However, there are several problems with this approach. First, the deterministic perspective on delay discounting underlying the indifference point method might be inappropriate. Second, the estimation procedure using the R2 measure often leads to poor model fit. Third, when parameters are estimated using indifference points only, much of the information collected in a delay discounting decision task is wasted. To overcome these problems, this article proposes a random utility model of delay discounting. The proposed model has 2 parameters, 1 for delay discounting and 1 for choice variability. It was fit to choice data obtained from a recently published data set using both maximum-likelihood and Bayesian parameter estimation. As in previous studies, the delay discounting parameter was significantly associated with both externalizing problems and working memory capacity. Furthermore, choice variability was also found to be significantly associated with both variables. This finding suggests that randomness in decisions may be a mechanism by which externalizing problems and low working memory capacity are associated with poor decision making. The random utility model thus has the advantage of disclosing the role of choice variability, which had been masked by the traditional deterministic model. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  17. A Systematic Evaluation of Ultrasound-based Fetal Weight Estimation Models on Indian Population

    Directory of Open Access Journals (Sweden)

    Sujitkumar S. Hiwale

    2017-12-01

    Conclusion: We found that the existing fetal weight estimation models have high systematic and random errors on Indian population, with a general tendency of overestimation of fetal weight in the LBW category and underestimation in the HBW category. We also observed that these models have a limited ability to predict babies at a risk of either low or high birth weight. It is recommended that the clinicians should consider all these factors, while interpreting estimated weight given by the existing models.

  18. A spatial error model with continuous random effects and an application to growth convergence

    Science.gov (United States)

    Laurini, Márcio Poletti

    2017-10-01

    We propose a spatial error model with continuous random effects based on Matérn covariance functions and apply this model for the analysis of income convergence processes (β -convergence). The use of a model with continuous random effects permits a clearer visualization and interpretation of the spatial dependency patterns, avoids the problems of defining neighborhoods in spatial econometrics models, and allows projecting the spatial effects for every possible location in the continuous space, circumventing the existing aggregations in discrete lattice representations. We apply this model approach to analyze the economic growth of Brazilian municipalities between 1991 and 2010 using unconditional and conditional formulations and a spatiotemporal model of convergence. The results indicate that the estimated spatial random effects are consistent with the existence of income convergence clubs for Brazilian municipalities in this period.

  19. NASA Software Cost Estimation Model: An Analogy Based Estimation Model

    Science.gov (United States)

    Hihn, Jairus; Juster, Leora; Menzies, Tim; Mathew, George; Johnson, James

    2015-01-01

    The cost estimation of software development activities is increasingly critical for large scale integrated projects such as those at DOD and NASA especially as the software systems become larger and more complex. As an example MSL (Mars Scientific Laboratory) developed at the Jet Propulsion Laboratory launched with over 2 million lines of code making it the largest robotic spacecraft ever flown (Based on the size of the software). Software development activities are also notorious for their cost growth, with NASA flight software averaging over 50% cost growth. All across the agency, estimators and analysts are increasingly being tasked to develop reliable cost estimates in support of program planning and execution. While there has been extensive work on improving parametric methods there is very little focus on the use of models based on analogy and clustering algorithms. In this paper we summarize our findings on effort/cost model estimation and model development based on ten years of software effort estimation research using data mining and machine learning methods to develop estimation models based on analogy and clustering. The NASA Software Cost Model performance is evaluated by comparing it to COCOMO II, linear regression, and K-­ nearest neighbor prediction model performance on the same data set.

  20. Lifetime Reliability Estimate and Extreme Permanent Deformations of Randomly Excited Elasto-Plastic Structures

    DEFF Research Database (Denmark)

    Nielsen, Søren R.K.; Sørensen, John Dalsgaard; Thoft-Christensen, Palle

    1983-01-01

    plastic deformation during several loadings can be modelled as a filtered Poisson process. Using the Markov property of this quantity the considered first-passage problem as well as the related extreme distribution problems are then solved numerically, and the results are compared to simulation studies.......A method is presented for life-time reliability' estimates of randomly excited yielding systems, assuming the structure to be safe, when the plastic deformations are confined below certain limits. The accumulated plastic deformations during any single significant loading history are considered...

  1. Properties of the endogenous post-stratified estimator using a random forests model

    Science.gov (United States)

    John Tipton; Jean Opsomer; Gretchen G. Moisen

    2012-01-01

    Post-stratification is used in survey statistics as a method to improve variance estimates. In traditional post-stratification methods, the variable on which the data is being stratified must be known at the population level. In many cases this is not possible, but it is possible to use a model to predict values using covariates, and then stratify on these predicted...

  2. Joint estimation and contention-resolution protocol for wireless random access

    DEFF Research Database (Denmark)

    Stefanovic, Cedomir; Trillingsgaard, Kasper Fløe; Kiilerich Pratas, Nuno

    2013-01-01

    We propose a contention-based random-access protocol, designed for wireless networks where the number of users is not a priori known. The protocol operates in rounds divided into equal-duration slots, performing at the same time estimation of the number of users and resolution of their transmissi......We propose a contention-based random-access protocol, designed for wireless networks where the number of users is not a priori known. The protocol operates in rounds divided into equal-duration slots, performing at the same time estimation of the number of users and resolution...... successive interference cancellation which, coupled with the use of the optimized access probabilities, enables throughputs that are substantially higher than the traditional slotted ALOHA-like protocols. The key feature of the proposed protocol is that the round durations are not a priori set...

  3. Generalization of Random Intercept Multilevel Models

    Directory of Open Access Journals (Sweden)

    Rehan Ahmad Khan

    2013-10-01

    Full Text Available The concept of random intercept models in a multilevel model developed by Goldstein (1986 has been extended for k-levels. The random variation in intercepts at individual level is marginally split into components by incorporating higher levels of hierarchy in the single level model. So, one can control the random variation in intercepts by incorporating the higher levels in the model.

  4. Measurement model choice influenced randomized controlled trial results.

    Science.gov (United States)

    Gorter, Rosalie; Fox, Jean-Paul; Apeldoorn, Adri; Twisk, Jos

    2016-11-01

    In randomized controlled trials (RCTs), outcome variables are often patient-reported outcomes measured with questionnaires. Ideally, all available item information is used for score construction, which requires an item response theory (IRT) measurement model. However, in practice, the classical test theory measurement model (sum scores) is mostly used, and differences between response patterns leading to the same sum score are ignored. The enhanced differentiation between scores with IRT enables more precise estimation of individual trajectories over time and group effects. The objective of this study was to show the advantages of using IRT scores instead of sum scores when analyzing RCTs. Two studies are presented, a real-life RCT, and a simulation study. Both IRT and sum scores are used to measure the construct and are subsequently used as outcomes for effect calculation. The bias in RCT results is conditional on the measurement model that was used to construct the scores. A bias in estimated trend of around one standard deviation was found when sum scores were used, where IRT showed negligible bias. Accurate statistical inferences are made from an RCT study when using IRT to estimate construct measurements. The use of sum scores leads to incorrect RCT results. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves

    International Nuclear Information System (INIS)

    Bachschmid-Romano, Ludovica; Opper, Manfred

    2015-01-01

    We study analytically the performance of a recently proposed algorithm for learning the couplings of a random asymmetric kinetic Ising model from finite length trajectories of the spin dynamics. Our analysis shows the importance of the nontrivial equal time correlations between spins induced by the dynamics for the speed of learning. These correlations become more important as the spin’s stochasticity is decreased. We also analyse the deviation of the estimation error (paper)

  6. Restoration of dimensional reduction in the random-field Ising model at five dimensions

    Science.gov (United States)

    Fytas, Nikolaos G.; Martín-Mayor, Víctor; Picco, Marco; Sourlas, Nicolas

    2017-04-01

    The random-field Ising model is one of the few disordered systems where the perturbative renormalization group can be carried out to all orders of perturbation theory. This analysis predicts dimensional reduction, i.e., that the critical properties of the random-field Ising model in D dimensions are identical to those of the pure Ising ferromagnet in D -2 dimensions. It is well known that dimensional reduction is not true in three dimensions, thus invalidating the perturbative renormalization group prediction. Here, we report high-precision numerical simulations of the 5D random-field Ising model at zero temperature. We illustrate universality by comparing different probability distributions for the random fields. We compute all the relevant critical exponents (including the critical slowing down exponent for the ground-state finding algorithm), as well as several other renormalization-group invariants. The estimated values of the critical exponents of the 5D random-field Ising model are statistically compatible to those of the pure 3D Ising ferromagnet. These results support the restoration of dimensional reduction at D =5 . We thus conclude that the failure of the perturbative renormalization group is a low-dimensional phenomenon. We close our contribution by comparing universal quantities for the random-field problem at dimensions 3 ≤D equality at all studied dimensions.

  7. Exploring the Influence of Neighborhood Characteristics on Burglary Risks: A Bayesian Random Effects Modeling Approach

    Directory of Open Access Journals (Sweden)

    Hongqiang Liu

    2016-06-01

    Full Text Available A Bayesian random effects modeling approach was used to examine the influence of neighborhood characteristics on burglary risks in Jianghan District, Wuhan, China. This random effects model is essentially spatial; a spatially structured random effects term and an unstructured random effects term are added to the traditional non-spatial Poisson regression model. Based on social disorganization and routine activity theories, five covariates extracted from the available data at the neighborhood level were used in the modeling. Three regression models were fitted and compared by the deviance information criterion to identify which model best fit our data. A comparison of the results from the three models indicates that the Bayesian random effects model is superior to the non-spatial models in fitting the data and estimating regression coefficients. Our results also show that neighborhoods with above average bar density and department store density have higher burglary risks. Neighborhood-specific burglary risks and posterior probabilities of neighborhoods having a burglary risk greater than 1.0 were mapped, indicating the neighborhoods that should warrant more attention and be prioritized for crime intervention and reduction. Implications and limitations of the study are discussed in our concluding section.

  8. Linearization effect in multifractal analysis: Insights from the Random Energy Model

    Science.gov (United States)

    Angeletti, Florian; Mézard, Marc; Bertin, Eric; Abry, Patrice

    2011-08-01

    The analysis of the linearization effect in multifractal analysis, and hence of the estimation of moments for multifractal processes, is revisited borrowing concepts from the statistical physics of disordered systems, notably from the analysis of the so-called Random Energy Model. Considering a standard multifractal process (compound Poisson motion), chosen as a simple representative example, we show the following: (i) the existence of a critical order q∗ beyond which moments, though finite, cannot be estimated through empirical averages, irrespective of the sample size of the observation; (ii) multifractal exponents necessarily behave linearly in q, for q>q∗. Tailoring the analysis conducted for the Random Energy Model to that of Compound Poisson motion, we provide explicative and quantitative predictions for the values of q∗ and for the slope controlling the linear behavior of the multifractal exponents. These quantities are shown to be related only to the definition of the multifractal process and not to depend on the sample size of the observation. Monte Carlo simulations, conducted over a large number of large sample size realizations of compound Poisson motion, comfort and extend these analyses.

  9. Generalized linear longitudinal mixed models with linear covariance structure and multiplicative random effects

    DEFF Research Database (Denmark)

    Holst, René; Jørgensen, Bent

    2015-01-01

    The paper proposes a versatile class of multiplicative generalized linear longitudinal mixed models (GLLMM) with additive dispersion components, based on explicit modelling of the covariance structure. The class incorporates a longitudinal structure into the random effects models and retains...... a marginal as well as a conditional interpretation. The estimation procedure is based on a computationally efficient quasi-score method for the regression parameters combined with a REML-like bias-corrected Pearson estimating function for the dispersion and correlation parameters. This avoids...... the multidimensional integral of the conventional GLMM likelihood and allows an extension of the robust empirical sandwich estimator for use with both association and regression parameters. The method is applied to a set of otholit data, used for age determination of fish....

  10. Modeling of chromosome intermingling by partially overlapping uniform random polygons.

    Science.gov (United States)

    Blackstone, T; Scharein, R; Borgo, B; Varela, R; Diao, Y; Arsuaga, J

    2011-03-01

    During the early phase of the cell cycle the eukaryotic genome is organized into chromosome territories. The geometry of the interface between any two chromosomes remains a matter of debate and may have important functional consequences. The Interchromosomal Network model (introduced by Branco and Pombo) proposes that territories intermingle along their periphery. In order to partially quantify this concept we here investigate the probability that two chromosomes form an unsplittable link. We use the uniform random polygon as a crude model for chromosome territories and we model the interchromosomal network as the common spatial region of two overlapping uniform random polygons. This simple model allows us to derive some rigorous mathematical results as well as to perform computer simulations easily. We find that the probability that one uniform random polygon of length n that partially overlaps a fixed polygon is bounded below by 1 − O(1/√n). We use numerical simulations to estimate the dependence of the linking probability of two uniform random polygons (of lengths n and m, respectively) on the amount of overlapping. The degree of overlapping is parametrized by a parameter [Formula: see text] such that [Formula: see text] indicates no overlapping and [Formula: see text] indicates total overlapping. We propose that this dependence relation may be modeled as f (ε, m, n) = [Formula: see text]. Numerical evidence shows that this model works well when [Formula: see text] is relatively large (ε ≥ 0.5). We then use these results to model the data published by Branco and Pombo and observe that for the amount of overlapping observed experimentally the URPs have a non-zero probability of forming an unsplittable link.

  11. Gaussian Mixture Random Coefficient model based framework for SHM in structures with time-dependent dynamics under uncertainty

    Science.gov (United States)

    Avendaño-Valencia, Luis David; Fassois, Spilios D.

    2017-12-01

    The problem of vibration-based damage diagnosis in structures characterized by time-dependent dynamics under significant environmental and/or operational uncertainty is considered. A stochastic framework consisting of a Gaussian Mixture Random Coefficient model of the uncertain time-dependent dynamics under each structural health state, proper estimation methods, and Bayesian or minimum distance type decision making, is postulated. The Random Coefficient (RC) time-dependent stochastic model with coefficients following a multivariate Gaussian Mixture Model (GMM) allows for significant flexibility in uncertainty representation. Certain of the model parameters are estimated via a simple procedure which is founded on the related Multiple Model (MM) concept, while the GMM weights are explicitly estimated for optimizing damage diagnostic performance. The postulated framework is demonstrated via damage detection in a simple simulated model of a quarter-car active suspension with time-dependent dynamics and considerable uncertainty on the payload. Comparisons with a simpler Gaussian RC model based method are also presented, with the postulated framework shown to be capable of offering considerable improvement in diagnostic performance.

  12. On the estimation variance for the specific Euler-Poincaré characteristic of random networks.

    Science.gov (United States)

    Tscheschel, A; Stoyan, D

    2003-07-01

    The specific Euler number is an important topological characteristic in many applications. It is considered here for the case of random networks, which may appear in microscopy either as primary objects of investigation or as secondary objects describing in an approximate way other structures such as, for example, porous media. For random networks there is a simple and natural estimator of the specific Euler number. For its estimation variance, a simple Poisson approximation is given. It is based on the general exact formula for the estimation variance. In two examples of quite different nature and topology application of the formulas is demonstrated.

  13. Nonparametric Estimation of Distributions in Random Effects Models

    KAUST Repository

    Hart, Jeffrey D.; Cañ ette, Isabel

    2011-01-01

    to every small dataset. A detailed algorithm for computing minimum distance estimates is proposed, and the usefulness of our methodology is illustrated by a simulation study and an analysis of microarray data. Supplemental materials for the article

  14. Challenges in modelling the random structure correctly in growth mixture models and the impact this has on model mixtures.

    Science.gov (United States)

    Gilthorpe, M S; Dahly, D L; Tu, Y K; Kubzansky, L D; Goodman, E

    2014-06-01

    Lifecourse trajectories of clinical or anthropological attributes are useful for identifying how our early-life experiences influence later-life morbidity and mortality. Researchers often use growth mixture models (GMMs) to estimate such phenomena. It is common to place constrains on the random part of the GMM to improve parsimony or to aid convergence, but this can lead to an autoregressive structure that distorts the nature of the mixtures and subsequent model interpretation. This is especially true if changes in the outcome within individuals are gradual compared with the magnitude of differences between individuals. This is not widely appreciated, nor is its impact well understood. Using repeat measures of body mass index (BMI) for 1528 US adolescents, we estimated GMMs that required variance-covariance constraints to attain convergence. We contrasted constrained models with and without an autocorrelation structure to assess the impact this had on the ideal number of latent classes, their size and composition. We also contrasted model options using simulations. When the GMM variance-covariance structure was constrained, a within-class autocorrelation structure emerged. When not modelled explicitly, this led to poorer model fit and models that differed substantially in the ideal number of latent classes, as well as class size and composition. Failure to carefully consider the random structure of data within a GMM framework may lead to erroneous model inferences, especially for outcomes with greater within-person than between-person homogeneity, such as BMI. It is crucial to reflect on the underlying data generation processes when building such models.

  15. Tukey g-and-h Random Fields

    KAUST Repository

    Xu, Ganggang; Genton, Marc G.

    2016-01-01

    We propose a new class of trans-Gaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of the maximum likelihood estimator and the probabilistic properties of the TGH random fields are investigated. An efficient estimation procedure, based on maximum approximated likelihood, is proposed and an extreme spatial outlier detection algorithm is formulated. Kriging and probabilistic prediction with TGH random fields are developed along with prediction confidence intervals. The predictive performance of TGH random fields is demonstrated through extensive simulation studies and an application to a dataset of total precipitation in the south east of the United States.

  16. Tukey g-and-h Random Fields

    KAUST Repository

    Xu, Ganggang

    2016-07-15

    We propose a new class of trans-Gaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of the maximum likelihood estimator and the probabilistic properties of the TGH random fields are investigated. An efficient estimation procedure, based on maximum approximated likelihood, is proposed and an extreme spatial outlier detection algorithm is formulated. Kriging and probabilistic prediction with TGH random fields are developed along with prediction confidence intervals. The predictive performance of TGH random fields is demonstrated through extensive simulation studies and an application to a dataset of total precipitation in the south east of the United States.

  17. Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Kammoun, Abla; Al-Naffouri, Tareq Y.

    2016-01-01

    random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various

  18. Estimates of Inequality Indices Based on Simple Random, Ranked Set, and Systematic Sampling

    OpenAIRE

    Bansal, Pooja; Arora, Sangeeta; Mahajan, Kalpana K.

    2013-01-01

    Gini index, Bonferroni index, and Absolute Lorenz index are some popular indices of inequality showing different features of inequality measurement. In general simple random sampling procedure is commonly used to estimate the inequality indices and their related inference. The key condition that the samples must be drawn via simple random sampling procedure though makes calculations much simpler but this assumption is often violated in practice as the data does not always yield simple random ...

  19. The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples

    Science.gov (United States)

    Avetisyan, Marianna; Fox, Jean-Paul

    2012-01-01

    In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…

  20. A spatial approach to the modelling and estimation of areal precipitation

    Energy Technology Data Exchange (ETDEWEB)

    Skaugen, T

    1996-12-31

    In hydroelectric power technology it is important that the mean precipitation that falls in an area can be calculated. This doctoral thesis studies how the morphology of rainfall, described by the spatial statistical parameters, can be used to improve interpolation and estimation procedures. It attempts to formulate a theory which includes the relations between the size of the catchment and the size of the precipitation events in the modelling of areal precipitation. The problem of estimating and modelling areal precipitation can be formulated as the problem of estimating an inhomogeneously distributed flux of a certain spatial extent being measured at points in a randomly placed domain. The information contained in the different morphology of precipitation types is used to improve estimation procedures of areal precipitation, by interpolation (kriging) or by constructing areal reduction factors. A new approach to precipitation modelling is introduced where the analysis of the spatial coverage of precipitation at different intensities plays a key role in the formulation of a stochastic model for extreme areal precipitation and in deriving the probability density function of areal precipitation. 127 refs., 30 figs., 13 tabs.

  1. Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

    Science.gov (United States)

    Lin, Huiming; Fu, Bo; Qin, Guoyou; Zhu, Zhongyi

    2017-12-01

    We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study. © 2017, The International Biometric Society.

  2. Adjustment of Measurements with Multiplicative Errors: Error Analysis, Estimates of the Variance of Unit Weight, and Effect on Volume Estimation from LiDAR-Type Digital Elevation Models

    Directory of Open Access Journals (Sweden)

    Yun Shi

    2014-01-01

    Full Text Available Modern observation technology has verified that measurement errors can be proportional to the true values of measurements such as GPS, VLBI baselines and LiDAR. Observational models of this type are called multiplicative error models. This paper is to extend the work of Xu and Shimada published in 2000 on multiplicative error models to analytical error analysis of quantities of practical interest and estimates of the variance of unit weight. We analytically derive the variance-covariance matrices of the three least squares (LS adjustments, the adjusted measurements and the corrections of measurements in multiplicative error models. For quality evaluation, we construct five estimators for the variance of unit weight in association of the three LS adjustment methods. Although LiDAR measurements are contaminated with multiplicative random errors, LiDAR-based digital elevation models (DEM have been constructed as if they were of additive random errors. We will simulate a model landslide, which is assumed to be surveyed with LiDAR, and investigate the effect of LiDAR-type multiplicative error measurements on DEM construction and its effect on the estimate of landslide mass volume from the constructed DEM.

  3. Remote optimal state estimation over communication channels with random delays

    KAUST Repository

    Mahmoud, Magdi S.

    2014-01-22

    This paper considers the optimal estimation of linear systems over unreliable communication channels with random delays. In this work, it is assumed that the system to be estimated is far away from the filter. The observations of the system are capsulized without time stamp and then transmitted to the network node at which the filter is located. The probabilities of time delays are assumed to be known. The event-driven estimation scheme is applied in this paper and the estimate of the states is updated only at each time instant when any measurement arrives. To capture the feature of communication, the system considered is augmented, and the arrived measurements are regarded as the uncertain observations of the augmented system. The corresponding optimal estimation algorithm is proposed and additionally, a numerical simulation represents the performance of this work. © 2014 The authors. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

  4. Analyzing indirect effects in cluster randomized trials. The effect of estimation method, number of groups and group sizes on accuracy and power.

    Directory of Open Access Journals (Sweden)

    Joop eHox

    2014-02-01

    Full Text Available Cluster randomized trials assess the effect of an intervention that is carried out at the group or cluster level. Ajzen’s theory of planned behaviour is often used to model the effect of the intervention as an indirect effect mediated in turn by attitude, norms and behavioural intention. Structural equation modelling (SEM is the technique of choice to estimate indirect effects and their significance. However, this is a large sample technique, and its application in a cluster randomized trial assumes a relatively large number of clusters. In practice, the number of clusters in these studies tends to be relatively small, e.g. much less than fifty. This study uses simulation methods to find the lowest number of clusters needed when multilevel SEM is used to estimate the indirect effect. Maximum likelihood estimation is compared to Bayesian analysis, with the central quality criteria being accuracy of the point estimate and the confidence interval. We also investigate the power of the test for the indirect effect. We conclude that Bayes estimation works well with much smaller cluster level sample sizes such as 20 cases than maximum likelihood estimation; although the bias is larger the coverage is much better. When only 5 to 10 clusters are available per treatment condition even with Bayesian estimation problems occur.

  5. Estimating anisotropic diffusion of neutrons near the boundary of a pebble bed random system

    Energy Technology Data Exchange (ETDEWEB)

    Vasques, R. [Department of Mathematics, Center for Computational Engineering Science, RWTH Aachen University, Schinkel Strasse 2, D-52062 Aachen (Germany)

    2013-07-01

    Due to the arrangement of the pebbles in a Pebble Bed Reactor (PBR) core, if a neutron is located close to a boundary wall, its path length probability distribution function in directions of flight parallel to the wall is significantly different than in other directions. Hence, anisotropic diffusion of neutrons near the boundaries arises. We describe an analysis of neutron transport in a simplified 3-D pebble bed random system, in which we investigate the anisotropic diffusion of neutrons born near one of the system's boundary walls. While this simplified system does not model the actual physical process that takes place near the boundaries of a PBR core, the present work paves the road to a formulation that may enable more accurate diffusion simulations of such problems to be performed in the future. Monte Carlo codes have been developed for (i) deriving realizations of the 3-D random system, and (ii) performing 3-D neutron transport inside the heterogeneous model; numerical results are presented for three different choices of parameters. These numerical results are used to assess the accuracy of estimates for the mean-squared displacement of neutrons obtained with the diffusion approximations of the Atomic Mix Model and of the recently introduced [1] Non-Classical Theory with angular-dependent path length distribution. The Non-Classical Theory makes use of a Generalized Linear Boltzmann Equation in which the locations of the scattering centers in the system are correlated and the distance to collision is not exponentially distributed. We show that the results predicted using the Non-Classical Theory successfully model the anisotropic behavior of the neutrons in the random system, and more closely agree with experiment than the results predicted by the Atomic Mix Model. (authors)

  6. Estimating anisotropic diffusion of neutrons near the boundary of a pebble bed random system

    International Nuclear Information System (INIS)

    Vasques, R.

    2013-01-01

    Due to the arrangement of the pebbles in a Pebble Bed Reactor (PBR) core, if a neutron is located close to a boundary wall, its path length probability distribution function in directions of flight parallel to the wall is significantly different than in other directions. Hence, anisotropic diffusion of neutrons near the boundaries arises. We describe an analysis of neutron transport in a simplified 3-D pebble bed random system, in which we investigate the anisotropic diffusion of neutrons born near one of the system's boundary walls. While this simplified system does not model the actual physical process that takes place near the boundaries of a PBR core, the present work paves the road to a formulation that may enable more accurate diffusion simulations of such problems to be performed in the future. Monte Carlo codes have been developed for (i) deriving realizations of the 3-D random system, and (ii) performing 3-D neutron transport inside the heterogeneous model; numerical results are presented for three different choices of parameters. These numerical results are used to assess the accuracy of estimates for the mean-squared displacement of neutrons obtained with the diffusion approximations of the Atomic Mix Model and of the recently introduced [1] Non-Classical Theory with angular-dependent path length distribution. The Non-Classical Theory makes use of a Generalized Linear Boltzmann Equation in which the locations of the scattering centers in the system are correlated and the distance to collision is not exponentially distributed. We show that the results predicted using the Non-Classical Theory successfully model the anisotropic behavior of the neutrons in the random system, and more closely agree with experiment than the results predicted by the Atomic Mix Model. (authors)

  7. Estimation of Sensitive Proportion by Randomized Response Data in Successive Sampling

    Directory of Open Access Journals (Sweden)

    Bo Yu

    2015-01-01

    Full Text Available This paper considers the problem of estimation for binomial proportions of sensitive or stigmatizing attributes in the population of interest. Randomized response techniques are suggested for protecting the privacy of respondents and reducing the response bias while eliciting information on sensitive attributes. In many sensitive question surveys, the same population is often sampled repeatedly on each occasion. In this paper, we apply successive sampling scheme to improve the estimation of the sensitive proportion on current occasion.

  8. Bayesian and Classical Estimation of Stress-Strength Reliability for Inverse Weibull Lifetime Models

    Directory of Open Access Journals (Sweden)

    Qixuan Bi

    2017-06-01

    Full Text Available In this paper, we consider the problem of estimating stress-strength reliability for inverse Weibull lifetime models having the same shape parameters but different scale parameters. We obtain the maximum likelihood estimator and its asymptotic distribution. Since the classical estimator doesn’t hold explicit forms, we propose an approximate maximum likelihood estimator. The asymptotic confidence interval and two bootstrap intervals are obtained. Using the Gibbs sampling technique, Bayesian estimator and the corresponding credible interval are obtained. The Metropolis-Hastings algorithm is used to generate random variates. Monte Carlo simulations are conducted to compare the proposed methods. Analysis of a real dataset is performed.

  9. Modelling and estimating degradation processes with application in structural reliability

    International Nuclear Information System (INIS)

    Chiquet, J.

    2007-06-01

    The characteristic level of degradation of a given structure is modeled through a stochastic process called the degradation process. The random evolution of the degradation process is governed by a differential system with Markovian environment. We put the associated reliability framework by considering the failure of the structure once the degradation process reaches a critical threshold. A closed form solution of the reliability function is obtained thanks to Markov renewal theory. Then, we build an estimation methodology for the parameters of the stochastic processes involved. The estimation methods and the theoretical results, as well as the associated numerical algorithms, are validated on simulated data sets. Our method is applied to the modelling of a real degradation mechanism, known as crack growth, for which an experimental data set is considered. (authors)

  10. Bounds and Estimates for Transport Coefficients of Random and Porous Media with High Contrasts

    International Nuclear Information System (INIS)

    Berryman, J G

    2004-01-01

    Bounds on transport coefficients of random polycrystals of laminates are presented, including the well-known Hashin-Shtrikman bounds and some newly formulated bounds involving two formation factors for a two-component porous medium. Some new types of self-consistent estimates are then formulated based on the observed analytical structure both of these bounds and also of earlier self-consistent estimates (of the CPA or coherent potential approximation type). A numerical study is made, assuming first that the internal structure (i.e., the laminated grain structure) is not known, and then that it is known. The purpose of this aspect of the study is to attempt to quantify the differences in the predictions of properties of a system being modeled when such organized internal structure is present in the medium but detailed spatial correlation information may or (more commonly) may not be available. Some methods of estimating formation factors from data are also presented and then applied to a high-contrast fluid-permeability data set. Hashin-Shtrikman bounds are found to be very accurate estimates for low contrast heterogeneous media. But formation factor lower bounds are superior estimates for high contrast situations. The new self-consistent estimators also tend to agree better with data than either the bounds or the CPA estimates, which themselves tend to overestimate values for high contrast conducting composites

  11. A random regression model in analysis of litter size in pigs | Lukovi& ...

    African Journals Online (AJOL)

    Dispersion parameters for number of piglets born alive (NBA) were estimated using a random regression model (RRM). Two data sets of litter records from the Nemščak farm in Slovenia were used for analyses. The first dataset (DS1) included records from the first to the sixth parity. The second dataset (DS2) was extended ...

  12. A simulation study on estimating biomarker-treatment interaction effects in randomized trials with prognostic variables.

    Science.gov (United States)

    Haller, Bernhard; Ulm, Kurt

    2018-02-20

    To individualize treatment decisions based on patient characteristics, identification of an interaction between a biomarker and treatment is necessary. Often such potential interactions are analysed using data from randomized clinical trials intended for comparison of two treatments. Tests of interactions are often lacking statistical power and we investigated if and how a consideration of further prognostic variables can improve power and decrease the bias of estimated biomarker-treatment interactions in randomized clinical trials with time-to-event outcomes. A simulation study was performed to assess how prognostic factors affect the estimate of the biomarker-treatment interaction for a time-to-event outcome, when different approaches, like ignoring other prognostic factors, including all available covariates or using variable selection strategies, are applied. Different scenarios regarding the proportion of censored observations, the correlation structure between the covariate of interest and further potential prognostic variables, and the strength of the interaction were considered. The simulation study revealed that in a regression model for estimating a biomarker-treatment interaction, the probability of detecting a biomarker-treatment interaction can be increased by including prognostic variables that are associated with the outcome, and that the interaction estimate is biased when relevant prognostic variables are not considered. However, the probability of a false-positive finding increases if too many potential predictors are included or if variable selection is performed inadequately. We recommend undertaking an adequate literature search before data analysis to derive information about potential prognostic variables and to gain power for detecting true interaction effects and pre-specifying analyses to avoid selective reporting and increased false-positive rates.

  13. A random sampling approach for robust estimation of tissue-to-plasma ratio from extremely sparse data.

    Science.gov (United States)

    Chu, Hui-May; Ette, Ene I

    2005-09-02

    his study was performed to develop a new nonparametric approach for the estimation of robust tissue-to-plasma ratio from extremely sparsely sampled paired data (ie, one sample each from plasma and tissue per subject). Tissue-to-plasma ratio was estimated from paired/unpaired experimental data using independent time points approach, area under the curve (AUC) values calculated with the naïve data averaging approach, and AUC values calculated using sampling based approaches (eg, the pseudoprofile-based bootstrap [PpbB] approach and the random sampling approach [our proposed approach]). The random sampling approach involves the use of a 2-phase algorithm. The convergence of the sampling/resampling approaches was investigated, as well as the robustness of the estimates produced by different approaches. To evaluate the latter, new data sets were generated by introducing outlier(s) into the real data set. One to 2 concentration values were inflated by 10% to 40% from their original values to produce the outliers. Tissue-to-plasma ratios computed using the independent time points approach varied between 0 and 50 across time points. The ratio obtained from AUC values acquired using the naive data averaging approach was not associated with any measure of uncertainty or variability. Calculating the ratio without regard to pairing yielded poorer estimates. The random sampling and pseudoprofile-based bootstrap approaches yielded tissue-to-plasma ratios with uncertainty and variability. However, the random sampling approach, because of the 2-phase nature of its algorithm, yielded more robust estimates and required fewer replications. Therefore, a 2-phase random sampling approach is proposed for the robust estimation of tissue-to-plasma ratio from extremely sparsely sampled data.

  14. Assessing robustness of designs for random effects parameters for nonlinear mixed-effects models.

    Science.gov (United States)

    Duffull, Stephen B; Hooker, Andrew C

    2017-12-01

    Optimal designs for nonlinear models are dependent on the choice of parameter values. Various methods have been proposed to provide designs that are robust to uncertainty in the prior choice of parameter values. These methods are generally based on estimating the expectation of the determinant (or a transformation of the determinant) of the information matrix over the prior distribution of the parameter values. For high dimensional models this can be computationally challenging. For nonlinear mixed-effects models the question arises as to the importance of accounting for uncertainty in the prior value of the variances of the random effects parameters. In this work we explore the influence of the variance of the random effects parameters on the optimal design. We find that the method for approximating the expectation and variance of the likelihood is of potential importance for considering the influence of random effects. The most common approximation to the likelihood, based on a first-order Taylor series approximation, yields designs that are relatively insensitive to the prior value of the variance of the random effects parameters and under these conditions it appears to be sufficient to consider uncertainty on the fixed-effects parameters only.

  15. Effect of inter-crystal scatter on estimation methods for random coincidences and subsequent correction

    International Nuclear Information System (INIS)

    Torres-Espallardo, I; Spanoudaki, V; Ziegler, S I; Rafecas, M; McElroy, D P

    2008-01-01

    Random coincidences can contribute substantially to the background in positron emission tomography (PET). Several estimation methods are being used for correcting them. The goal of this study was to investigate the validity of techniques for random coincidence estimation, with various low-energy thresholds (LETs). Simulated singles list-mode data of the MADPET-II small animal PET scanner were used as input. The simulations have been performed using the GATE simulation toolkit. Several sources with different geometries have been employed. We evaluated the number of random events using three methods: delayed window (DW), singles rate (SR) and time histogram fitting (TH). Since the GATE simulations allow random and true coincidences to be distinguished, a comparison between the number of random coincidences estimated using the standard methods and the number obtained using GATE was performed. An overestimation in the number of random events was observed using the DW and SR methods. This overestimation decreases for LETs higher than 255 keV. It is additionally reduced when the single events which have undergone a Compton interaction in crystals before being detected are removed from the data. These two observations lead us to infer that the overestimation is due to inter-crystal scatter. The effect of this mismatch in the reconstructed images is important for quantification because it leads to an underestimation of activity. This was shown using a hot-cold-background source with 3.7 MBq total activity in the background region and a 1.59 MBq total activity in the hot region. For both 200 keV and 400 keV LET, an overestimation of random coincidences for the DW and SR methods was observed, resulting in approximately 1.5% or more (at 200 keV LET: 1.7% for DW and 7% for SR) and less than 1% (at 400 keV LET: both methods) underestimation of activity within the background region. In almost all cases, images obtained by compensating for random events in the reconstruction

  16. GLOBAL RANDOM WALK SIMULATIONS FOR SENSITIVITY AND UNCERTAINTY ANALYSIS OF PASSIVE TRANSPORT MODELS

    Directory of Open Access Journals (Sweden)

    Nicolae Suciu

    2011-07-01

    Full Text Available The Global Random Walk algorithm (GRW performs a simultaneoustracking on a fixed grid of huge numbers of particles at costscomparable to those of a single-trajectory simulation by the traditional Particle Tracking (PT approach. Statistical ensembles of GRW simulations of a typical advection-dispersion process in groundwater systems with randomly distributed spatial parameters are used to obtain reliable estimations of the input parameters for the upscaled transport model and of their correlations, input-output correlations, as well as full probability distributions of the input and output parameters.

  17. Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches.

    Science.gov (United States)

    Chan, Jennifer S K

    2016-05-01

    Dropouts are common in longitudinal study. If the dropout probability depends on the missing observations at or after dropout, this type of dropout is called informative (or nonignorable) dropout (ID). Failure to accommodate such dropout mechanism into the model will bias the parameter estimates. We propose a conditional autoregressive model for longitudinal binary data with an ID model such that the probabilities of positive outcomes as well as the drop-out indicator in each occasion are logit linear in some covariates and outcomes. This model adopting a marginal model for outcomes and a conditional model for dropouts is called a selection model. To allow for the heterogeneity and clustering effects, the outcome model is extended to incorporate mixture and random effects. Lastly, the model is further extended to a novel model that models the outcome and dropout jointly such that their dependency is formulated through an odds ratio function. Parameters are estimated by a Bayesian approach implemented using the user-friendly Bayesian software WinBUGS. A methadone clinic dataset is analyzed to illustrate the proposed models. Result shows that the treatment time effect is still significant but weaker after allowing for an ID process in the data. Finally the effect of drop-out on parameter estimates is evaluated through simulation studies. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. Estimation of breeding values for mean and dispersion, their variance and correlation using double hierarchical generalized linear models.

    Science.gov (United States)

    Felleki, M; Lee, D; Lee, Y; Gilmour, A R; Rönnegård, L

    2012-12-01

    The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being -0·52 for IRWLS and -0·62 in Sorensen & Waagepetersen (2003).

  19. An Online SOC and SOH Estimation Model for Lithium-Ion Batteries

    Directory of Open Access Journals (Sweden)

    Shyh-Chin Huang

    2017-04-01

    Full Text Available The monitoring and prognosis of cell degradation in lithium-ion (Li-ion batteries are essential for assuring the reliability and safety of electric and hybrid vehicles. This paper aims to develop a reliable and accurate model for online, simultaneous state-of-charge (SOC and state-of-health (SOH estimations of Li-ion batteries. Through the analysis of battery cycle-life test data, the instantaneous discharging voltage (V and its unit time voltage drop, V′, are proposed as the model parameters for the SOC equation. The SOH equation is found to have a linear relationship with 1/V′ times the modification factor, which is a function of SOC. Four batteries are tested in the laboratory, and the data are regressed for the model coefficients. The results show that the model built upon the data from one single cell is able to estimate the SOC and SOH of the three other cells within a 5% error bound. The derived model is also proven to be robust. A random sampling test to simulate the online real-time SOC and SOH estimation proves that this model is accurate and can be potentially used in an electric vehicle battery management system (BMS.

  20. Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

    Science.gov (United States)

    Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

    2018-05-01

    Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

  1. Conditional Monte Carlo randomization tests for regression models.

    Science.gov (United States)

    Parhat, Parwen; Rosenberger, William F; Diao, Guoqing

    2014-08-15

    We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.

  2. Infinite Random Graphs as Statistical Mechanical Models

    DEFF Research Database (Denmark)

    Durhuus, Bergfinnur Jøgvan; Napolitano, George Maria

    2011-01-01

    We discuss two examples of infinite random graphs obtained as limits of finite statistical mechanical systems: a model of two-dimensional dis-cretized quantum gravity defined in terms of causal triangulated surfaces, and the Ising model on generic random trees. For the former model we describe a ...

  3. Estimation and Inference for Very Large Linear Mixed Effects Models

    OpenAIRE

    Gao, K.; Owen, A. B.

    2016-01-01

    Linear mixed models with large imbalanced crossed random effects structures pose severe computational problems for maximum likelihood estimation and for Bayesian analysis. The costs can grow as fast as $N^{3/2}$ when there are N observations. Such problems arise in any setting where the underlying factors satisfy a many to many relationship (instead of a nested one) and in electronic commerce applications, the N can be quite large. Methods that do not account for the correlation structure can...

  4. The Compound Binomial Risk Model with Randomly Charging Premiums and Paying Dividends to Shareholders

    Directory of Open Access Journals (Sweden)

    Xiong Wang

    2013-01-01

    Full Text Available Based on characteristics of the nonlife joint-stock insurance company, this paper presents a compound binomial risk model that randomizes the premium income on unit time and sets the threshold for paying dividends to shareholders. In this model, the insurance company obtains the insurance policy in unit time with probability and pays dividends to shareholders with probability when the surplus is no less than . We then derive the recursive formulas of the expected discounted penalty function and the asymptotic estimate for it. And we will derive the recursive formulas and asymptotic estimates for the ruin probability and the distribution function of the deficit at ruin. The numerical examples have been shown to illustrate the accuracy of the asymptotic estimations.

  5. Republic of Georgia estimates for prevalence of drug use: Randomized response techniques suggest under-estimation.

    Science.gov (United States)

    Kirtadze, Irma; Otiashvili, David; Tabatadze, Mzia; Vardanashvili, Irina; Sturua, Lela; Zabransky, Tomas; Anthony, James C

    2018-06-01

    Validity of responses in surveys is an important research concern, especially in emerging market economies where surveys in the general population are a novelty, and the level of social control is traditionally higher. The Randomized Response Technique (RRT) can be used as a check on response validity when the study aim is to estimate population prevalence of drug experiences and other socially sensitive and/or illegal behaviors. To apply RRT and to study potential under-reporting of drug use in a nation-scale, population-based general population survey of alcohol and other drug use. For this first-ever household survey on addictive substances for the Country of Georgia, we used the multi-stage probability sampling of 18-to-64-year-old household residents of 111 urban and 49 rural areas. During the interviewer-administered assessments, RRT involved pairing of sensitive and non-sensitive questions about drug experiences. Based upon the standard household self-report survey estimate, an estimated 17.3% [95% confidence interval, CI: 15.5%, 19.1%] of Georgian household residents have tried cannabis. The corresponding RRT estimate was 29.9% [95% CI: 24.9%, 34.9%]. The RRT estimates for other drugs such as heroin also were larger than the standard self-report estimates. We remain unsure about what is the "true" value for prevalence of using illegal psychotropic drugs in the Republic of Georgia study population. Our RRT results suggest that standard non-RRT approaches might produce 'under-estimates' or at best, highly conservative, lower-end estimates. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. Case management: a randomized controlled study comparing a neighborhood team and a centralized individual model.

    OpenAIRE

    Eggert, G M; Zimmer, J G; Hall, W J; Friedman, B

    1991-01-01

    This randomized controlled study compared two types of case management for skilled nursing level patients living at home: the centralized individual model and the neighborhood team model. The team model differed from the individual model in that team case managers performed client assessments, care planning, some direct services, and reassessments; they also had much smaller caseloads and were assigned a specific catchment area. While patients in both groups incurred very high estimated healt...

  7. Dispersal and habitat connectivity in complex heterogeneous landscapes: an analysis with a GIS based random walk model

    NARCIS (Netherlands)

    Schippers, P.; Verboom, J.; Knaapen, J.P.; Apeldoorn, van R.

    1996-01-01

    A grid-based random walk model has been developed to simulate animal dispersal, taking landscape heterogeneity and linear barriers such as roads and rivers into account. The model can be used to estimate connectivity and has been parameterized for thebadger in the central part of the Netherlands.

  8. Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization

    Science.gov (United States)

    Dai, James Y.; Chan, Kwun Chuen Gary; Hsu, Li

    2014-01-01

    Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerale work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent due to the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed. PMID:24863158

  9. Estimation of uncertainties in predictions of environmental transfer models: evaluation of methods and application to CHERPAC

    International Nuclear Information System (INIS)

    Koch, J.; Peterson, S-R.

    1995-10-01

    Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs

  10. Estimation of uncertainties in predictions of environmental transfer models: evaluation of methods and application to CHERPAC

    Energy Technology Data Exchange (ETDEWEB)

    Koch, J. [Israel Atomic Energy Commission, Yavne (Israel). Soreq Nuclear Research Center; Peterson, S-R.

    1995-10-01

    Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs.

  11. Estimating Model Probabilities using Thermodynamic Markov Chain Monte Carlo Methods

    Science.gov (United States)

    Ye, M.; Liu, P.; Beerli, P.; Lu, D.; Hill, M. C.

    2014-12-01

    Markov chain Monte Carlo (MCMC) methods are widely used to evaluate model probability for quantifying model uncertainty. In a general procedure, MCMC simulations are first conducted for each individual model, and MCMC parameter samples are then used to approximate marginal likelihood of the model by calculating the geometric mean of the joint likelihood of the model and its parameters. It has been found the method of evaluating geometric mean suffers from the numerical problem of low convergence rate. A simple test case shows that even millions of MCMC samples are insufficient to yield accurate estimation of the marginal likelihood. To resolve this problem, a thermodynamic method is used to have multiple MCMC runs with different values of a heating coefficient between zero and one. When the heating coefficient is zero, the MCMC run is equivalent to a random walk MC in the prior parameter space; when the heating coefficient is one, the MCMC run is the conventional one. For a simple case with analytical form of the marginal likelihood, the thermodynamic method yields more accurate estimate than the method of using geometric mean. This is also demonstrated for a case of groundwater modeling with consideration of four alternative models postulated based on different conceptualization of a confining layer. This groundwater example shows that model probabilities estimated using the thermodynamic method are more reasonable than those obtained using the geometric method. The thermodynamic method is general, and can be used for a wide range of environmental problem for model uncertainty quantification.

  12. Knock probability estimation through an in-cylinder temperature model with exogenous noise

    Science.gov (United States)

    Bares, P.; Selmanaj, D.; Guardiola, C.; Onder, C.

    2018-01-01

    This paper presents a new knock model which combines a deterministic knock model based on the in-cylinder temperature and an exogenous noise disturbing this temperature. The autoignition of the end-gas is modelled by an Arrhenius-like function and the knock probability is estimated by propagating a virtual error probability distribution. Results show that the random nature of knock can be explained by uncertainties at the in-cylinder temperature estimation. The model only has one parameter for calibration and thus can be easily adapted online. In order to reduce the measurement uncertainties associated with the air mass flow sensor, the trapped mass is derived from the in-cylinder pressure resonance, which improves the knock probability estimation and reduces the number of sensors needed for the model. A four stroke SI engine was used for model validation. By varying the intake temperature, the engine speed, the injected fuel mass, and the spark advance, specific tests were conducted, which furnished data with various knock intensities and probabilities. The new model is able to predict the knock probability within a sufficient range at various operating conditions. The trapped mass obtained by the acoustical model was compared in steady conditions by using a fuel balance and a lambda sensor and differences below 1 % were found.

  13. Social aggregation in pea aphids: experiment and random walk modeling.

    Directory of Open Access Journals (Sweden)

    Christa Nilsen

    Full Text Available From bird flocks to fish schools and ungulate herds to insect swarms, social biological aggregations are found across the natural world. An ongoing challenge in the mathematical modeling of aggregations is to strengthen the connection between models and biological data by quantifying the rules that individuals follow. We model aggregation of the pea aphid, Acyrthosiphon pisum. Specifically, we conduct experiments to track the motion of aphids walking in a featureless circular arena in order to deduce individual-level rules. We observe that each aphid transitions stochastically between a moving and a stationary state. Moving aphids follow a correlated random walk. The probabilities of motion state transitions, as well as the random walk parameters, depend strongly on distance to an aphid's nearest neighbor. For large nearest neighbor distances, when an aphid is essentially isolated, its motion is ballistic with aphids moving faster, turning less, and being less likely to stop. In contrast, for short nearest neighbor distances, aphids move more slowly, turn more, and are more likely to become stationary; this behavior constitutes an aggregation mechanism. From the experimental data, we estimate the state transition probabilities and correlated random walk parameters as a function of nearest neighbor distance. With the individual-level model established, we assess whether it reproduces the macroscopic patterns of movement at the group level. To do so, we consider three distributions, namely distance to nearest neighbor, angle to nearest neighbor, and percentage of population moving at any given time. For each of these three distributions, we compare our experimental data to the output of numerical simulations of our nearest neighbor model, and of a control model in which aphids do not interact socially. Our stochastic, social nearest neighbor model reproduces salient features of the experimental data that are not captured by the control.

  14. Correlation length estimation in a polycrystalline material model

    International Nuclear Information System (INIS)

    Simonovski, I.; Cizelj, L.

    2005-01-01

    This paper deals with the correlation length estimated from a mesoscopic model of a polycrystalline material. The correlation length can be used in some macroscopic material models as a material parameter that describes the internal length. It can be estimated directly from the strain and stress fields calculated from a finite-element model, which explicitly accounts for the selected mesoscopic features such as the random orientation, shape and size of the grains. A crystal plasticity material model was applied in the finite-element analysis. Different correlation lengths were obtained depending on the used set of crystallographic orientations. We determined that the different sets of crystallographic orientations affect the general level of the correlation length, however, as the external load is increased the behaviour of correlation length is similar in all the analyzed cases. The correlation lengths also changed with the macroscopic load. If the load is below the yield strength the correlation lengths are constant, and are slightly higher than the average grain size. The correlation length can therefore be considered as an indicator of first plastic deformations in the material. Increasing the load above the yield strength creates shear bands that temporarily increase the values of the correlation lengths calculated from the strain fields. With a further load increase the correlation lengths decrease slightly but stay above the average grain size. (author)

  15. A Correlated Random Effects Model for Non-homogeneous Markov Processes with Nonignorable Missingness.

    Science.gov (United States)

    Chen, Baojiang; Zhou, Xiao-Hua

    2013-05-01

    Life history data arising in clusters with prespecified assessment time points for patients often feature incomplete data since patients may choose to visit the clinic based on their needs. Markov process models provide a useful tool describing disease progression for life history data. The literature mainly focuses on time homogeneous process. In this paper we develop methods to deal with non-homogeneous Markov process with incomplete clustered life history data. A correlated random effects model is developed to deal with the nonignorable missingness, and a time transformation is employed to address the non-homogeneity in the transition model. Maximum likelihood estimate based on the Monte-Carlo EM algorithm is advocated for parameter estimation. Simulation studies demonstrate that the proposed method works well in many situations. We also apply this method to an Alzheimer's disease study.

  16. Bayesian analysis for exponential random graph models using the adaptive exchange sampler

    KAUST Repository

    Jin, Ick Hoon

    2013-01-01

    Exponential random graph models have been widely used in social network analysis. However, these models are extremely difficult to handle from a statistical viewpoint, because of the existence of intractable normalizing constants. In this paper, we consider a fully Bayesian analysis for exponential random graph models using the adaptive exchange sampler, which solves the issue of intractable normalizing constants encountered in Markov chain Monte Carlo (MCMC) simulations. The adaptive exchange sampler can be viewed as a MCMC extension of the exchange algorithm, and it generates auxiliary networks via an importance sampling procedure from an auxiliary Markov chain running in parallel. The convergence of this algorithm is established under mild conditions. The adaptive exchange sampler is illustrated using a few social networks, including the Florentine business network, molecule synthetic network, and dolphins network. The results indicate that the adaptive exchange algorithm can produce more accurate estimates than approximate exchange algorithms, while maintaining the same computational efficiency.

  17. A model of distributed phase aberration for deblurring phase estimated from scattering.

    Science.gov (United States)

    Tillett, Jason C; Astheimer, Jeffrey P; Waag, Robert C

    2010-01-01

    Correction of aberration in ultrasound imaging uses the response of a point reflector or its equivalent to characterize the aberration. Because a point reflector is usually unavailable, its equivalent is obtained using statistical methods, such as processing reflections from multiple focal regions in a random medium. However, the validity of methods that use reflections from multiple points is limited to isoplanatic patches for which the aberration is essentially the same. In this study, aberration is modeled by an offset phase screen to relax the isoplanatic restriction. Methods are developed to determine the depth and phase of the screen and to use the model for compensation of aberration as the beam is steered. Use of the model to enhance the performance of the noted statistical estimation procedure is also described. Experimental results obtained with tissue-mimicking phantoms that implement different models and produce different amounts of aberration are presented to show the efficacy of these methods. The improvement in b-scan resolution realized with the model is illustrated. The results show that the isoplanatic patch assumption for estimation of aberration can be relaxed and that propagation-path characteristics and aberration estimation are closely related.

  18. Genetic analysis of partial egg production records in Japanese quail using random regression models.

    Science.gov (United States)

    Abou Khadiga, G; Mahmoud, B Y F; Farahat, G S; Emam, A M; El-Full, E A

    2017-08-01

    The main objectives of this study were to detect the most appropriate random regression model (RRM) to fit the data of monthly egg production in 2 lines (selected and control) of Japanese quail and to test the consistency of different criteria of model choice. Data from 1,200 female Japanese quails for the first 5 months of egg production from 4 consecutive generations of an egg line selected for egg production in the first month (EP1) was analyzed. Eight RRMs with different orders of Legendre polynomials were compared to determine the proper model for analysis. All criteria of model choice suggested that the adequate model included the second-order Legendre polynomials for fixed effects, and the third-order for additive genetic effects and permanent environmental effects. Predictive ability of the best model was the highest among all models (ρ = 0.987). According to the best model fitted to the data, estimates of heritability were relatively low to moderate (0.10 to 0.17) showed a descending pattern from the first to the fifth month of production. A similar pattern was observed for permanent environmental effects with greater estimates in the first (0.36) and second (0.23) months of production than heritability estimates. Genetic correlations between separate production periods were higher (0.18 to 0.93) than their phenotypic counterparts (0.15 to 0.87). The superiority of the selected line over the control was observed through significant (P egg production in earlier ages (first and second months) than later ones. A methodology based on random regression animal models can be recommended for genetic evaluation of egg production in Japanese quail. © 2017 Poultry Science Association Inc.

  19. Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors

    International Nuclear Information System (INIS)

    Herschtal, A; Te Marvelde, L; Mengersen, K; Foroudi, F; Ball, D; Devereux, T; Pham, D; Greer, P B; Pichler, P; Eade, T; Kneebone, A; Bell, L; Caine, H; Hindson, B; Kron, T; Hosseinifard, Z

    2015-01-01

    Collected real-life clinical target volume (CTV) displacement data show that some patients undergoing external beam radiotherapy (EBRT) demonstrate significantly more fraction-to-fraction variability in their displacement (‘random error’) than others. This contrasts with the common assumption made by historical recipes for margin estimation for EBRT, that the random error is constant across patients. In this work we present statistical models of CTV displacements in which random errors are characterised by an inverse gamma (IG) distribution in order to assess the impact of random error variability on CTV-to-PTV margin widths, for eight real world patient cohorts from four institutions, and for different sites of malignancy. We considered a variety of clinical treatment requirements and penumbral widths. The eight cohorts consisted of a total of 874 patients and 27 391 treatment sessions. Compared to a traditional margin recipe that assumes constant random errors across patients, for a typical 4 mm penumbral width, the IG based margin model mandates that in order to satisfy the common clinical requirement that 90% of patients receive at least 95% of prescribed RT dose to the entire CTV, margins be increased by a median of 10% (range over the eight cohorts −19% to +35%). This substantially reduces the proportion of patients for whom margins are too small to satisfy clinical requirements. (paper)

  20. Comparison Between Two Methods for Estimating the Vertical Scale of Fluctuation for Modeling Random Geotechnical Problems

    Science.gov (United States)

    Pieczyńska-Kozłowska, Joanna M.

    2015-12-01

    The design process in geotechnical engineering requires the most accurate mapping of soil. The difficulty lies in the spatial variability of soil parameters, which has been a site of investigation of many researches for many years. This study analyses the soil-modeling problem by suggesting two effective methods of acquiring information for modeling that consists of variability from cone penetration test (CPT). The first method has been used in geotechnical engineering, but the second one has not been associated with geotechnics so far. Both methods are applied to a case study in which the parameters of changes are estimated. The knowledge of the variability of parameters allows in a long term more effective estimation, for example, bearing capacity probability of failure.

  1. A multilevel model for cardiovascular disease prevalence in the US and its application to micro area prevalence estimates

    Directory of Open Access Journals (Sweden)

    Congdon Peter

    2009-01-01

    Full Text Available Abstract Background Estimates of disease prevalence for small areas are increasingly required for the allocation of health funds according to local need. Both individual level and geographic risk factors are likely to be relevant to explaining prevalence variations, and in turn relevant to the procedure for small area prevalence estimation. Prevalence estimates are of particular importance for major chronic illnesses such as cardiovascular disease. Methods A multilevel prevalence model for cardiovascular outcomes is proposed that incorporates both survey information on patient risk factors and the effects of geographic location. The model is applied to derive micro area prevalence estimates, specifically estimates of cardiovascular disease for Zip Code Tabulation Areas in the USA. The model incorporates prevalence differentials by age, sex, ethnicity and educational attainment from the 2005 Behavioral Risk Factor Surveillance System survey. Influences of geographic context are modelled at both county and state level, with the county effects relating to poverty and urbanity. State level influences are modelled using a random effects approach that allows both for spatial correlation and spatial isolates. Results To assess the importance of geographic variables, three types of model are compared: a model with person level variables only; a model with geographic effects that do not interact with person attributes; and a full model, allowing for state level random effects that differ by ethnicity. There is clear evidence that geographic effects improve statistical fit. Conclusion Geographic variations in disease prevalence partly reflect the demographic composition of area populations. However, prevalence variations may also show distinct geographic 'contextual' effects. The present study demonstrates by formal modelling methods that improved explanation is obtained by allowing for distinct geographic effects (for counties and states and for

  2. Supplementary Appendix for: Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

    KAUST Repository

    Suliman, Mohamed Abdalla Elhag; Ballal, Tarig; Kammoun, Abla; Alnaffouri, Tareq Y.

    2016-01-01

    In this supplementary appendix we provide proofs and additional simulation results that complement the paper (constrained perturbation regularization approach for signal estimation using random matrix theory).

  3. An Exponential Tilt Mixture Model for Time-to-Event Data to Evaluate Treatment Effect Heterogeneity in Randomized Clinical Trials.

    Science.gov (United States)

    Wang, Chi; Tan, Zhiqiang; Louis, Thomas A

    2014-01-01

    Evaluating the effect of a treatment on a time-to-event outcome is the focus of many randomized clinical trials. It is often observed that the treatment effect is heterogeneous, where only a subgroup of the patients may respond to the treatment due to some unknown mechanism such as genetic polymorphism. In this paper, we propose a semiparametric exponential tilt mixture model to estimate the proportion of patients who respond to the treatment and to assess the treatment effect. Our model is a natural extension of parametric mixture models to a semiparametric setting with a time-to-event outcome. We propose a nonparametric maximum likelihood estimation approach for inference and establish related asymptotic properties. Our method is illustrated by a randomized clinical trial on biodegradable polymer-delivered chemotherapy for malignant gliomas patients.

  4. Surface tensor estimation from linear sections

    DEFF Research Database (Denmark)

    Kousholt, Astrid; Kiderlen, Markus; Hug, Daniel

    From Crofton's formula for Minkowski tensors we derive stereological estimators of translation invariant surface tensors of convex bodies in the n-dimensional Euclidean space. The estimators are based on one-dimensional linear sections. In a design based setting we suggest three types of estimators....... These are based on isotropic uniform random lines, vertical sections, and non-isotropic random lines, respectively. Further, we derive estimators of the specific surface tensors associated with a stationary process of convex particles in the model based setting....

  5. Surface tensor estimation from linear sections

    DEFF Research Database (Denmark)

    Kousholt, Astrid; Kiderlen, Markus; Hug, Daniel

    2015-01-01

    From Crofton’s formula for Minkowski tensors we derive stereological estimators of translation invariant surface tensors of convex bodies in the n-dimensional Euclidean space. The estimators are based on one-dimensional linear sections. In a design based setting we suggest three types of estimators....... These are based on isotropic uniform random lines, vertical sections, and non-isotropic random lines, respectively. Further, we derive estimators of the specific surface tensors associated with a stationary process of convex particles in the model based setting....

  6. Reduction of the number of parameters needed for a polynomial random regression test-day model

    NARCIS (Netherlands)

    Pool, M.H.; Meuwissen, T.H.E.

    2000-01-01

    Legendre polynomials were used to describe the (co)variance matrix within a random regression test day model. The goodness of fit depended on the polynomial order of fit, i.e., number of parameters to be estimated per animal but is limited by computing capacity. Two aspects: incomplete lactation

  7. Ordered random variables theory and applications

    CERN Document Server

    Shahbaz, Muhammad Qaiser; Hanif Shahbaz, Saman; Al-Zahrani, Bander M

    2016-01-01

    Ordered Random Variables have attracted several authors. The basic building block of Ordered Random Variables is Order Statistics which has several applications in extreme value theory and ordered estimation. The general model for ordered random variables, known as Generalized Order Statistics has been introduced relatively recently by Kamps (1995).

  8. Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

    OpenAIRE

    Chen, Junfei; Li, Ming; Wang, Weiguang

    2012-01-01

    Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI). We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF-) based forecast model has ...

  9. Application of random number generators in genetic algorithms to improve rainfall-runoff modelling

    Science.gov (United States)

    Chlumecký, Martin; Buchtele, Josef; Richta, Karel

    2017-10-01

    The efficient calibration of rainfall-runoff models is a difficult issue, even for experienced hydrologists. Therefore, fast and high-quality model calibration is a valuable improvement. This paper describes a novel methodology and software for the optimisation of a rainfall-runoff modelling using a genetic algorithm (GA) with a newly prepared concept of a random number generator (HRNG), which is the core of the optimisation. The GA estimates model parameters using evolutionary principles, which requires a quality number generator. The new HRNG generates random numbers based on hydrological information and it provides better numbers compared to pure software generators. The GA enhances the model calibration very well and the goal is to optimise the calibration of the model with a minimum of user interaction. This article focuses on improving the internal structure of the GA, which is shielded from the user. The results that we obtained indicate that the HRNG provides a stable trend in the output quality of the model, despite various configurations of the GA. In contrast to previous research, the HRNG speeds up the calibration of the model and offers an improvement of rainfall-runoff modelling.

  10. DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING

    Data.gov (United States)

    National Aeronautics and Space Administration — DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING SUBHASISH MOHANTY*, ADITI CHATTOPADHYAY, JOHN N. RAJADAS, AND CLYDE...

  11. Gray bootstrap method for estimating frequency-varying random vibration signals with small samples

    Directory of Open Access Journals (Sweden)

    Wang Yanqing

    2014-04-01

    Full Text Available During environment testing, the estimation of random vibration signals (RVS is an important technique for the airborne platform safety and reliability. However, the available methods including extreme value envelope method (EVEM, statistical tolerances method (STM and improved statistical tolerance method (ISTM require large samples and typical probability distribution. Moreover, the frequency-varying characteristic of RVS is usually not taken into account. Gray bootstrap method (GBM is proposed to solve the problem of estimating frequency-varying RVS with small samples. Firstly, the estimated indexes are obtained including the estimated interval, the estimated uncertainty, the estimated value, the estimated error and estimated reliability. In addition, GBM is applied to estimating the single flight testing of certain aircraft. At last, in order to evaluate the estimated performance, GBM is compared with bootstrap method (BM and gray method (GM in testing analysis. The result shows that GBM has superiority for estimating dynamic signals with small samples and estimated reliability is proved to be 100% at the given confidence level.

  12. Entropy Characterization of Random Network Models

    Directory of Open Access Journals (Sweden)

    Pedro J. Zufiria

    2017-06-01

    Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.

  13. Bayesian Modeling for Identification and Estimation of the Learning Effects of Pointing Tasks

    Science.gov (United States)

    Kyo, Koki

    Recently, in the field of human-computer interaction, a model containing the systematic factor and human factor has been proposed to evaluate the performance of the input devices of a computer. This is called the SH-model. In this paper, in order to extend the range of application of the SH-model, we propose some new models based on the Box-Cox transformation and apply a Bayesian modeling method for identification and estimation of the learning effects of pointing tasks. We consider the parameters describing the learning effect as random variables and introduce smoothness priors for them. Illustrative results show that the newly-proposed models work well.

  14. Fuel Burn Estimation Model

    Science.gov (United States)

    Chatterji, Gano

    2011-01-01

    Conclusions: Validated the fuel estimation procedure using flight test data. A good fuel model can be created if weight and fuel data are available. Error in assumed takeoff weight results in similar amount of error in the fuel estimate. Fuel estimation error bounds can be determined.

  15. Curvature of random walks and random polygons in confinement

    International Nuclear Information System (INIS)

    Diao, Y; Ernst, C; Montemayor, A; Ziegler, U

    2013-01-01

    The purpose of this paper is to study the curvature of equilateral random walks and polygons that are confined in a sphere. Curvature is one of several basic geometric properties that can be used to describe random walks and polygons. We show that confinement affects curvature quite strongly, and in the limit case where the confinement diameter equals the edge length the unconfined expected curvature value doubles from π/2 to π. To study curvature a simple model of an equilateral random walk in spherical confinement in dimensions 2 and 3 is introduced. For this simple model we derive explicit integral expressions for the expected value of the total curvature in both dimensions. These expressions are functions that depend only on the radius R of the confinement sphere. We then show that the values obtained by numeric integration of these expressions agrees with numerical average curvature estimates obtained from simulations of random walks. Finally, we compare the confinement effect on curvature of random walks with random polygons. (paper)

  16. A non-linear and stochastic response surface method for Bayesian estimation of uncertainty in soil moisture simulation from a land surface model

    Directory of Open Access Journals (Sweden)

    F. Hossain

    2004-01-01

    Full Text Available This study presents a simple and efficient scheme for Bayesian estimation of uncertainty in soil moisture simulation by a Land Surface Model (LSM. The scheme is assessed within a Monte Carlo (MC simulation framework based on the Generalized Likelihood Uncertainty Estimation (GLUE methodology. A primary limitation of using the GLUE method is the prohibitive computational burden imposed by uniform random sampling of the model's parameter distributions. Sampling is improved in the proposed scheme by stochastic modeling of the parameters' response surface that recognizes the non-linear deterministic behavior between soil moisture and land surface parameters. Uncertainty in soil moisture simulation (model output is approximated through a Hermite polynomial chaos expansion of normal random variables that represent the model's parameter (model input uncertainty. The unknown coefficients of the polynomial are calculated using limited number of model simulation runs. The calibrated polynomial is then used as a fast-running proxy to the slower-running LSM to predict the degree of representativeness of a randomly sampled model parameter set. An evaluation of the scheme's efficiency in sampling is made through comparison with the fully random MC sampling (the norm for GLUE and the nearest-neighborhood sampling technique. The scheme was able to reduce computational burden of random MC sampling for GLUE in the ranges of 10%-70%. The scheme was also found to be about 10% more efficient than the nearest-neighborhood sampling method in predicting a sampled parameter set's degree of representativeness. The GLUE based on the proposed sampling scheme did not alter the essential features of the uncertainty structure in soil moisture simulation. The scheme can potentially make GLUE uncertainty estimation for any LSM more efficient as it does not impose any additional structural or distributional assumptions.

  17. The influence of random element displacement on DOA estimates obtained with (Khatri-Rao-)root-MUSIC.

    Science.gov (United States)

    Inghelbrecht, Veronique; Verhaevert, Jo; van Hecke, Tanja; Rogier, Hendrik

    2014-11-11

    Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case.

  18. High-dimensional model estimation and model selection

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.

  19. Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models.

    Science.gov (United States)

    Selapa, N W; Nephawe, K A; Maiwashe, A; Norris, D

    2012-02-08

    The aim of this study was to estimate genetic parameters for body weights of individually fed beef bulls measured at centralized testing stations in South Africa using random regression models. Weekly body weights of Bonsmara bulls (N = 2919) tested between 1999 and 2003 were available for the analyses. The model included a fixed regression of the body weights on fourth-order orthogonal Legendre polynomials of the actual days on test (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84) for starting age and contemporary group effects. Random regressions on fourth-order orthogonal Legendre polynomials of the actual days on test were included for additive genetic effects and additional uncorrelated random effects of the weaning-herd-year and the permanent environment of the animal. Residual effects were assumed to be independently distributed with heterogeneous variance for each test day. Variance ratios for additive genetic, permanent environment and weaning-herd-year for weekly body weights at different test days ranged from 0.26 to 0.29, 0.37 to 0.44 and 0.26 to 0.34, respectively. The weaning-herd-year was found to have a significant effect on the variation of body weights of bulls despite a 28-day adjustment period. Genetic correlations amongst body weights at different test days were high, ranging from 0.89 to 1.00. Heritability estimates were comparable to literature using multivariate models. Therefore, random regression model could be applied in the genetic evaluation of body weight of individually fed beef bulls in South Africa.

  20. Bayesian hierarchical models for smoothing in two-phase studies, with application to small area estimation.

    Science.gov (United States)

    Ross, Michelle; Wakefield, Jon

    2015-10-01

    Two-phase study designs are appealing since they allow for the oversampling of rare sub-populations which improves efficiency. In this paper we describe a Bayesian hierarchical model for the analysis of two-phase data. Such a model is particularly appealing in a spatial setting in which random effects are introduced to model between-area variability. In such a situation, one may be interested in estimating regression coefficients or, in the context of small area estimation, in reconstructing the population totals by strata. The efficiency gains of the two-phase sampling scheme are compared to standard approaches using 2011 birth data from the research triangle area of North Carolina. We show that the proposed method can overcome small sample difficulties and improve on existing techniques. We conclude that the two-phase design is an attractive approach for small area estimation.

  1. Correlation between the model accuracy and model-based SOC estimation

    International Nuclear Information System (INIS)

    Wang, Qianqian; Wang, Jiao; Zhao, Pengju; Kang, Jianqiang; Yan, Few; Du, Changqing

    2017-01-01

    State-of-charge (SOC) estimation is a core technology for battery management systems. Considerable progress has been achieved in the study of SOC estimation algorithms, especially the algorithm on the basis of Kalman filter to meet the increasing demand of model-based battery management systems. The Kalman filter weakens the influence of white noise and initial error during SOC estimation but cannot eliminate the existing error of the battery model itself. As such, the accuracy of SOC estimation is directly related to the accuracy of the battery model. Thus far, the quantitative relationship between model accuracy and model-based SOC estimation remains unknown. This study summarizes three equivalent circuit lithium-ion battery models, namely, Thevenin, PNGV, and DP models. The model parameters are identified through hybrid pulse power characterization test. The three models are evaluated, and SOC estimation conducted by EKF-Ah method under three operating conditions are quantitatively studied. The regression and correlation of the standard deviation and normalized RMSE are studied and compared between the model error and the SOC estimation error. These parameters exhibit a strong linear relationship. Results indicate that the model accuracy affects the SOC estimation accuracy mainly in two ways: dispersion of the frequency distribution of the error and the overall level of the error. On the basis of the relationship between model error and SOC estimation error, our study provides a strategy for selecting a suitable cell model to meet the requirements of SOC precision using Kalman filter.

  2. Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models.

    Science.gov (United States)

    Martinussen, Torben; Vansteelandt, Stijn; Tchetgen Tchetgen, Eric J; Zucker, David M

    2017-12-01

    The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study. © 2017, The International Biometric Society.

  3. Estimating random transverse velocities in the fast solar wind from EISCAT Interplanetary Scintillation measurements

    Directory of Open Access Journals (Sweden)

    A. Canals

    2002-09-01

    Full Text Available Interplanetary scintillation measurements can yield estimates of a large number of solar wind parameters, including bulk flow speed, variation in bulk velocity along the observing path through the solar wind and random variation in transverse velocity. This last parameter is of particular interest, as it can indicate the flux of low-frequency Alfvén waves, and the dissipation of these waves has been proposed as an acceleration mechanism for the fast solar wind. Analysis of IPS data is, however, a significantly unresolved problem and a variety of a priori assumptions must be made in interpreting the data. Furthermore, the results may be affected by the physical structure of the radio source and by variations in the solar wind along the scintillation ray path. We have used observations of simple point-like radio sources made with EISCAT between 1994 and 1998 to obtain estimates of random transverse velocity in the fast solar wind. The results obtained with various a priori assumptions made in the analysis are compared, and we hope thereby to be able to provide some indication of the reliability of our estimates of random transverse velocity and the variation of this parameter with distance from the Sun.Key words. Interplanetary physics (MHD waves and turbulence; solar wind plasma; instruments and techniques

  4. Estimating random transverse velocities in the fast solar wind from EISCAT Interplanetary Scintillation measurements

    Directory of Open Access Journals (Sweden)

    A. Canals

    Full Text Available Interplanetary scintillation measurements can yield estimates of a large number of solar wind parameters, including bulk flow speed, variation in bulk velocity along the observing path through the solar wind and random variation in transverse velocity. This last parameter is of particular interest, as it can indicate the flux of low-frequency Alfvén waves, and the dissipation of these waves has been proposed as an acceleration mechanism for the fast solar wind. Analysis of IPS data is, however, a significantly unresolved problem and a variety of a priori assumptions must be made in interpreting the data. Furthermore, the results may be affected by the physical structure of the radio source and by variations in the solar wind along the scintillation ray path. We have used observations of simple point-like radio sources made with EISCAT between 1994 and 1998 to obtain estimates of random transverse velocity in the fast solar wind. The results obtained with various a priori assumptions made in the analysis are compared, and we hope thereby to be able to provide some indication of the reliability of our estimates of random transverse velocity and the variation of this parameter with distance from the Sun.

    Key words. Interplanetary physics (MHD waves and turbulence; solar wind plasma; instruments and techniques

  5. Random walk in degree space and the time-dependent Watts-Strogatz model

    Science.gov (United States)

    Casa Grande, H. L.; Cotacallapa, M.; Hase, M. O.

    2017-01-01

    In this work, we propose a scheme that provides an analytical estimate for the time-dependent degree distribution of some networks. This scheme maps the problem into a random walk in degree space, and then we choose the paths that are responsible for the dominant contributions. The method is illustrated on the dynamical versions of the Erdős-Rényi and Watts-Strogatz graphs, which were introduced as static models in the original formulation. We have succeeded in obtaining an analytical form for the dynamics Watts-Strogatz model, which is asymptotically exact for some regimes.

  6. Supplementary Material for: Tukey g-and-h Random Fields

    KAUST Repository

    Xu, Ganggang

    2016-01-01

    We propose a new class of transGaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of the maximum likelihood estimator and the probabilistic properties of the TGH random fields are investigated. An efficient estimation procedure, based on maximum approximated likelihood, is proposed and an extreme spatial outlier detection algorithm is formulated. Kriging and probabilistic prediction with TGH random fields are developed along with prediction confidence intervals. The predictive performance of TGH random fields is demonstrated through extensive simulation studies and an application to a dataset of total precipitation in the south east of the United States. Supplementary materials for this article are available online.

  7. A termination criterion for parameter estimation in stochastic models in systems biology.

    Science.gov (United States)

    Zimmer, Christoph; Sahle, Sven

    2015-11-01

    Parameter estimation procedures are a central aspect of modeling approaches in systems biology. They are often computationally expensive, especially when the models take stochasticity into account. Typically parameter estimation involves the iterative optimization of an objective function that describes how well the model fits some measured data with a certain set of parameter values. In order to limit the computational expenses it is therefore important to apply an adequate stopping criterion for the optimization process, so that the optimization continues at least until a reasonable fit is obtained, but not much longer. In the case of stochastic modeling, at least some parameter estimation schemes involve an objective function that is itself a random variable. This means that plain convergence tests are not a priori suitable as stopping criteria. This article suggests a termination criterion suited to optimization problems in parameter estimation arising from stochastic models in systems biology. The termination criterion is developed for optimization algorithms that involve populations of parameter sets, such as particle swarm or evolutionary algorithms. It is based on comparing the variance of the objective function over the whole population of parameter sets with the variance of repeated evaluations of the objective function at the best parameter set. The performance is demonstrated for several different algorithms. To test the termination criterion we choose polynomial test functions as well as systems biology models such as an Immigration-Death model and a bistable genetic toggle switch. The genetic toggle switch is an especially challenging test case as it shows a stochastic switching between two steady states which is qualitatively different from the model behavior in a deterministic model. Copyright © 2015. Published by Elsevier Ireland Ltd.

  8. APLIKASI SPLINE ESTIMATOR TERBOBOT

    Directory of Open Access Journals (Sweden)

    I Nyoman Budiantara

    2001-01-01

    Full Text Available We considered the nonparametric regression model : Zj = X(tj + ej, j = 1,2,…,n, where X(tj is the regression curve. The random error ej are independently distributed normal with a zero mean and a variance s2/bj, bj > 0. The estimation of X obtained by minimizing a Weighted Least Square. The solution of this optimation is a Weighted Spline Polynomial. Further, we give an application of weigted spline estimator in nonparametric regression. Abstract in Bahasa Indonesia : Diberikan model regresi nonparametrik : Zj = X(tj + ej, j = 1,2,…,n, dengan X (tj kurva regresi dan ej sesatan random yang diasumsikan berdistribusi normal dengan mean nol dan variansi s2/bj, bj > 0. Estimasi kurva regresi X yang meminimumkan suatu Penalized Least Square Terbobot, merupakan estimator Polinomial Spline Natural Terbobot. Selanjutnya diberikan suatu aplikasi estimator spline terbobot dalam regresi nonparametrik. Kata kunci: Spline terbobot, Regresi nonparametrik, Penalized Least Square.

  9. Uniform Estimate of the Finite-Time Ruin Probability for All Times in a Generalized Compound Renewal Risk Model

    Directory of Open Access Journals (Sweden)

    Qingwu Gao

    2012-01-01

    Full Text Available We discuss the uniformly asymptotic estimate of the finite-time ruin probability for all times in a generalized compound renewal risk model, where the interarrival times of successive accidents and all the claim sizes caused by an accident are two sequences of random variables following a wide dependence structure. This wide dependence structure allows random variables to be either negatively dependent or positively dependent.

  10. The random walk model of intrafraction movement

    International Nuclear Information System (INIS)

    Ballhausen, H; Reiner, M; Kantz, S; Belka, C; Söhn, M

    2013-01-01

    The purpose of this paper is to understand intrafraction movement as a stochastic process driven by random external forces. The hypothetically proposed three-dimensional random walk model has significant impact on optimal PTV margins and offers a quantitatively correct explanation of experimental findings. Properties of the random walk are calculated from first principles, in particular fraction-average population density distributions for displacements along the principal axes. When substituted into the established optimal margin recipes these fraction-average distributions yield safety margins about 30% smaller as compared to the suggested values from end-of-fraction Gaussian fits. Stylized facts of a random walk are identified in clinical data, such as the increase of the standard deviation of displacements with the square root of time. Least squares errors in the comparison to experimental results are reduced by about 50% when accounting for non-Gaussian corrections from the random walk model. (paper)

  11. The random walk model of intrafraction movement.

    Science.gov (United States)

    Ballhausen, H; Reiner, M; Kantz, S; Belka, C; Söhn, M

    2013-04-07

    The purpose of this paper is to understand intrafraction movement as a stochastic process driven by random external forces. The hypothetically proposed three-dimensional random walk model has significant impact on optimal PTV margins and offers a quantitatively correct explanation of experimental findings. Properties of the random walk are calculated from first principles, in particular fraction-average population density distributions for displacements along the principal axes. When substituted into the established optimal margin recipes these fraction-average distributions yield safety margins about 30% smaller as compared to the suggested values from end-of-fraction gaussian fits. Stylized facts of a random walk are identified in clinical data, such as the increase of the standard deviation of displacements with the square root of time. Least squares errors in the comparison to experimental results are reduced by about 50% when accounting for non-gaussian corrections from the random walk model.

  12. Genetic analyses of partial egg production in Japanese quail using multi-trait random regression models.

    Science.gov (United States)

    Karami, K; Zerehdaran, S; Barzanooni, B; Lotfi, E

    2017-12-01

    1. The aim of the present study was to estimate genetic parameters for average egg weight (EW) and egg number (EN) at different ages in Japanese quail using multi-trait random regression (MTRR) models. 2. A total of 8534 records from 900 quail, hatched between 2014 and 2015, were used in the study. Average weekly egg weights and egg numbers were measured from second until sixth week of egg production. 3. Nine random regression models were compared to identify the best order of the Legendre polynomials (LP). The most optimal model was identified by the Bayesian Information Criterion. A model with second order of LP for fixed effects, second order of LP for additive genetic effects and third order of LP for permanent environmental effects (MTRR23) was found to be the best. 4. According to the MTRR23 model, direct heritability for EW increased from 0.26 in the second week to 0.53 in the sixth week of egg production, whereas the ratio of permanent environment to phenotypic variance decreased from 0.48 to 0.1. Direct heritability for EN was low, whereas the ratio of permanent environment to phenotypic variance decreased from 0.57 to 0.15 during the production period. 5. For each trait, estimated genetic correlations among weeks of egg production were high (from 0.85 to 0.98). Genetic correlations between EW and EN were low and negative for the first two weeks, but they were low and positive for the rest of the egg production period. 6. In conclusion, random regression models can be used effectively for analysing egg production traits in Japanese quail. Response to selection for increased egg weight would be higher at older ages because of its higher heritability and such a breeding program would have no negative genetic impact on egg production.

  13. More Precise Estimation of Lower-Level Interaction Effects in Multilevel Models.

    Science.gov (United States)

    Loeys, Tom; Josephy, Haeike; Dewitte, Marieke

    2018-01-01

    In hierarchical data, the effect of a lower-level predictor on a lower-level outcome may often be confounded by an (un)measured upper-level factor. When such confounding is left unaddressed, the effect of the lower-level predictor is estimated with bias. Separating this effect into a within- and between-component removes such bias in a linear random intercept model under a specific set of assumptions for the confounder. When the effect of the lower-level predictor is additionally moderated by another lower-level predictor, an interaction between both lower-level predictors is included into the model. To address unmeasured upper-level confounding, this interaction term ought to be decomposed into a within- and between-component as well. This can be achieved by first multiplying both predictors and centering that product term next, or vice versa. We show that while both approaches, on average, yield the same estimates of the interaction effect in linear models, the former decomposition is much more precise and robust against misspecification of the effects of cross-level and upper-level terms, compared to the latter.

  14. Modeling individual differences in randomized experiments using growth models: Recommendations for design, statistical analysis and reporting of results of internet interventions

    Directory of Open Access Journals (Sweden)

    Hugo Hesser

    2015-05-01

    Full Text Available Growth models (also known as linear mixed effects models, multilevel models, and random coefficients models have the capability of studying change at the group as well as the individual level. In addition, these methods have documented advantages over traditional data analytic approaches in the analysis of repeated-measures data. These advantages include, but are not limited to, the ability to incorporate time-varying predictors, handle dependence among repeated observations in a very flexible manner, and to provide accurate estimates with missing data under fairly unrestrictive missing data assumptions. The flexibility of the growth curve modeling approach to the analysis of change makes it the preferred choice in the evaluation of direct, indirect and moderated intervention effects. Although offering many benefits, growth models present challenges in terms of design, analysis and reporting of results. This paper provides a nontechnical overview of growth models in the analysis of change in randomized experiments and advocates for their use in the field of internet interventions. Practical recommendations for design, analysis and reporting of results from growth models are provided.

  15. Probability Theory Plus Noise: Descriptive Estimation and Inferential Judgment.

    Science.gov (United States)

    Costello, Fintan; Watts, Paul

    2018-01-01

    We describe a computational model of two central aspects of people's probabilistic reasoning: descriptive probability estimation and inferential probability judgment. This model assumes that people's reasoning follows standard frequentist probability theory, but it is subject to random noise. This random noise has a regressive effect in descriptive probability estimation, moving probability estimates away from normative probabilities and toward the center of the probability scale. This random noise has an anti-regressive effect in inferential judgement, however. These regressive and anti-regressive effects explain various reliable and systematic biases seen in people's descriptive probability estimation and inferential probability judgment. This model predicts that these contrary effects will tend to cancel out in tasks that involve both descriptive estimation and inferential judgement, leading to unbiased responses in those tasks. We test this model by applying it to one such task, described by Gallistel et al. ). Participants' median responses in this task were unbiased, agreeing with normative probability theory over the full range of responses. Our model captures the pattern of unbiased responses in this task, while simultaneously explaining systematic biases away from normatively correct probabilities seen in other tasks. Copyright © 2018 Cognitive Science Society, Inc.

  16. A random regret minimization model of travel choice

    NARCIS (Netherlands)

    Chorus, C.G.; Arentze, T.A.; Timmermans, H.J.P.

    2008-01-01

    Abstract This paper presents an alternative to Random Utility-Maximization models of travel choice. Our Random Regret-Minimization model is rooted in Regret Theory and provides several useful features for travel demand analysis. Firstly, it allows for the possibility that choices between travel

  17. Kalman filter data assimilation: targeting observations and parameter estimation.

    Science.gov (United States)

    Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex

    2014-06-01

    This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

  18. Kalman filter data assimilation: Targeting observations and parameter estimation

    International Nuclear Information System (INIS)

    Bellsky, Thomas; Kostelich, Eric J.; Mahalov, Alex

    2014-01-01

    This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation

  19. Random matrix models for phase diagrams

    International Nuclear Information System (INIS)

    Vanderheyden, B; Jackson, A D

    2011-01-01

    We describe a random matrix approach that can provide generic and readily soluble mean-field descriptions of the phase diagram for a variety of systems ranging from quantum chromodynamics to high-T c materials. Instead of working from specific models, phase diagrams are constructed by averaging over the ensemble of theories that possesses the relevant symmetries of the problem. Although approximate in nature, this approach has a number of advantages. First, it can be useful in distinguishing generic features from model-dependent details. Second, it can help in understanding the 'minimal' number of symmetry constraints required to reproduce specific phase structures. Third, the robustness of predictions can be checked with respect to variations in the detailed description of the interactions. Finally, near critical points, random matrix models bear strong similarities to Ginsburg-Landau theories with the advantage of additional constraints inherited from the symmetries of the underlying interaction. These constraints can be helpful in ruling out certain topologies in the phase diagram. In this Key Issues Review, we illustrate the basic structure of random matrix models, discuss their strengths and weaknesses, and consider the kinds of system to which they can be applied.

  20. Creating, generating and comparing random network models with NetworkRandomizer.

    Science.gov (United States)

    Tosadori, Gabriele; Bestvina, Ivan; Spoto, Fausto; Laudanna, Carlo; Scardoni, Giovanni

    2016-01-01

    Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

  1. Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast

    Directory of Open Access Journals (Sweden)

    Junfei Chen

    2012-01-01

    Full Text Available Drought is part of natural climate variability and ranks the first natural disaster in the world. Drought forecasting plays an important role in mitigating impacts on agriculture and water resources. In this study, a drought forecast model based on the random forest method is proposed to predict the time series of monthly standardized precipitation index (SPI. We demonstrate model application by four stations in the Haihe river basin, China. The random-forest- (RF- based forecast model has consistently shown better predictive skills than the ARIMA model for both long and short drought forecasting. The confidence intervals derived from the proposed model generally have good coverage, but still tend to be conservative to predict some extreme drought events.

  2. Random walk in degree space and the time-dependent Watts-Strogatz model

    OpenAIRE

    Grande, H. L. Casa; Cotacallapa, M.; Hase, M. O.

    2016-01-01

    In this work, we propose a scheme that provides an analytical estimate for the time-dependent degree distribution of some networks. This scheme maps the problem into a random walk in degree space, and then we choose the paths that are responsible for the dominant contributions. The method is illustrated on the dynamical versions of the Erd\\"os-R\\'enyi and Watts-Strogatz graphs, which were introduced as static models in the original formulation. We have succeeded in obtaining an analytical for...

  3. Performance of models for estimating absolute risk difference in multicenter trials with binary outcome

    Directory of Open Access Journals (Sweden)

    Claudia Pedroza

    2016-08-01

    Full Text Available Abstract Background Reporting of absolute risk difference (RD is recommended for clinical and epidemiological prospective studies. In analyses of multicenter studies, adjustment for center is necessary when randomization is stratified by center or when there is large variation in patients outcomes across centers. While regression methods are used to estimate RD adjusted for baseline predictors and clustering, no formal evaluation of their performance has been previously conducted. Methods We performed a simulation study to evaluate 6 regression methods fitted under a generalized estimating equation framework: binomial identity, Poisson identity, Normal identity, log binomial, log Poisson, and logistic regression model. We compared the model estimates to unadjusted estimates. We varied the true response function (identity or log, number of subjects per center, true risk difference, control outcome rate, effect of baseline predictor, and intracenter correlation. We compared the models in terms of convergence, absolute bias and coverage of 95 % confidence intervals for RD. Results The 6 models performed very similar to each other for the majority of scenarios. However, the log binomial model did not converge for a large portion of the scenarios including a baseline predictor. In scenarios with outcome rate close to the parameter boundary, the binomial and Poisson identity models had the best performance, but differences from other models were negligible. The unadjusted method introduced little bias to the RD estimates, but its coverage was larger than the nominal value in some scenarios with an identity response. Under the log response, coverage from the unadjusted method was well below the nominal value (<80 % for some scenarios. Conclusions We recommend the use of a binomial or Poisson GEE model with identity link to estimate RD for correlated binary outcome data. If these models fail to run, then either a logistic regression, log Poisson

  4. Unbiased stereological estimation of d-dimensional volume in Rn from an isotropic random slice through a fixed point

    DEFF Research Database (Denmark)

    Jensen, Eva B. Vedel; Kiêu, K

    1994-01-01

    Unbiased stereological estimators of d-dimensional volume in R(n) are derived, based on information from an isotropic random r-slice through a specified point. The content of the slice can be subsampled by means of a spatial grid. The estimators depend only on spatial distances. As a fundamental ...... lemma, an explicit formula for the probability that an isotropic random r-slice in R(n) through 0 hits a fixed point in R(n) is given....

  5. A random spatial network model based on elementary postulates

    Science.gov (United States)

    Karlinger, Michael R.; Troutman, Brent M.

    1989-01-01

    A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

  6. UNBIASED ESTIMATORS OF SPECIFIC CONNECTIVITY

    Directory of Open Access Journals (Sweden)

    Jean-Paul Jernot

    2011-05-01

    Full Text Available This paper deals with the estimation of the specific connectivity of a stationary random set in IRd. It turns out that the "natural" estimator is only asymptotically unbiased. The example of a boolean model of hypercubes illustrates the amplitude of the bias produced when the measurement field is relatively small with respect to the range of the random set. For that reason unbiased estimators are desired. Such an estimator can be found in the literature in the case where the measurement field is a right parallelotope. In this paper, this estimator is extended to apply to measurement fields of various shapes, and to possess a smaller variance. Finally an example from quantitative metallography (specific connectivity of a population of sintered bronze particles is given.

  7. Studies in astronomical time series analysis. IV - Modeling chaotic and random processes with linear filters

    Science.gov (United States)

    Scargle, Jeffrey D.

    1990-01-01

    While chaos arises only in nonlinear systems, standard linear time series models are nevertheless useful for analyzing data from chaotic processes. This paper introduces such a model, the chaotic moving average. This time-domain model is based on the theorem that any chaotic process can be represented as the convolution of a linear filter with an uncorrelated process called the chaotic innovation. A technique, minimum phase-volume deconvolution, is introduced to estimate the filter and innovation. The algorithm measures the quality of a model using the volume covered by the phase-portrait of the innovation process. Experiments on synthetic data demonstrate that the algorithm accurately recovers the parameters of simple chaotic processes. Though tailored for chaos, the algorithm can detect both chaos and randomness, distinguish them from each other, and separate them if both are present. It can also recover nonminimum-delay pulse shapes in non-Gaussian processes, both random and chaotic.

  8. Comparative study of random and uniform models for the distribution of TRISO particles in HTR-10 fuel elements

    International Nuclear Information System (INIS)

    Rosales, J.; Perez, J.; Garcia, C.; Munnoz, A.; Lira, C. A. B. O.

    2015-01-01

    TRISO particles are the specific features of HTR-10 and generally HTGR reactors. Their heterogeneity and random arrangement in graphite matrix of these reactors create a significant modeling challenge. In the simulation of spherical fuel elements using MCNPX are usually created repetitive structures using uniform distribution models. The use of these repetitive structures introduces two major approaches: the non-randomness of the TRISO particles inside the pebbles and the intersection of the pebble surface with the TRISO particles. These approaches could affect significantly the multiplicative properties of the core. In order to study the influence of these approaches in the multiplicative properties was estimated the K inf value in one pebble with white boundary conditions using 4 different configurations regarding the distribution of the TRISO particles inside the pebble: uniform hexagonal model, cubic uniform model, cubic uniform without the effect of cutting and a random distribution model. It was studied the impact these models on core scale solving the problem B1, from the Benchmark Problems presented in a Coordinated Research Program of the IAEA. (Author)

  9. A Generalized Random Regret Minimization Model

    NARCIS (Netherlands)

    Chorus, C.G.

    2013-01-01

    This paper presents, discusses and tests a generalized Random Regret Minimization (G-RRM) model. The G-RRM model is created by replacing a fixed constant in the attribute-specific regret functions of the RRM model, by a regret-weight variable. Depending on the value of the regret-weights, the G-RRM

  10. Simulation of a directed random-walk model: the effect of pseudo-random-number correlations

    OpenAIRE

    Shchur, L. N.; Heringa, J. R.; Blöte, H. W. J.

    1996-01-01

    We investigate the mechanism that leads to systematic deviations in cluster Monte Carlo simulations when correlated pseudo-random numbers are used. We present a simple model, which enables an analysis of the effects due to correlations in several types of pseudo-random-number sequences. This model provides qualitative understanding of the bias mechanism in a class of cluster Monte Carlo algorithms.

  11. A Logistic Regression Model with a Hierarchical Random Error Term for Analyzing the Utilization of Public Transport

    Directory of Open Access Journals (Sweden)

    Chong Wei

    2015-01-01

    Full Text Available Logistic regression models have been widely used in previous studies to analyze public transport utilization. These studies have shown travel time to be an indispensable variable for such analysis and usually consider it to be a deterministic variable. This formulation does not allow us to capture travelers’ perception error regarding travel time, and recent studies have indicated that this error can have a significant effect on modal choice behavior. In this study, we propose a logistic regression model with a hierarchical random error term. The proposed model adds a new random error term for the travel time variable. This term structure enables us to investigate travelers’ perception error regarding travel time from a given choice behavior dataset. We also propose an extended model that allows constraining the sign of this error in the model. We develop two Gibbs samplers to estimate the basic hierarchical model and the extended model. The performance of the proposed models is examined using a well-known dataset.

  12. Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples

    DEFF Research Database (Denmark)

    Thorson, James T.; Kristensen, Kasper

    2016-01-01

    Statistical models play an important role in fisheries science when reconciling ecological theory with available data for wild populations or experimental studies. Ecological models increasingly include both fixed and random effects, and are often estimated using maximum likelihood techniques...... configurations of an age-structured population dynamics model. This simulation experiment shows that the epsilon-method and the existing bias-correction method perform equally well in data-rich contexts, but the epsilon-method is slightly less biased in data-poor contexts. We then apply the epsilon......-method to a spatial regression model when estimating an index of population abundance, and compare results with an alternative bias-correction algorithm that involves Markov-chain Monte Carlo sampling. This example shows that the epsilon-method leads to a biologically significant difference in estimates of average...

  13. A METHOD TO ESTIMATE TEMPORAL INTERACTION IN A CONDITIONAL RANDOM FIELD BASED APPROACH FOR CROP RECOGNITION

    Directory of Open Access Journals (Sweden)

    P. M. A. Diaz

    2016-06-01

    Full Text Available This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at two consecutive epochs. In the proposed method, the estimation of temporal interaction parameters is considered as an optimization problem, whose goal is to find the transition matrix that maximizes the CRF performance, upon a set of labelled data. The objective functions underlying the optimization procedure can be formulated in terms of different accuracy metrics, such as overall and average class accuracy per crop or phenological stages. To validate the proposed approach, experiments were carried out upon a dataset consisting of 12 co-registered LANDSAT images of a region in southeast of Brazil. Pattern Search was used as the optimization algorithm. The experimental results demonstrated that the proposed method was able to substantially outperform estimates related to joint or conditional class transition probabilities, which rely on training samples.

  14. Genetic parameters for quail body weights using a random ...

    African Journals Online (AJOL)

    A model including fixed and random linear regressions is described for analyzing body weights at different ages. In this study, (co)variance components, heritabilities for quail weekly weights and genetic correlations among these weights were estimated using a random regression model by DFREML under DXMRR option.

  15. Factoring vs linear modeling in rate estimation: a simulation study of relative accuracy.

    Science.gov (United States)

    Maldonado, G; Greenland, S

    1998-07-01

    A common strategy for modeling dose-response in epidemiology is to transform ordered exposures and covariates into sets of dichotomous indicator variables (that is, to factor the variables). Factoring tends to increase estimation variance, but it also tends to decrease bias and thus may increase or decrease total accuracy. We conducted a simulation study to examine the impact of factoring on the accuracy of rate estimation. Factored and unfactored Poisson regression models were fit to follow-up study datasets that were randomly generated from 37,500 population model forms that ranged from subadditive to supramultiplicative. In the situations we examined, factoring sometimes substantially improved accuracy relative to fitting the corresponding unfactored model, sometimes substantially decreased accuracy, and sometimes made little difference. The difference in accuracy between factored and unfactored models depended in a complicated fashion on the difference between the true and fitted model forms, the strength of exposure and covariate effects in the population, and the study size. It may be difficult in practice to predict when factoring is increasing or decreasing accuracy. We recommend, therefore, that the strategy of factoring variables be supplemented with other strategies for modeling dose-response.

  16. Gaussian random bridges and a geometric model for information equilibrium

    Science.gov (United States)

    Mengütürk, Levent Ali

    2018-03-01

    The paper introduces a class of conditioned stochastic processes that we call Gaussian random bridges (GRBs) and proves some of their properties. Due to the anticipative representation of any GRB as the sum of a random variable and a Gaussian (T , 0) -bridge, GRBs can model noisy information processes in partially observed systems. In this spirit, we propose an asset pricing model with respect to what we call information equilibrium in a market with multiple sources of information. The idea is to work on a topological manifold endowed with a metric that enables us to systematically determine an equilibrium point of a stochastic system that can be represented by multiple points on that manifold at each fixed time. In doing so, we formulate GRB-based information diversity over a Riemannian manifold and show that it is pinned to zero over the boundary determined by Dirac measures. We then define an influence factor that controls the dominance of an information source in determining the best estimate of a signal in the L2-sense. When there are two sources, this allows us to construct information equilibrium as a functional of a geodesic-valued stochastic process, which is driven by an equilibrium convergence rate representing the signal-to-noise ratio. This leads us to derive price dynamics under what can be considered as an equilibrium probability measure. We also provide a semimartingale representation of Markovian GRBs associated with Gaussian martingales and a non-anticipative representation of fractional Brownian random bridges that can incorporate degrees of information coupling in a given system via the Hurst exponent.

  17. Black holes and random matrices

    Energy Technology Data Exchange (ETDEWEB)

    Cotler, Jordan S.; Gur-Ari, Guy [Stanford Institute for Theoretical Physics, Stanford University,Stanford, CA 94305 (United States); Hanada, Masanori [Stanford Institute for Theoretical Physics, Stanford University,Stanford, CA 94305 (United States); Yukawa Institute for Theoretical Physics, Kyoto University,Kyoto 606-8502 (Japan); The Hakubi Center for Advanced Research, Kyoto University,Kyoto 606-8502 (Japan); Polchinski, Joseph [Department of Physics, University of California,Santa Barbara, CA 93106 (United States); Kavli Institute for Theoretical Physics, University of California,Santa Barbara, CA 93106 (United States); Saad, Phil; Shenker, Stephen H. [Stanford Institute for Theoretical Physics, Stanford University,Stanford, CA 94305 (United States); Stanford, Douglas [Institute for Advanced Study,Princeton, NJ 08540 (United States); Streicher, Alexandre [Stanford Institute for Theoretical Physics, Stanford University,Stanford, CA 94305 (United States); Department of Physics, University of California,Santa Barbara, CA 93106 (United States); Tezuka, Masaki [Department of Physics, Kyoto University,Kyoto 606-8501 (Japan)

    2017-05-22

    We argue that the late time behavior of horizon fluctuations in large anti-de Sitter (AdS) black holes is governed by the random matrix dynamics characteristic of quantum chaotic systems. Our main tool is the Sachdev-Ye-Kitaev (SYK) model, which we use as a simple model of a black hole. We use an analytically continued partition function |Z(β+it)|{sup 2} as well as correlation functions as diagnostics. Using numerical techniques we establish random matrix behavior at late times. We determine the early time behavior exactly in a double scaling limit, giving us a plausible estimate for the crossover time to random matrix behavior. We use these ideas to formulate a conjecture about general large AdS black holes, like those dual to 4D super-Yang-Mills theory, giving a provisional estimate of the crossover time. We make some preliminary comments about challenges to understanding the late time dynamics from a bulk point of view.

  18. Event-Triggered Fault Estimation for Stochastic Systems over Multi-Hop Relay Networks with Randomly Occurring Sensor Nonlinearities and Packet Dropouts.

    Science.gov (United States)

    Li, Yunji; Peng, Li

    2018-02-28

    Wireless sensors have many new applications where remote estimation is essential. Considering that a remote estimator is located far away from the process and the wireless transmission distance of sensor nodes is limited, sensor nodes always forward data packets to the remote estimator through a series of relays over a multi-hop link. In this paper, we consider a network with sensor nodes and relay nodes where the relay nodes can forward the estimated values to the remote estimator. An event-triggered remote estimator of state and fault with the corresponding data-forwarding scheme is investigated for stochastic systems subject to both randomly occurring nonlinearity and randomly occurring packet dropouts governed by Bernoulli-distributed sequences to achieve a trade-off between estimation accuracy and energy consumption. Recursive Riccati-like matrix equations are established to calculate the estimator gain to minimize an upper bound of the estimator error covariance. Subsequently, a sufficient condition and data-forwarding scheme are presented under which the error covariance is mean-square bounded in the multi-hop links with random packet dropouts. Furthermore, implementation issues of the theoretical results are discussed where a new data-forwarding communication protocol is designed. Finally, the effectiveness of the proposed algorithms and communication protocol are extensively evaluated using an experimental platform that was established for performance evaluation with a sensor and two relay nodes.

  19. The random field Blume-Capel model revisited

    Science.gov (United States)

    Santos, P. V.; da Costa, F. A.; de Araújo, J. M.

    2018-04-01

    We have revisited the mean-field treatment for the Blume-Capel model under the presence of a discrete random magnetic field as introduced by Kaufman and Kanner (1990). The magnetic field (H) versus temperature (T) phase diagrams for given values of the crystal field D were recovered in accordance to Kaufman and Kanner original work. However, our main goal in the present work was to investigate the distinct structures of the crystal field versus temperature phase diagrams as the random magnetic field is varied because similar models have presented reentrant phenomenon due to randomness. Following previous works we have classified the distinct phase diagrams according to five different topologies. The topological structure of the phase diagrams is maintained for both H - T and D - T cases. Although the phase diagrams exhibit a richness of multicritical phenomena we did not found any reentrant effect as have been seen in similar models.

  20. Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

    Science.gov (United States)

    Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

    2012-12-01

    This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

  1. ON THE ESTIMATION OF RANDOM UNCERTAINTIES OF STAR FORMATION HISTORIES

    Energy Technology Data Exchange (ETDEWEB)

    Dolphin, Andrew E., E-mail: adolphin@raytheon.com [Raytheon Company, Tucson, AZ, 85734 (United States)

    2013-09-20

    The standard technique for measurement of random uncertainties of star formation histories (SFHs) is the bootstrap Monte Carlo, in which the color-magnitude diagram (CMD) is repeatedly resampled. The variation in SFHs measured from the resampled CMDs is assumed to represent the random uncertainty in the SFH measured from the original data. However, this technique systematically and significantly underestimates the uncertainties for times in which the measured star formation rate is low or zero, leading to overly (and incorrectly) high confidence in that measurement. This study proposes an alternative technique, the Markov Chain Monte Carlo (MCMC), which samples the probability distribution of the parameters used in the original solution to directly estimate confidence intervals. While the most commonly used MCMC algorithms are incapable of adequately sampling a probability distribution that can involve thousands of highly correlated dimensions, the Hybrid Monte Carlo algorithm is shown to be extremely effective and efficient for this particular task. Several implementation details, such as the handling of implicit priors created by parameterization of the SFH, are discussed in detail.

  2. ON THE ESTIMATION OF RANDOM UNCERTAINTIES OF STAR FORMATION HISTORIES

    International Nuclear Information System (INIS)

    Dolphin, Andrew E.

    2013-01-01

    The standard technique for measurement of random uncertainties of star formation histories (SFHs) is the bootstrap Monte Carlo, in which the color-magnitude diagram (CMD) is repeatedly resampled. The variation in SFHs measured from the resampled CMDs is assumed to represent the random uncertainty in the SFH measured from the original data. However, this technique systematically and significantly underestimates the uncertainties for times in which the measured star formation rate is low or zero, leading to overly (and incorrectly) high confidence in that measurement. This study proposes an alternative technique, the Markov Chain Monte Carlo (MCMC), which samples the probability distribution of the parameters used in the original solution to directly estimate confidence intervals. While the most commonly used MCMC algorithms are incapable of adequately sampling a probability distribution that can involve thousands of highly correlated dimensions, the Hybrid Monte Carlo algorithm is shown to be extremely effective and efficient for this particular task. Several implementation details, such as the handling of implicit priors created by parameterization of the SFH, are discussed in detail

  3. MCMC estimation of multidimensional IRT models

    NARCIS (Netherlands)

    Beguin, Anton; Glas, Cornelis A.W.

    1998-01-01

    A Bayesian procedure to estimate the three-parameter normal ogive model and a generalization to a model with multidimensional ability parameters are discussed. The procedure is a generalization of a procedure by J. Albert (1992) for estimating the two-parameter normal ogive model. The procedure will

  4. Semi-analytical Model for Estimating Absorption Coefficients of Optically Active Constituents in Coastal Waters

    Science.gov (United States)

    Wang, D.; Cui, Y.

    2015-12-01

    The objectives of this paper are to validate the applicability of a multi-band quasi-analytical algorithm (QAA) in retrieval absorption coefficients of optically active constituents in turbid coastal waters, and to further improve the model using a proposed semi-analytical model (SAA). The ap(531) and ag(531) semi-analytically derived using SAA model are quite different from the retrievals procedures of QAA model that ap(531) and ag(531) are semi-analytically derived from the empirical retrievals results of a(531) and a(551). The two models are calibrated and evaluated against datasets taken from 19 independent cruises in West Florida Shelf in 1999-2003, provided by SeaBASS. The results indicate that the SAA model produces a superior performance to QAA model in absorption retrieval. Using of the SAA model in retrieving absorption coefficients of optically active constituents from West Florida Shelf decreases the random uncertainty of estimation by >23.05% from the QAA model. This study demonstrates the potential of the SAA model in absorption coefficients of optically active constituents estimating even in turbid coastal waters. Keywords: Remote sensing; Coastal Water; Absorption Coefficient; Semi-analytical Model

  5. Software Cost-Estimation Model

    Science.gov (United States)

    Tausworthe, R. C.

    1985-01-01

    Software Cost Estimation Model SOFTCOST provides automated resource and schedule model for software development. Combines several cost models found in open literature into one comprehensive set of algorithms. Compensates for nearly fifty implementation factors relative to size of task, inherited baseline, organizational and system environment and difficulty of task.

  6. Emergent randomness in the Jaynes-Cummings model

    International Nuclear Information System (INIS)

    Garraway, B M; Stenholm, S

    2008-01-01

    We consider the well-known Jaynes-Cummings model and ask if it can display randomness. As a solvable Hamiltonian system, it does not display chaotic behaviour in the ordinary sense. Here, however, we look at the distribution of values taken up during the total time evolution. This evolution is determined by the eigenvalues distributed as the square roots of integers and leads to a seemingly erratic behaviour. That this may display a random Gaussian value distribution is suggested by an exactly provable result by Kac. In order to reach our conclusion we use the Kac model to develop tests for the emergence of a Gaussian. Even if the consequent double limits are difficult to evaluate numerically, we find definite indications that the Jaynes-Cummings case also produces a randomness in its value distributions. Numerical methods do not establish such a result beyond doubt, but our conclusions are definite enough to suggest strongly an unexpected randomness emerging in a dynamic time evolution

  7. Critical Behavior of the Annealed Ising Model on Random Regular Graphs

    Science.gov (United States)

    Can, Van Hao

    2017-11-01

    In Giardinà et al. (ALEA Lat Am J Probab Math Stat 13(1):121-161, 2016), the authors have defined an annealed Ising model on random graphs and proved limit theorems for the magnetization of this model on some random graphs including random 2-regular graphs. Then in Can (Annealed limit theorems for the Ising model on random regular graphs, arXiv:1701.08639, 2017), we generalized their results to the class of all random regular graphs. In this paper, we study the critical behavior of this model. In particular, we determine the critical exponents and prove a non standard limit theorem stating that the magnetization scaled by n^{3/4} converges to a specific random variable, with n the number of vertices of random regular graphs.

  8. Estimating the encounter rate variance in distance sampling

    Science.gov (United States)

    Fewster, R.M.; Buckland, S.T.; Burnham, K.P.; Borchers, D.L.; Jupp, P.E.; Laake, J.L.; Thomas, L.

    2009-01-01

    The dominant source of variance in line transect sampling is usually the encounter rate variance. Systematic survey designs are often used to reduce the true variability among different realizations of the design, but estimating the variance is difficult and estimators typically approximate the variance by treating the design as a simple random sample of lines. We explore the properties of different encounter rate variance estimators under random and systematic designs. We show that a design-based variance estimator improves upon the model-based estimator of Buckland et al. (2001, Introduction to Distance Sampling. Oxford: Oxford University Press, p. 79) when transects are positioned at random. However, if populations exhibit strong spatial trends, both estimators can have substantial positive bias under systematic designs. We show that poststratification is effective in reducing this bias. ?? 2008, The International Biometric Society.

  9. Representing Degree Distributions, Clustering, and Homophily in Social Networks With Latent Cluster Random Effects Models.

    Science.gov (United States)

    Krivitsky, Pavel N; Handcock, Mark S; Raftery, Adrian E; Hoff, Peter D

    2009-07-01

    Social network data often involve transitivity, homophily on observed attributes, clustering, and heterogeneity of actor degrees. We propose a latent cluster random effects model to represent all of these features, and we describe a Bayesian estimation method for it. The model is applicable to both binary and non-binary network data. We illustrate the model using two real datasets. We also apply it to two simulated network datasets with the same, highly skewed, degree distribution, but very different network behavior: one unstructured and the other with transitivity and clustering. Models based on degree distributions, such as scale-free, preferential attachment and power-law models, cannot distinguish between these very different situations, but our model does.

  10. Approximating prediction uncertainty for random forest regression models

    Science.gov (United States)

    John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

    2016-01-01

    Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

  11. Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

    Science.gov (United States)

    Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

    2017-08-01

    Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

  12. LED Lighting System Reliability Modeling and Inference via Random Effects Gamma Process and Copula Function

    Directory of Open Access Journals (Sweden)

    Huibing Hao

    2015-01-01

    Full Text Available Light emitting diode (LED lamp has attracted increasing interest in the field of lighting systems due to its low energy and long lifetime. For different functions (i.e., illumination and color, it may have two or more performance characteristics. When the multiple performance characteristics are dependent, it creates a challenging problem to accurately analyze the system reliability. In this paper, we assume that the system has two performance characteristics, and each performance characteristic is governed by a random effects Gamma process where the random effects can capture the unit to unit differences. The dependency of performance characteristics is described by a Frank copula function. Via the copula function, the reliability assessment model is proposed. Considering the model is so complicated and analytically intractable, the Markov chain Monte Carlo (MCMC method is used to estimate the unknown parameters. A numerical example about actual LED lamps data is given to demonstrate the usefulness and validity of the proposed model and method.

  13. Compensatory and non-compensatory multidimensional randomized item response models

    NARCIS (Netherlands)

    Fox, J.P.; Entink, R.K.; Avetisyan, M.

    2014-01-01

    Randomized response (RR) models are often used for analysing univariate randomized response data and measuring population prevalence of sensitive behaviours. There is much empirical support for the belief that RR methods improve the cooperation of the respondents. Recently, RR models have been

  14. Random diffusion and leverage effect in financial markets.

    Science.gov (United States)

    Perelló, Josep; Masoliver, Jaume

    2003-03-01

    We prove that Brownian market models with random diffusion coefficients provide an exact measure of the leverage effect [J-P. Bouchaud et al., Phys. Rev. Lett. 87, 228701 (2001)]. This empirical fact asserts that past returns are anticorrelated with future diffusion coefficient. Several models with random diffusion have been suggested but without a quantitative study of the leverage effect. Our analysis lets us to fully estimate all parameters involved and allows a deeper study of correlated random diffusion models that may have practical implications for many aspects of financial markets.

  15. Modeling SMAP Spacecraft Attitude Control Estimation Error Using Signal Generation Model

    Science.gov (United States)

    Rizvi, Farheen

    2016-01-01

    Two ground simulation software are used to model the SMAP spacecraft dynamics. The CAST software uses a higher fidelity model than the ADAMS software. The ADAMS software models the spacecraft plant, controller and actuator models, and assumes a perfect sensor and estimator model. In this simulation study, the spacecraft dynamics results from the ADAMS software are used as CAST software is unavailable. The main source of spacecraft dynamics error in the higher fidelity CAST software is due to the estimation error. A signal generation model is developed to capture the effect of this estimation error in the overall spacecraft dynamics. Then, this signal generation model is included in the ADAMS software spacecraft dynamics estimate such that the results are similar to CAST. This signal generation model has similar characteristics mean, variance and power spectral density as the true CAST estimation error. In this way, ADAMS software can still be used while capturing the higher fidelity spacecraft dynamics modeling from CAST software.

  16. A cluster expansion approach to exponential random graph models

    International Nuclear Information System (INIS)

    Yin, Mei

    2012-01-01

    The exponential family of random graphs are among the most widely studied network models. We show that any exponential random graph model may alternatively be viewed as a lattice gas model with a finite Banach space norm. The system may then be treated using cluster expansion methods from statistical mechanics. In particular, we derive a convergent power series expansion for the limiting free energy in the case of small parameters. Since the free energy is the generating function for the expectations of other random variables, this characterizes the structure and behavior of the limiting network in this parameter region

  17. Robust estimation of hydrological model parameters

    Directory of Open Access Journals (Sweden)

    A. Bárdossy

    2008-11-01

    Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

  18. Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments

    International Nuclear Information System (INIS)

    Bachoc, F.

    2013-01-01

    The parametric estimation of the covariance function of a Gaussian process is studied, in the framework of the Kriging model. Maximum Likelihood and Cross Validation estimators are considered. The correctly specified case, in which the covariance function of the Gaussian process does belong to the parametric set used for estimation, is first studied in an increasing-domain asymptotic framework. The sampling considered is a randomly perturbed multidimensional regular grid. Consistency and asymptotic normality are proved for the two estimators. It is then put into evidence that strong perturbations of the regular grid are always beneficial to Maximum Likelihood estimation. The incorrectly specified case, in which the covariance function of the Gaussian process does not belong to the parametric set used for estimation, is then studied. It is shown that Cross Validation is more robust than Maximum Likelihood in this case. Finally, two applications of the Kriging model with Gaussian processes are carried out on industrial data. For a validation problem of the friction model of the thermal-hydraulic code FLICA 4, where experimental results are available, it is shown that Gaussian process modeling of the FLICA 4 code model error enables to considerably improve its predictions. Finally, for a meta modeling problem of the GERMINAL thermal-mechanical code, the interest of the Kriging model with Gaussian processes, compared to neural network methods, is shown. (author) [fr

  19. On parameter estimation in deformable models

    DEFF Research Database (Denmark)

    Fisker, Rune; Carstensen, Jens Michael

    1998-01-01

    Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian form...

  20. Measurement Model Nonlinearity in Estimation of Dynamical Systems

    Science.gov (United States)

    Majji, Manoranjan; Junkins, J. L.; Turner, J. D.

    2012-06-01

    The role of nonlinearity of the measurement model and its interactions with the uncertainty of measurements and geometry of the problem is studied in this paper. An examination of the transformations of the probability density function in various coordinate systems is presented for several astrodynamics applications. Smooth and analytic nonlinear functions are considered for the studies on the exact transformation of uncertainty. Special emphasis is given to understanding the role of change of variables in the calculus of random variables. The transformation of probability density functions through mappings is shown to provide insight in to understanding the evolution of uncertainty in nonlinear systems. Examples are presented to highlight salient aspects of the discussion. A sequential orbit determination problem is analyzed, where the transformation formula provides useful insights for making the choice of coordinates for estimation of dynamic systems.

  1. Cost efficiency of Japanese steam power generation companies: A Bayesian comparison of random and fixed frontier models

    Energy Technology Data Exchange (ETDEWEB)

    Assaf, A. George [Isenberg School of Management, University of Massachusetts-Amherst, 90 Campus Center Way, Amherst 01002 (United States); Barros, Carlos Pestana [Instituto Superior de Economia e Gestao, Technical University of Lisbon, Rua Miguel Lupi, 20, 1249-078 Lisbon (Portugal); Managi, Shunsuke [Graduate School of Environmental Studies, Tohoku University, 6-6-20 Aramaki-Aza Aoba, Aoba-Ku, Sendai 980-8579 (Japan)

    2011-04-15

    This study analyses and compares the cost efficiency of Japanese steam power generation companies using the fixed and random Bayesian frontier models. We show that it is essential to account for heterogeneity in modelling the performance of energy companies. Results from the model estimation also indicate that restricting CO{sub 2} emissions can lead to a decrease in total cost. The study finally discusses the efficiency variations between the energy companies under analysis, and elaborates on the managerial and policy implications of the results. (author)

  2. The effect of selection on genetic parameter estimates

    African Journals Online (AJOL)

    Unknown

    The South African Journal of Animal Science is available online at ... A simulation study was carried out to investigate the effect of selection on the estimation of genetic ... The model contained a fixed effect, random genetic and random.

  3. Uncertainty analysis for effluent trading planning using a Bayesian estimation-based simulation-optimization modeling approach.

    Science.gov (United States)

    Zhang, J L; Li, Y P; Huang, G H; Baetz, B W; Liu, J

    2017-06-01

    In this study, a Bayesian estimation-based simulation-optimization modeling approach (BESMA) is developed for identifying effluent trading strategies. BESMA incorporates nutrient fate modeling with soil and water assessment tool (SWAT), Bayesian estimation, and probabilistic-possibilistic interval programming with fuzzy random coefficients (PPI-FRC) within a general framework. Based on the water quality protocols provided by SWAT, posterior distributions of parameters can be analyzed through Bayesian estimation; stochastic characteristic of nutrient loading can be investigated which provides the inputs for the decision making. PPI-FRC can address multiple uncertainties in the form of intervals with fuzzy random boundaries and the associated system risk through incorporating the concept of possibility and necessity measures. The possibility and necessity measures are suitable for optimistic and pessimistic decision making, respectively. BESMA is applied to a real case of effluent trading planning in the Xiangxihe watershed, China. A number of decision alternatives can be obtained under different trading ratios and treatment rates. The results can not only facilitate identification of optimal effluent-trading schemes, but also gain insight into the effects of trading ratio and treatment rate on decision making. The results also reveal that decision maker's preference towards risk would affect decision alternatives on trading scheme as well as system benefit. Compared with the conventional optimization methods, it is proved that BESMA is advantageous in (i) dealing with multiple uncertainties associated with randomness and fuzziness in effluent-trading planning within a multi-source, multi-reach and multi-period context; (ii) reflecting uncertainties existing in nutrient transport behaviors to improve the accuracy in water quality prediction; and (iii) supporting pessimistic and optimistic decision making for effluent trading as well as promoting diversity of decision

  4. Computer simulations of the random barrier model

    DEFF Research Database (Denmark)

    Schrøder, Thomas; Dyre, Jeppe

    2002-01-01

    A brief review of experimental facts regarding ac electronic and ionic conduction in disordered solids is given followed by a discussion of what is perhaps the simplest realistic model, the random barrier model (symmetric hopping model). Results from large scale computer simulations are presented...

  5. On the estimation of multiple random integrals and U-statistics

    CERN Document Server

    Major, Péter

    2013-01-01

    This work starts with the study of those limit theorems in probability theory for which classical methods do not work. In many cases some form of linearization can help to solve the problem, because the linearized version is simpler. But in order to apply such a method we have to show that the linearization causes a negligible error. The estimation of this error leads to some important large deviation type problems, and the main subject of this work is their investigation. We provide sharp estimates of the tail distribution of multiple integrals with respect to a normalized empirical measure and so-called degenerate U-statistics and also of the supremum of appropriate classes of such quantities. The proofs apply a number of useful techniques of modern probability that enable us to investigate the non-linear functionals of independent random variables. This lecture note yields insights into these methods, and may also be useful for those who only want some new tools to help them prove limit theorems when stand...

  6. Uncertainty estimation of the velocity model for the TrigNet GPS network

    Science.gov (United States)

    Hackl, Matthias; Malservisi, Rocco; Hugentobler, Urs; Wonnacott, Richard

    2010-05-01

    Satellite based geodetic techniques - above all GPS - provide an outstanding tool to measure crustal motions. They are widely used to derive geodetic velocity models that are applied in geodynamics to determine rotations of tectonic blocks, to localize active geological features, and to estimate rheological properties of the crust and the underlying asthenosphere. However, it is not a trivial task to derive GPS velocities and their uncertainties from positioning time series. In general time series are assumed to be represented by linear models (sometimes offsets, annual, and semi-annual signals are included) and noise. It has been shown that models accounting only for white noise tend to underestimate the uncertainties of rates derived from long time series and that different colored noise components (flicker noise, random walk, etc.) need to be considered. However, a thorough error analysis including power spectra analyses and maximum likelihood estimates is quite demanding and are usually not carried out for every site, but the uncertainties are scaled by latitude dependent factors. Analyses of the South Africa continuous GPS network TrigNet indicate that the scaled uncertainties overestimate the velocity errors. So we applied a method similar to the Allan Variance that is commonly used in the estimation of clock uncertainties and is able to account for time dependent probability density functions (colored noise) to the TrigNet time series. Finally, we compared these estimates to the results obtained by spectral analyses using CATS. Comparisons with synthetic data show that the noise can be represented quite well by a power law model in combination with a seasonal signal in agreement with previous studies.

  7. Randomized Item Response Theory Models

    NARCIS (Netherlands)

    Fox, Gerardus J.A.

    2005-01-01

    The randomized response (RR) technique is often used to obtain answers on sensitive questions. A new method is developed to measure latent variables using the RR technique because direct questioning leads to biased results. Within the RR technique is the probability of the true response modeled by

  8. Parameter Estimation of Partial Differential Equation Models.

    Science.gov (United States)

    Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab

    2013-01-01

    Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.

  9. INTEGRATED SPEED ESTIMATION MODEL FOR MULTILANE EXPREESSWAYS

    Science.gov (United States)

    Hong, Sungjoon; Oguchi, Takashi

    In this paper, an integrated speed-estimation model is developed based on empirical analyses for the basic sections of intercity multilane expressway un der the uncongested condition. This model enables a speed estimation for each lane at any site under arb itrary highway-alignment, traffic (traffic flow and truck percentage), and rainfall conditions. By combin ing this model and a lane-use model which estimates traffic distribution on the lanes by each vehicle type, it is also possible to es timate an average speed across all the lanes of one direction from a traffic demand by vehicle type under specific highway-alignment and rainfall conditions. This model is exp ected to be a tool for the evaluation of traffic performance for expressways when the performance me asure is travel speed, which is necessary for Performance-Oriented Highway Planning and Design. Regarding the highway-alignment condition, two new estimators, called effective horizo ntal curvature and effective vertical grade, are proposed in this paper which take into account the influence of upstream and downstream alignment conditions. They are applied to the speed-estimation model, and it shows increased accuracy of the estimation.

  10. Gradients estimation from random points with volumetric tensor in turbulence

    Science.gov (United States)

    Watanabe, Tomoaki; Nagata, Koji

    2017-12-01

    We present an estimation method of fully-resolved/coarse-grained gradients from randomly distributed points in turbulence. The method is based on a linear approximation of spatial gradients expressed with the volumetric tensor, which is a 3 × 3 matrix determined by a geometric distribution of the points. The coarse grained gradient can be considered as a low pass filtered gradient, whose cutoff is estimated with the eigenvalues of the volumetric tensor. The present method, the volumetric tensor approximation, is tested for velocity and passive scalar gradients in incompressible planar jet and mixing layer. Comparison with a finite difference approximation on a Cartesian grid shows that the volumetric tensor approximation computes the coarse grained gradients fairly well at a moderate computational cost under various conditions of spatial distributions of points. We also show that imposing the solenoidal condition improves the accuracy of the present method for solenoidal vectors, such as a velocity vector in incompressible flows, especially when the number of the points is not large. The volumetric tensor approximation with 4 points poorly estimates the gradient because of anisotropic distribution of the points. Increasing the number of points from 4 significantly improves the accuracy. Although the coarse grained gradient changes with the cutoff length, the volumetric tensor approximation yields the coarse grained gradient whose magnitude is close to the one obtained by the finite difference. We also show that the velocity gradient estimated with the present method well captures the turbulence characteristics such as local flow topology, amplification of enstrophy and strain, and energy transfer across scales.

  11. Many multicenter trials had few events per center, requiring analysis via random-effects models or GEEs.

    Science.gov (United States)

    Kahan, Brennan C; Harhay, Michael O

    2015-12-01

    Adjustment for center in multicenter trials is recommended when there are between-center differences or when randomization has been stratified by center. However, common methods of analysis (such as fixed-effects, Mantel-Haenszel, or stratified Cox models) often require a large number of patients or events per center to perform well. We reviewed 206 multicenter randomized trials published in four general medical journals to assess the average number of patients and events per center and determine whether appropriate methods of analysis were used in trials with few patients or events per center. The median number of events per center/treatment arm combination for trials using a binary or survival outcome was 3 (interquartile range, 1-10). Sixteen percent of trials had less than 1 event per center/treatment combination, 50% fewer than 3, and 63% fewer than 5. Of the trials which adjusted for center using a method of analysis which requires a large number of events per center, 6% had less than 1 event per center-treatment combination, 25% fewer than 3, and 50% fewer than 5. Methods of analysis that allow for few events per center, such as random-effects models or generalized estimating equations (GEEs), were rarely used. Many multicenter trials contain few events per center. Adjustment for center using random-effects models or GEE with model-based (non-robust) standard errors may be beneficial in these scenarios. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. A multilevel model to estimate the within- and the between-center components of the exposure/disease association in the EPIC study.

    Science.gov (United States)

    Sera, Francesco; Ferrari, Pietro

    2015-01-01

    In a multicenter study, the overall relationship between exposure and the risk of cancer can be broken down into a within-center component, which reflects the individual level association, and a between-center relationship, which captures the association at the aggregate level. A piecewise exponential proportional hazards model with random effects was used to evaluate the association between dietary fiber intake and colorectal cancer (CRC) risk in the EPIC study. During an average follow-up of 11.0 years, 4,517 CRC events occurred among study participants recruited in 28 centers from ten European countries. Models were adjusted by relevant confounding factors. Heterogeneity among centers was modelled with random effects. Linear regression calibration was used to account for errors in dietary questionnaire (DQ) measurements. Risk ratio estimates for a 10 g/day increment in dietary fiber were equal to 0.90 (95%CI: 0.85, 0.96) and 0.85 (0.64, 1.14), at the individual and aggregate levels, respectively, while calibrated estimates were 0.85 (0.76, 0.94), and 0.87 (0.65, 1.15), respectively. In multicenter studies, over a straightforward ecological analysis, random effects models allow information at the individual and ecologic levels to be captured, while controlling for confounding at both levels of evidence.

  13. A multilevel model to estimate the within- and the between-center components of the exposure/disease association in the EPIC study.

    Directory of Open Access Journals (Sweden)

    Francesco Sera

    Full Text Available In a multicenter study, the overall relationship between exposure and the risk of cancer can be broken down into a within-center component, which reflects the individual level association, and a between-center relationship, which captures the association at the aggregate level. A piecewise exponential proportional hazards model with random effects was used to evaluate the association between dietary fiber intake and colorectal cancer (CRC risk in the EPIC study. During an average follow-up of 11.0 years, 4,517 CRC events occurred among study participants recruited in 28 centers from ten European countries. Models were adjusted by relevant confounding factors. Heterogeneity among centers was modelled with random effects. Linear regression calibration was used to account for errors in dietary questionnaire (DQ measurements. Risk ratio estimates for a 10 g/day increment in dietary fiber were equal to 0.90 (95%CI: 0.85, 0.96 and 0.85 (0.64, 1.14, at the individual and aggregate levels, respectively, while calibrated estimates were 0.85 (0.76, 0.94, and 0.87 (0.65, 1.15, respectively. In multicenter studies, over a straightforward ecological analysis, random effects models allow information at the individual and ecologic levels to be captured, while controlling for confounding at both levels of evidence.

  14. Improved diagnostic model for estimating wind energy

    Energy Technology Data Exchange (ETDEWEB)

    Endlich, R.M.; Lee, J.D.

    1983-03-01

    Because wind data are available only at scattered locations, a quantitative method is needed to estimate the wind resource at specific sites where wind energy generation may be economically feasible. This report describes a computer model that makes such estimates. The model uses standard weather reports and terrain heights in deriving wind estimates; the method of computation has been changed from what has been used previously. The performance of the current model is compared with that of the earlier version at three sites; estimates of wind energy at four new sites are also presented.

  15. 93-106, 2015 93 Multilevel random effect and marginal models

    African Journals Online (AJOL)

    Multilevel random effect and marginal models for longitudinal data ... and random effect models that take the correlation among measurements of the same subject ... comparing the level of redness, pain and irritability ... clinical trial evaluating the safety profile of a new .... likelihood-based methods to compare models and.

  16. Ising model of a randomly triangulated random surface as a definition of fermionic string theory

    International Nuclear Information System (INIS)

    Bershadsky, M.A.; Migdal, A.A.

    1986-01-01

    Fermionic degrees of freedom are added to randomly triangulated planar random surfaces. It is shown that the Ising model on a fixed graph is equivalent to a certain Majorana fermion theory on the dual graph. (orig.)

  17. On the number of subgraphs of the Barabasi-Albert random graph

    Energy Technology Data Exchange (ETDEWEB)

    Ryabchenko, Aleksandr A; Samosvat, Egor A [Moscow Institute of Physics and Technology (State University), Dolgoprudnyi, Moscow Region, Russian Frderation (Russian Federation)

    2012-06-30

    We study a model of a random graph of the type of the Barabasi-Albert preferential attachment model. We develop a technique that makes it possible to estimate the mathematical expectation for a fairly wide class of random variables in the model under consideration. We use this technique to prove a theorem on the asymptotics of the mathematical expectation of the number of subgraphs isomorphic to a certain fixed graph in the random graphs of this model.

  18. Identification of System Parameters by the Random Decrement Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Kirkegaard, Poul Henning; Rytter, Anders

    1991-01-01

    -Walker equations and finally, least-square fitting of the theoretical correlation function. The results are compared to the results of fitting an Auto Regressive Moving Average (ARMA) model directly to the system output from a single-degree-of-freedom system loaded by white noise.......The aim of this paper is to investigate and illustrate the possibilities of using correlation functions estimated by the Random Decrement Technique as a basis for parameter identification. A two-stage system identification system is used: first, the correlation functions are estimated by the Random...... Decrement Technique, and then the system parameters are identified from the correlation function estimates. Three different techniques are used in the parameter identification process: a simple non-parametric method, estimation of an Auto Regressive (AR) model by solving an overdetermined set of Yule...

  19. Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

    Science.gov (United States)

    Amalia, Junita; Purhadi, Otok, Bambang Widjanarko

    2017-11-01

    Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.

  20. Estimating Stochastic Volatility Models using Prediction-based Estimating Functions

    DEFF Research Database (Denmark)

    Lunde, Asger; Brix, Anne Floor

    to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from......In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared...... to correctly account for the noise are investigated. Our Monte Carlo study shows that the estimator based on PBEFs outperforms the GMM estimator, both in the setting with and without MMS noise. Finally, an empirical application investigates the possible challenges and general performance of applying the PBEF...

  1. Random matrices and random difference equations

    International Nuclear Information System (INIS)

    Uppuluri, V.R.R.

    1975-01-01

    Mathematical models leading to products of random matrices and random difference equations are discussed. A one-compartment model with random behavior is introduced, and it is shown how the average concentration in the discrete time model converges to the exponential function. This is of relevance to understanding how radioactivity gets trapped in bone structure in blood--bone systems. The ideas are then generalized to two-compartment models and mammillary systems, where products of random matrices appear in a natural way. The appearance of products of random matrices in applications in demography and control theory is considered. Then random sequences motivated from the following problems are studied: constant pulsing and random decay models, random pulsing and constant decay models, and random pulsing and random decay models

  2. Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine

    Directory of Open Access Journals (Sweden)

    Jeremy T. Howard

    2018-02-01

    Full Text Available In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs (n = 198 that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope. The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite was 0.15 (0.18 and 0.31 (0.40, respectively. For the parent drug (metabolite, the mean heritability across time was 0.27 (0.60 and 0.14 (0.44 for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug

  3. Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine

    Science.gov (United States)

    Howard, Jeremy T.; Ashwell, Melissa S.; Baynes, Ronald E.; Brooks, James D.; Yeatts, James L.; Maltecca, Christian

    2018-01-01

    In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs (n = 198) that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK) parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope). The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite) was 0.15 (0.18) and 0.31 (0.40), respectively. For the parent drug (metabolite), the mean heritability across time was 0.27 (0.60) and 0.14 (0.44) for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug

  4. Evaluating Bounds and Estimators for Constants of Random Polycrystals Composed of Orthotropic Elastic Materials

    Energy Technology Data Exchange (ETDEWEB)

    Berryman, J. G.

    2012-03-01

    While the well-known Voigt and Reuss (VR) bounds, and the Voigt-Reuss-Hill (VRH) elastic constant estimators for random polycrystals are all straightforwardly calculated once the elastic constants of anisotropic crystals are known, the Hashin-Shtrikman (HS) bounds and related self-consistent (SC) estimators for the same constants are, by comparison, more difficult to compute. Recent work has shown how to simplify (to some extent) these harder to compute HS bounds and SC estimators. An overview and analysis of a subsampling of these results is presented here with the main point being to show whether or not this extra work (i.e., in calculating both the HS bounds and the SC estimates) does provide added value since, in particular, the VRH estimators often do not fall within the HS bounds, while the SC estimators (for good reasons) have always been found to do so. The quantitative differences between the SC and the VRH estimators in the eight cases considered are often quite small however, being on the order of ±1%. These quantitative results hold true even though these polycrystal Voigt-Reuss-Hill estimators more typically (but not always) fall outside the Hashin-Shtrikman bounds, while the self-consistent estimators always fall inside (or on the boundaries of) these same bounds.

  5. Hedonic travel cost and random utility models of recreation

    Energy Technology Data Exchange (ETDEWEB)

    Pendleton, L. [Univ. of Southern California, Los Angeles, CA (United States); Mendelsohn, R.; Davis, E.W. [Yale Univ., New Haven, CT (United States). School of Forestry and Environmental Studies

    1998-07-09

    Micro-economic theory began as an attempt to describe, predict and value the demand and supply of consumption goods. Quality was largely ignored at first, but economists have started to address quality within the theory of demand and specifically the question of site quality, which is an important component of land management. This paper demonstrates that hedonic and random utility models emanate from the same utility theoretical foundation, although they make different estimation assumptions. Using a theoretically consistent comparison, both approaches are applied to examine the quality of wilderness areas in the Southeastern US. Data were collected on 4778 visits to 46 trails in 20 different forest areas near the Smoky Mountains. Visitor data came from permits and an independent survey. The authors limited the data set to visitors from within 300 miles of the North Carolina and Tennessee border in order to focus the analysis on single purpose trips. When consistently applied, both models lead to results with similar signs but different magnitudes. Because the two models are equally valid, recreation studies should continue to use both models to value site quality. Further, practitioners should be careful not to make simplifying a priori assumptions which limit the effectiveness of both techniques.

  6. Analysis of time to event outcomes in randomized controlled trials by generalized additive models.

    Directory of Open Access Journals (Sweden)

    Christos Argyropoulos

    Full Text Available Randomized Controlled Trials almost invariably utilize the hazard ratio calculated with a Cox proportional hazard model as a treatment efficacy measure. Despite the widespread adoption of HRs, these provide a limited understanding of the treatment effect and may even provide a biased estimate when the assumption of proportional hazards in the Cox model is not verified by the trial data. Additional treatment effect measures on the survival probability or the time scale may be used to supplement HRs but a framework for the simultaneous generation of these measures is lacking.By splitting follow-up time at the nodes of a Gauss Lobatto numerical quadrature rule, techniques for Poisson Generalized Additive Models (PGAM can be adopted for flexible hazard modeling. Straightforward simulation post-estimation transforms PGAM estimates for the log hazard into estimates of the survival function. These in turn were used to calculate relative and absolute risks or even differences in restricted mean survival time between treatment arms. We illustrate our approach with extensive simulations and in two trials: IPASS (in which the proportionality of hazards was violated and HEMO a long duration study conducted under evolving standards of care on a heterogeneous patient population.PGAM can generate estimates of the survival function and the hazard ratio that are essentially identical to those obtained by Kaplan Meier curve analysis and the Cox model. PGAMs can simultaneously provide multiple measures of treatment efficacy after a single data pass. Furthermore, supported unadjusted (overall treatment effect but also subgroup and adjusted analyses, while incorporating multiple time scales and accounting for non-proportional hazards in survival data.By augmenting the HR conventionally reported, PGAMs have the potential to support the inferential goals of multiple stakeholders involved in the evaluation and appraisal of clinical trial results under proportional and

  7. The Dangers of Estimating V˙O2max Using Linear, Nonexercise Prediction Models.

    Science.gov (United States)

    Nevill, Alan M; Cooke, Carlton B

    2017-05-01

    This study aimed to compare the accuracy and goodness of fit of two competing models (linear vs allometric) when estimating V˙O2max (mL·kg·min) using nonexercise prediction models. The two competing models were fitted to the V˙O2max (mL·kg·min) data taken from two previously published studies. Study 1 (the Allied Dunbar National Fitness Survey) recruited 1732 randomly selected healthy participants, 16 yr and older, from 30 English parliamentary constituencies. Estimates of V˙O2max were obtained using a progressive incremental test on a motorized treadmill. In study 2, maximal oxygen uptake was measured directly during a fatigue limited treadmill test in older men (n = 152) and women (n = 146) 55 to 86 yr old. In both studies, the quality of fit associated with estimating V˙O2max (mL·kg·min) was superior using allometric rather than linear (additive) models based on all criteria (R, maximum log-likelihood, and Akaike information criteria). Results suggest that linear models will systematically overestimate V˙O2max for participants in their 20s and underestimate V˙O2max for participants in their 60s and older. The residuals saved from the linear models were neither normally distributed nor independent of the predicted values nor age. This will probably explain the absence of a key quadratic age term in the linear models, crucially identified using allometric models. Not only does the curvilinear age decline within an exponential function follow a more realistic age decline (the right-hand side of a bell-shaped curve), but the allometric models identified either a stature-to-body mass ratio (study 1) or a fat-free mass-to-body mass ratio (study 2), both associated with leanness when estimating V˙O2max. Adopting allometric models will provide more accurate predictions of V˙O2max (mL·kg·min) using plausible, biologically sound, and interpretable models.

  8. Semi-parametric estimation for ARCH models

    Directory of Open Access Journals (Sweden)

    Raed Alzghool

    2018-03-01

    Full Text Available In this paper, we conduct semi-parametric estimation for autoregressive conditional heteroscedasticity (ARCH model with Quasi likelihood (QL and Asymptotic Quasi-likelihood (AQL estimation methods. The QL approach relaxes the distributional assumptions of ARCH processes. The AQL technique is obtained from the QL method when the process conditional variance is unknown. We present an application of the methods to a daily exchange rate series. Keywords: ARCH model, Quasi likelihood (QL, Asymptotic Quasi-likelihood (AQL, Martingale difference, Kernel estimator

  9. Deep subsurface structure modeling and site amplification factor estimation in Niigata plain for broadband strong motion prediction

    International Nuclear Information System (INIS)

    Sato, Hiroaki

    2009-01-01

    This report addresses a methodology of deep subsurface structure modeling in Niigata plain, Japan to estimate site amplification factor in the broadband frequency range for broadband strong motion prediction. In order to investigate deep S-wave velocity structures, we conduct microtremor array measurements at nine sites in Niigata plain, which are important to estimate both long- and short-period ground motion. The estimated depths of the top of the basement layer agree well with those of the Green tuff formation as well as the Bouguer anomaly distribution. Dispersion characteristics derived from the observed long-period ground motion records are well explained by the theoretical dispersion curves of Love wave group velocities calculated from the estimated subsurface structures. These results demonstrate the deep subsurface structures from microtremor array measurements make it possible to estimate long-period ground motions in Niigata plain. Moreover an applicability of microtremor array exploration for inclined basement structure like a folding structure is shown from the two dimensional finite difference numerical simulations. The short-period site amplification factors in Niigata plain are empirically estimated by the spectral inversion analysis from S-wave parts of strong motion data. The resultant characteristics of site amplification are relative large in the frequency range of about 1.5-5 Hz, and decay significantly with the frequency increasing over about 5 Hz. However, these features can't be explained by the calculations from the deep subsurface structures. The estimation of site amplification factors in the frequency range of about 1.5-5 Hz are improved by introducing a shallow detailed structure down to GL-20m depth at a site. We also propose to consider random fluctuation in a modeling of deep S-wave velocity structure for broadband site amplification factor estimation. The Site amplification in the frequency range higher than about 5 Hz are filtered

  10. Generating Realistic Labelled, Weighted Random Graphs

    Directory of Open Access Journals (Sweden)

    Michael Charles Davis

    2015-12-01

    Full Text Available Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs. Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

  11. Bayesian random-effect model for predicting outcome fraught with heterogeneity--an illustration with episodes of 44 patients with intractable epilepsy.

    Science.gov (United States)

    Yen, A M-F; Liou, H-H; Lin, H-L; Chen, T H-H

    2006-01-01

    The study aimed to develop a predictive model to deal with data fraught with heterogeneity that cannot be explained by sampling variation or measured covariates. The random-effect Poisson regression model was first proposed to deal with over-dispersion for data fraught with heterogeneity after making allowance for measured covariates. Bayesian acyclic graphic model in conjunction with Markov Chain Monte Carlo (MCMC) technique was then applied to estimate the parameters of both relevant covariates and random effect. Predictive distribution was then generated to compare the predicted with the observed for the Bayesian model with and without random effect. Data from repeated measurement of episodes among 44 patients with intractable epilepsy were used as an illustration. The application of Poisson regression without taking heterogeneity into account to epilepsy data yielded a large value of heterogeneity (heterogeneity factor = 17.90, deviance = 1485, degree of freedom (df) = 83). After taking the random effect into account, the value of heterogeneity factor was greatly reduced (heterogeneity factor = 0.52, deviance = 42.5, df = 81). The Pearson chi2 for the comparison between the expected seizure frequencies and the observed ones at two and three months of the model with and without random effect were 34.27 (p = 1.00) and 1799.90 (p dispersion attributed either to correlated property or to subject-to-subject variability.

  12. Supplementary Material for: Tukey g-and-h Random Fields

    KAUST Repository

    Xu, Ganggang; Genton, Marc G.

    2016-01-01

    We propose a new class of transGaussian random fields named Tukey g-and-h (TGH) random fields to model non-Gaussian spatial data. The proposed TGH random fields have extremely flexible marginal distributions, possibly skewed and/or heavy-tailed, and, therefore, have a wide range of applications. The special formulation of the TGH random field enables an automatic search for the most suitable transformation for the dataset of interest while estimating model parameters. Asymptotic properties of the maximum likelihood estimator and the probabilistic properties of the TGH random fields are investigated. An efficient estimation procedure, based on maximum approximated likelihood, is proposed and an extreme spatial outlier detection algorithm is formulated. Kriging and probabilistic prediction with TGH random fields are developed along with prediction confidence intervals. The predictive performance of TGH random fields is demonstrated through extensive simulation studies and an application to a dataset of total precipitation in the south east of the United States. Supplementary materials for this article are available online.

  13. The hard-core model on random graphs revisited

    International Nuclear Information System (INIS)

    Barbier, Jean; Krzakala, Florent; Zhang, Pan; Zdeborová, Lenka

    2013-01-01

    We revisit the classical hard-core model, also known as independent set and dual to vertex cover problem, where one puts particles with a first-neighbor hard-core repulsion on the vertices of a random graph. Although the case of random graphs with small and very large average degrees respectively are quite well understood, they yield qualitatively different results and our aim here is to reconciliate these two cases. We revisit results that can be obtained using the (heuristic) cavity method and show that it provides a closed-form conjecture for the exact density of the densest packing on random regular graphs with degree K ≥ 20, and that for K > 16 the nature of the phase transition is the same as for large K. This also shows that the hard-code model is the simplest mean-field lattice model for structural glasses and jamming

  14. Biochemical transport modeling, estimation, and detection in realistic environments

    Science.gov (United States)

    Ortner, Mathias; Nehorai, Arye

    2006-05-01

    Early detection and estimation of the spread of a biochemical contaminant are major issues for homeland security applications. We present an integrated approach combining the measurements given by an array of biochemical sensors with a physical model of the dispersion and statistical analysis to solve these problems and provide system performance measures. We approximate the dispersion model of the contaminant in a realistic environment through numerical simulations of reflected stochastic diffusions describing the microscopic transport phenomena due to wind and chemical diffusion using the Feynman-Kac formula. We consider arbitrary complex geometries and account for wind turbulence. Localizing the dispersive sources is useful for decontamination purposes and estimation of the cloud evolution. To solve the associated inverse problem, we propose a Bayesian framework based on a random field that is particularly powerful for localizing multiple sources with small amounts of measurements. We also develop a sequential detector using the numerical transport model we propose. Sequential detection allows on-line analysis and detecting wether a change has occurred. We first focus on the formulation of a suitable sequential detector that overcomes the presence of unknown parameters (e.g. release time, intensity and location). We compute a bound on the expected delay before false detection in order to decide the threshold of the test. For a fixed false-alarm rate, we obtain the detection probability of a substance release as a function of its location and initial concentration. Numerical examples are presented for two real-world scenarios: an urban area and an indoor ventilation duct.

  15. Application of Poisson random effect models for highway network screening.

    Science.gov (United States)

    Jiang, Ximiao; Abdel-Aty, Mohamed; Alamili, Samer

    2014-02-01

    In recent years, Bayesian random effect models that account for the temporal and spatial correlations of crash data became popular in traffic safety research. This study employs random effect Poisson Log-Normal models for crash risk hotspot identification. Both the temporal and spatial correlations of crash data were considered. Potential for Safety Improvement (PSI) were adopted as a measure of the crash risk. Using the fatal and injury crashes that occurred on urban 4-lane divided arterials from 2006 to 2009 in the Central Florida area, the random effect approaches were compared to the traditional Empirical Bayesian (EB) method and the conventional Bayesian Poisson Log-Normal model. A series of method examination tests were conducted to evaluate the performance of different approaches. These tests include the previously developed site consistence test, method consistence test, total rank difference test, and the modified total score test, as well as the newly proposed total safety performance measure difference test. Results show that the Bayesian Poisson model accounting for both temporal and spatial random effects (PTSRE) outperforms the model that with only temporal random effect, and both are superior to the conventional Poisson Log-Normal model (PLN) and the EB model in the fitting of crash data. Additionally, the method evaluation tests indicate that the PTSRE model is significantly superior to the PLN model and the EB model in consistently identifying hotspots during successive time periods. The results suggest that the PTSRE model is a superior alternative for road site crash risk hotspot identification. Copyright © 2013 Elsevier Ltd. All rights reserved.

  16. NIMROD: a program for inference via a normal approximation of the posterior in models with random effects based on ordinary differential equations.

    Science.gov (United States)

    Prague, Mélanie; Commenges, Daniel; Guedj, Jérémie; Drylewicz, Julia; Thiébaut, Rodolphe

    2013-08-01

    Models based on ordinary differential equations (ODE) are widespread tools for describing dynamical systems. In biomedical sciences, data from each subject can be sparse making difficult to precisely estimate individual parameters by standard non-linear regression but information can often be gained from between-subjects variability. This makes natural the use of mixed-effects models to estimate population parameters. Although the maximum likelihood approach is a valuable option, identifiability issues favour Bayesian approaches which can incorporate prior knowledge in a flexible way. However, the combination of difficulties coming from the ODE system and from the presence of random effects raises a major numerical challenge. Computations can be simplified by making a normal approximation of the posterior to find the maximum of the posterior distribution (MAP). Here we present the NIMROD program (normal approximation inference in models with random effects based on ordinary differential equations) devoted to the MAP estimation in ODE models. We describe the specific implemented features such as convergence criteria and an approximation of the leave-one-out cross-validation to assess the model quality of fit. In pharmacokinetics models, first, we evaluate the properties of this algorithm and compare it with FOCE and MCMC algorithms in simulations. Then, we illustrate NIMROD use on Amprenavir pharmacokinetics data from the PUZZLE clinical trial in HIV infected patients. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  17. Random matrix model for disordered conductors

    Indian Academy of Sciences (India)

    In the interpretation of transport properties of mesoscopic systems, the multichannel ... One defines the random matrix model with N eigenvalues 0. λТ ..... With heuristic arguments, using the ideas pertaining to Dyson Coulomb gas analogy,.

  18. Analog model for quantum gravity effects: phonons in random fluids.

    Science.gov (United States)

    Krein, G; Menezes, G; Svaiter, N F

    2010-09-24

    We describe an analog model for quantum gravity effects in condensed matter physics. The situation discussed is that of phonons propagating in a fluid with a random velocity wave equation. We consider that there are random fluctuations in the reciprocal of the bulk modulus of the system and study free phonons in the presence of Gaussian colored noise with zero mean. We show that, in this model, after performing the random averages over the noise function a free conventional scalar quantum field theory describing free phonons becomes a self-interacting model.

  19. Parameter Estimation of Partial Differential Equation Models

    KAUST Repository

    Xun, Xiaolei

    2013-09-01

    Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  20. Statistical analysis of random duration times

    International Nuclear Information System (INIS)

    Engelhardt, M.E.

    1996-04-01

    This report presents basic statistical methods for analyzing data obtained by observing random time durations. It gives nonparametric estimates of the cumulative distribution function, reliability function and cumulative hazard function. These results can be applied with either complete or censored data. Several models which are commonly used with time data are discussed, and methods for model checking and goodness-of-fit tests are discussed. Maximum likelihood estimates and confidence limits are given for the various models considered. Some results for situations where repeated durations such as repairable systems are also discussed

  1. A random matrix approach to credit risk.

    Directory of Open Access Journals (Sweden)

    Michael C Münnix

    Full Text Available We estimate generic statistical properties of a structural credit risk model by considering an ensemble of correlation matrices. This ensemble is set up by Random Matrix Theory. We demonstrate analytically that the presence of correlations severely limits the effect of diversification in a credit portfolio if the correlations are not identically zero. The existence of correlations alters the tails of the loss distribution considerably, even if their average is zero. Under the assumption of randomly fluctuating correlations, a lower bound for the estimation of the loss distribution is provided.

  2. A random matrix approach to credit risk.

    Science.gov (United States)

    Münnix, Michael C; Schäfer, Rudi; Guhr, Thomas

    2014-01-01

    We estimate generic statistical properties of a structural credit risk model by considering an ensemble of correlation matrices. This ensemble is set up by Random Matrix Theory. We demonstrate analytically that the presence of correlations severely limits the effect of diversification in a credit portfolio if the correlations are not identically zero. The existence of correlations alters the tails of the loss distribution considerably, even if their average is zero. Under the assumption of randomly fluctuating correlations, a lower bound for the estimation of the loss distribution is provided.

  3. Parameter Estimation for Thurstone Choice Models

    Energy Technology Data Exchange (ETDEWEB)

    Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-04-24

    We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.

  4. Nonparametric estimation in models for unobservable heterogeneity

    OpenAIRE

    Hohmann, Daniel

    2014-01-01

    Nonparametric models which allow for data with unobservable heterogeneity are studied. The first publication introduces new estimators and their asymptotic properties for conditional mixture models. The second publication considers estimation of a function from noisy observations of its Radon transform in a Gaussian white noise model.

  5. Modeling random combustion of lycopodium particles and gas

    Directory of Open Access Journals (Sweden)

    M Bidabadi

    2016-06-01

    Full Text Available The random modeling combustion of lycopodium particles has been researched by many authors. In this paper, we extend this model and we also generate a different method by analyzing the effect of random distributed sources of combustible mixture. The flame structure is assumed to consist of a preheat-vaporization zone, a reaction zone and finally a post flame zone. We divide the preheat zone to different parts. We assumed that there is different distribution of particles in sections which are really random. Meanwhile, it is presumed that the fuel particles vaporize first to yield gaseous fuel. In other words, most of the fuel particles are vaporized at the end of the preheat zone. It is assumed that the Zel’dovich number is large; therefore, the reaction term in preheat zone is negligible. In this work, the effect of random distribution of particles in the preheat zone on combustion characteristics such as burning velocity, flame temperature for different particle radius is obtained.

  6. Estimation of Model's Marginal likelihood Using Adaptive Sparse Grid Surrogates in Bayesian Model Averaging

    Science.gov (United States)

    Zeng, X.

    2015-12-01

    A large number of model executions are required to obtain alternative conceptual models' predictions and their posterior probabilities in Bayesian model averaging (BMA). The posterior model probability is estimated through models' marginal likelihood and prior probability. The heavy computation burden hinders the implementation of BMA prediction, especially for the elaborated marginal likelihood estimator. For overcoming the computation burden of BMA, an adaptive sparse grid (SG) stochastic collocation method is used to build surrogates for alternative conceptual models through the numerical experiment of a synthetical groundwater model. BMA predictions depend on model posterior weights (or marginal likelihoods), and this study also evaluated four marginal likelihood estimators, including arithmetic mean estimator (AME), harmonic mean estimator (HME), stabilized harmonic mean estimator (SHME), and thermodynamic integration estimator (TIE). The results demonstrate that TIE is accurate in estimating conceptual models' marginal likelihoods. The BMA-TIE has better predictive performance than other BMA predictions. TIE has high stability for estimating conceptual model's marginal likelihood. The repeated estimated conceptual model's marginal likelihoods by TIE have significant less variability than that estimated by other estimators. In addition, the SG surrogates are efficient to facilitate BMA predictions, especially for BMA-TIE. The number of model executions needed for building surrogates is 4.13%, 6.89%, 3.44%, and 0.43% of the required model executions of BMA-AME, BMA-HME, BMA-SHME, and BMA-TIE, respectively.

  7. The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models.

    Science.gov (United States)

    Browne, William J; Steele, Fiona; Golalizadeh, Mousa; Green, Martin J

    2009-06-01

    We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.

  8. Identification of System Parameters by the Random Decrement Technique

    DEFF Research Database (Denmark)

    Brincker, Rune; Kirkegaard, Poul Henning; Rytter, Anders

    -Walker equations and finally least square fitting of the theoretical correlation function. The results are compared to the results of fitting an Auto Regressive Moving Average(ARMA) model directly to the system output. All investigations are performed on the simulated output from a single degree-off-freedom system......The aim of this paper is to investigate and illustrate the possibilities of using correlation functions estimated by the Random Decrement Technique as a basis for parameter identification. A two-stage system identification method is used: first the correlation functions are estimated by the Random...... Decrement technique and then the system parameters are identified from the correlation function estimates. Three different techniques are used in the parameters identification process: a simple non-paramatic method, estimation of an Auto Regressive(AR) model by solving an overdetermined set of Yule...

  9. Collocation methods for uncertainty quanti cation in PDE models with random data

    KAUST Repository

    Nobile, Fabio

    2014-01-06

    In this talk we consider Partial Differential Equations (PDEs) whose input data are modeled as random fields to account for their intrinsic variability or our lack of knowledge. After parametrizing the input random fields by finitely many independent random variables, we exploit the high regularity of the solution of the PDE as a function of the input random variables and consider sparse polynomial approximations in probability (Polynomial Chaos expansion) by collocation methods. We first address interpolatory approximations where the PDE is solved on a sparse grid of Gauss points in the probability space and the solutions thus obtained interpolated by multivariate polynomials. We present recent results on optimized sparse grids in which the selection of points is based on a knapsack approach and relies on sharp estimates of the decay of the coefficients of the polynomial chaos expansion of the solution. Secondly, we consider regression approaches where the PDE is evaluated on randomly chosen points in the probability space and a polynomial approximation constructed by the least square method. We present recent theoretical results on the stability and optimality of the approximation under suitable conditions between the number of sampling points and the dimension of the polynomial space. In particular, we show that for uniform random variables, the number of sampling point has to scale quadratically with the dimension of the polynomial space to maintain the stability and optimality of the approximation. Numerical results show that such condition is sharp in the monovariate case but seems to be over-constraining in higher dimensions. The regression technique seems therefore to be attractive in higher dimensions.

  10. Flow in Random Microstructures: a Multilevel Monte Carlo Approach

    KAUST Repository

    Icardi, Matteo

    2016-01-06

    In this work we are interested in the fast estimation of effective parameters of random heterogeneous materials using Multilevel Monte Carlo (MLMC). MLMC is an efficient and flexible solution for the propagation of uncertainties in complex models, where an explicit parametrisation of the input randomness is not available or too expensive. We propose a general-purpose algorithm and computational code for the solution of Partial Differential Equations (PDEs) on random heterogeneous materials. We make use of the key idea of MLMC, based on different discretization levels, extending it in a more general context, making use of a hierarchy of physical resolution scales, solvers, models and other numerical/geometrical discretisation parameters. Modifications of the classical MLMC estimators are proposed to further reduce variance in cases where analytical convergence rates and asymptotic regimes are not available. Spheres, ellipsoids and general convex-shaped grains are placed randomly in the domain with different placing/packing algorithms and the effective properties of the heterogeneous medium are computed. These are, for example, effective diffusivities, conductivities, and reaction rates. The implementation of the Monte-Carlo estimators, the statistical samples and each single solver is done efficiently in parallel. The method is tested and applied for pore-scale simulations of random sphere packings.

  11. Infant bone age estimation based on fibular shaft length: model development and clinical validation

    International Nuclear Information System (INIS)

    Tsai, Andy; Stamoulis, Catherine; Bixby, Sarah D.; Breen, Micheal A.; Connolly, Susan A.; Kleinman, Paul K.

    2016-01-01

    Bone age in infants (<1 year old) is generally estimated using hand/wrist or knee radiographs, or by counting ossification centers. The accuracy and reproducibility of these techniques are largely unknown. To develop and validate an infant bone age estimation technique using fibular shaft length and compare it to conventional methods. We retrospectively reviewed negative skeletal surveys of 247 term-born low-risk-of-abuse infants (no persistent child protection team concerns) from July 2005 to February 2013, and randomized them into two datasets: (1) model development (n = 123) and (2) model testing (n = 124). Three pediatric radiologists measured all fibular shaft lengths. An ordinary linear regression model was fitted to dataset 1, and the model was evaluated using dataset 2. Readers also estimated infant bone ages in dataset 2 using (1) the hemiskeleton method of Sontag, (2) the hemiskeleton method of Elgenmark, (3) the hand/wrist atlas of Greulich and Pyle, and (4) the knee atlas of Pyle and Hoerr. For validation, we selected lower-extremity radiographs of 114 normal infants with no suspicion of abuse. Readers measured the fibulas and also estimated bone ages using the knee atlas. Bone age estimates from the proposed method were compared to the other methods. The proposed method outperformed all other methods in accuracy and reproducibility. Its accuracy was similar for the testing and validating datasets, with root-mean-square error of 36 days and 37 days; mean absolute error of 28 days and 31 days; and error variability of 22 days and 20 days, respectively. This study provides strong support for an infant bone age estimation technique based on fibular shaft length as a more accurate alternative to conventional methods. (orig.)

  12. Infant bone age estimation based on fibular shaft length: model development and clinical validation

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, Andy; Stamoulis, Catherine; Bixby, Sarah D.; Breen, Micheal A.; Connolly, Susan A.; Kleinman, Paul K. [Boston Children' s Hospital, Harvard Medical School, Department of Radiology, Boston, MA (United States)

    2016-03-15

    Bone age in infants (<1 year old) is generally estimated using hand/wrist or knee radiographs, or by counting ossification centers. The accuracy and reproducibility of these techniques are largely unknown. To develop and validate an infant bone age estimation technique using fibular shaft length and compare it to conventional methods. We retrospectively reviewed negative skeletal surveys of 247 term-born low-risk-of-abuse infants (no persistent child protection team concerns) from July 2005 to February 2013, and randomized them into two datasets: (1) model development (n = 123) and (2) model testing (n = 124). Three pediatric radiologists measured all fibular shaft lengths. An ordinary linear regression model was fitted to dataset 1, and the model was evaluated using dataset 2. Readers also estimated infant bone ages in dataset 2 using (1) the hemiskeleton method of Sontag, (2) the hemiskeleton method of Elgenmark, (3) the hand/wrist atlas of Greulich and Pyle, and (4) the knee atlas of Pyle and Hoerr. For validation, we selected lower-extremity radiographs of 114 normal infants with no suspicion of abuse. Readers measured the fibulas and also estimated bone ages using the knee atlas. Bone age estimates from the proposed method were compared to the other methods. The proposed method outperformed all other methods in accuracy and reproducibility. Its accuracy was similar for the testing and validating datasets, with root-mean-square error of 36 days and 37 days; mean absolute error of 28 days and 31 days; and error variability of 22 days and 20 days, respectively. This study provides strong support for an infant bone age estimation technique based on fibular shaft length as a more accurate alternative to conventional methods. (orig.)

  13. A generalized model via random walks for information filtering

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Zhuo-Ming, E-mail: zhuomingren@gmail.com [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland); Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, ChongQing, 400714 (China); Kong, Yixiu [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland); Shang, Ming-Sheng, E-mail: msshang@cigit.ac.cn [Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, ChongQing, 400714 (China); Zhang, Yi-Cheng [Department of Physics, University of Fribourg, Chemin du Musée 3, CH-1700, Fribourg (Switzerland)

    2016-08-06

    There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation. - Highlights: • We propose a generalized recommendation model employing the random walk dynamics. • The proposed model with single and hybrid of degree information is analyzed. • A strategy with the hybrid degree information improves precision of recommendation.

  14. A generalized model via random walks for information filtering

    International Nuclear Information System (INIS)

    Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng

    2016-01-01

    There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation. - Highlights: • We propose a generalized recommendation model employing the random walk dynamics. • The proposed model with single and hybrid of degree information is analyzed. • A strategy with the hybrid degree information improves precision of recommendation.

  15. Mathematical model of transmission network static state estimation

    Directory of Open Access Journals (Sweden)

    Ivanov Aleksandar

    2012-01-01

    Full Text Available In this paper the characteristics and capabilities of the power transmission network static state estimator are presented. The solving process of the mathematical model containing the measurement errors and their processing is developed. To evaluate difference between the general model of state estimation and the fast decoupled state estimation model, the both models are applied to an example, and so derived results are compared.

  16. A random energy model for size dependence : recurrence vs. transience

    NARCIS (Netherlands)

    Külske, Christof

    1998-01-01

    We investigate the size dependence of disordered spin models having an infinite number of Gibbs measures in the framework of a simplified 'random energy model for size dependence'. We introduce two versions (involving either independent random walks or branching processes), that can be seen as

  17. Amplitude Models for Discrimination and Yield Estimation

    Energy Technology Data Exchange (ETDEWEB)

    Phillips, William Scott [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-09-01

    This seminar presentation describes amplitude models and yield estimations that look at the data in order to inform legislation. The following points were brought forth in the summary: global models that will predict three-component amplitudes (R-T-Z) were produced; Q models match regional geology; corrected source spectra can be used for discrimination and yield estimation; three-component data increase coverage and reduce scatter in source spectral estimates; three-component efforts must include distance-dependent effects; a community effort on instrument calibration is needed.

  18. 3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

    Science.gov (United States)

    Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

    2015-03-01

    During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

  19. Robust estimation for ordinary differential equation models.

    Science.gov (United States)

    Cao, J; Wang, L; Xu, J

    2011-12-01

    Applied scientists often like to use ordinary differential equations (ODEs) to model complex dynamic processes that arise in biology, engineering, medicine, and many other areas. It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. We propose a robust method to address this problem. The dynamic process is represented with a nonparametric function, which is a linear combination of basis functions. The nonparametric function is estimated by a robust penalized smoothing method. The penalty term is defined with the parametric ODE model, which controls the roughness of the nonparametric function and maintains the fidelity of the nonparametric function to the ODE model. The basis coefficients and ODE parameters are estimated in two nested levels of optimization. The coefficient estimates are treated as an implicit function of ODE parameters, which enables one to derive the analytic gradients for optimization using the implicit function theorem. Simulation studies show that the robust method gives satisfactory estimates for the ODE parameters from noisy data with outliers. The robust method is demonstrated by estimating a predator-prey ODE model from real ecological data. © 2011, The International Biometric Society.

  20. How does spatial study design influence density estimates from spatial capture-recapture models?

    Directory of Open Access Journals (Sweden)

    Rahel Sollmann

    Full Text Available When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km(2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species.

  1. Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.

    Science.gov (United States)

    Palmer, Tom M; Holmes, Michael V; Keating, Brendan J; Sheehan, Nuala A

    2017-11-01

    Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors. © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.

  2. Effects of random noise in a dynamical model of love

    Energy Technology Data Exchange (ETDEWEB)

    Xu Yong, E-mail: hsux3@nwpu.edu.cn [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China); Gu Rencai; Zhang Huiqing [Department of Applied Mathematics, Northwestern Polytechnical University, Xi' an 710072 (China)

    2011-07-15

    Highlights: > We model the complexity and unpredictability of psychology as Gaussian white noise. > The stochastic system of love is considered including bifurcation and chaos. > We show that noise can both suppress and induce chaos in dynamical models of love. - Abstract: This paper aims to investigate the stochastic model of love and the effects of random noise. We first revisit the deterministic model of love and some basic properties are presented such as: symmetry, dissipation, fixed points (equilibrium), chaotic behaviors and chaotic attractors. Then we construct a stochastic love-triangle model with parametric random excitation due to the complexity and unpredictability of the psychological system, where the randomness is modeled as the standard Gaussian noise. Stochastic dynamics under different three cases of 'Romeo's romantic style', are examined and two kinds of bifurcations versus the noise intensity parameter are observed by the criteria of changes of top Lyapunov exponent and shape of stationary probability density function (PDF) respectively. The phase portraits and time history are carried out to verify the proposed results, and the good agreement can be found. And also the dual roles of the random noise, namely suppressing and inducing chaos are revealed.

  3. Effects of random noise in a dynamical model of love

    International Nuclear Information System (INIS)

    Xu Yong; Gu Rencai; Zhang Huiqing

    2011-01-01

    Highlights: → We model the complexity and unpredictability of psychology as Gaussian white noise. → The stochastic system of love is considered including bifurcation and chaos. → We show that noise can both suppress and induce chaos in dynamical models of love. - Abstract: This paper aims to investigate the stochastic model of love and the effects of random noise. We first revisit the deterministic model of love and some basic properties are presented such as: symmetry, dissipation, fixed points (equilibrium), chaotic behaviors and chaotic attractors. Then we construct a stochastic love-triangle model with parametric random excitation due to the complexity and unpredictability of the psychological system, where the randomness is modeled as the standard Gaussian noise. Stochastic dynamics under different three cases of 'Romeo's romantic style', are examined and two kinds of bifurcations versus the noise intensity parameter are observed by the criteria of changes of top Lyapunov exponent and shape of stationary probability density function (PDF) respectively. The phase portraits and time history are carried out to verify the proposed results, and the good agreement can be found. And also the dual roles of the random noise, namely suppressing and inducing chaos are revealed.

  4. A random field model for the estimation of seismic hazard. Final report for the period 1 January 1990 - 31 December 1990

    International Nuclear Information System (INIS)

    Yucemen, S.

    1991-02-01

    The general theory of stationary random functions is utilized to assess the seismic hazard associated with a linearly extending seismic source. The past earthquake occurrence data associated with a portion of the North Anatolian fault are used to demonstrate the implementation of the proposed model. 18 refs, figs and tabs

  5. A random field model for the estimation of seismic hazard. Final report for the period 1 January 1990 - 31 December 1990

    Energy Technology Data Exchange (ETDEWEB)

    Yucemen, S [Middle East Technical Univ., Ankara (Turkey). Dept. of Statistics

    1991-02-01

    The general theory of stationary random functions is utilized to assess the seismic hazard associated with a linearly extending seismic source. The past earthquake occurrence data associated with a portion of the North Anatolian fault are used to demonstrate the implementation of the proposed model. 18 refs, figs and tabs.

  6. Using Random Forest Models to Predict Organizational Violence

    Science.gov (United States)

    Levine, Burton; Bobashev, Georgly

    2012-01-01

    We present a methodology to access the proclivity of an organization to commit violence against nongovernment personnel. We fitted a Random Forest model using the Minority at Risk Organizational Behavior (MAROS) dataset. The MAROS data is longitudinal; so, individual observations are not independent. We propose a modification to the standard Random Forest methodology to account for the violation of the independence assumption. We present the results of the model fit, an example of predicting violence for an organization; and finally, we present a summary of the forest in a "meta-tree,"

  7. Model-Based Estimation of Ankle Joint Stiffness.

    Science.gov (United States)

    Misgeld, Berno J E; Zhang, Tony; Lüken, Markus J; Leonhardt, Steffen

    2017-03-29

    We address the estimation of biomechanical parameters with wearable measurement technologies. In particular, we focus on the estimation of sagittal plane ankle joint stiffness in dorsiflexion/plantar flexion. For this estimation, a novel nonlinear biomechanical model of the lower leg was formulated that is driven by electromyographic signals. The model incorporates a two-dimensional kinematic description in the sagittal plane for the calculation of muscle lever arms and torques. To reduce estimation errors due to model uncertainties, a filtering algorithm is necessary that employs segmental orientation sensor measurements. Because of the model's inherent nonlinearities and nonsmooth dynamics, a square-root cubature Kalman filter was developed. The performance of the novel estimation approach was evaluated in silico and in an experimental procedure. The experimental study was conducted with body-worn sensors and a test-bench that was specifically designed to obtain reference angle and torque measurements for a single joint. Results show that the filter is able to reconstruct joint angle positions, velocities and torque, as well as, joint stiffness during experimental test bench movements.

  8. Semi-empirical modelling for forest above ground biomass estimation using hybrid and fully PolSAR data

    Science.gov (United States)

    Tomar, Kiledar S.; Kumar, Shashi; Tolpekin, Valentyn A.; Joshi, Sushil K.

    2016-05-01

    Forests act as sink of carbon and as a result maintains carbon cycle in atmosphere. Deforestation leads to imbalance in global carbon cycle and changes in climate. Hence estimation of forest biophysical parameter like biomass becomes a necessity. PolSAR has the ability to discriminate the share of scattering element like surface, double bounce and volume scattering in a single SAR resolution cell. Studies have shown that volume scattering is a significant parameter for forest biophysical characterization which mainly occurred from vegetation due to randomly oriented structures. This random orientation of forest structure causes shift in orientation angle of polarization ellipse which ultimately disturbs the radar signature and shows overestimation of volume scattering and underestimation of double bounce scattering after decomposition of fully PolSAR data. Hybrid polarimetry has the advantage of zero POA shift due to rotational symmetry followed by the circular transmission of electromagnetic waves. The prime objective of this study was to extract the potential of Hybrid PolSAR and fully PolSAR data for AGB estimation using Extended Water Cloud model. Validation was performed using field biomass. The study site chosen was Barkot Forest, Uttarakhand, India. To obtain the decomposition components, m-alpha and Yamaguchi decomposition modelling for Hybrid and fully PolSAR data were implied respectively. The RGB composite image for both the decomposition techniques has generated. The contribution of all scattering from each plot for m-alpha and Yamaguchi decomposition modelling were extracted. The R2 value for modelled AGB and field biomass from Hybrid PolSAR and fully PolSAR data were found 0.5127 and 0.4625 respectively. The RMSE for Hybrid and fully PolSAR between modelled AGB and field biomass were 63.156 (t ha-1) and 73.424 (t ha-1) respectively. On the basis of RMSE and R2 value, this study suggests Hybrid PolSAR decomposition modelling to retrieve scattering

  9. Influence of Averaging Preprocessing on Image Analysis with a Markov Random Field Model

    Science.gov (United States)

    Sakamoto, Hirotaka; Nakanishi-Ohno, Yoshinori; Okada, Masato

    2018-02-01

    This paper describes our investigations into the influence of averaging preprocessing on the performance of image analysis. Averaging preprocessing involves a trade-off: image averaging is often undertaken to reduce noise while the number of image data available for image analysis is decreased. We formulated a process of generating image data by using a Markov random field (MRF) model to achieve image analysis tasks such as image restoration and hyper-parameter estimation by a Bayesian approach. According to the notions of Bayesian inference, posterior distributions were analyzed to evaluate the influence of averaging. There are three main results. First, we found that the performance of image restoration with a predetermined value for hyper-parameters is invariant regardless of whether averaging is conducted. We then found that the performance of hyper-parameter estimation deteriorates due to averaging. Our analysis of the negative logarithm of the posterior probability, which is called the free energy based on an analogy with statistical mechanics, indicated that the confidence of hyper-parameter estimation remains higher without averaging. Finally, we found that when the hyper-parameters are estimated from the data, the performance of image restoration worsens as averaging is undertaken. We conclude that averaging adversely influences the performance of image analysis through hyper-parameter estimation.

  10. Alternative model of random surfaces

    International Nuclear Information System (INIS)

    Ambartzumian, R.V.; Sukiasian, G.S.; Savvidy, G.K.; Savvidy, K.G.

    1992-01-01

    We analyse models of triangulated random surfaces and demand that geometrically nearby configurations of these surfaces must have close actions. The inclusion of this principle drives us to suggest a new action, which is a modified Steiner functional. General arguments, based on the Minkowski inequality, shows that the maximal distribution to the partition function comes from surfaces close to the sphere. (orig.)

  11. Improvement of Bragg peak shift estimation using dimensionality reduction techniques and predictive linear modeling

    Science.gov (United States)

    Xing, Yafei; Macq, Benoit

    2017-11-01

    With the emergence of clinical prototypes and first patient acquisitions for proton therapy, the research on prompt gamma imaging is aiming at making most use of the prompt gamma data for in vivo estimation of any shift from expected Bragg peak (BP). The simple problem of matching the measured prompt gamma profile of each pencil beam with a reference simulation from the treatment plan is actually made complex by uncertainties which can translate into distortions during treatment. We will illustrate this challenge and demonstrate the robustness of a predictive linear model we proposed for BP shift estimation based on principal component analysis (PCA) method. It considered the first clinical knife-edge slit camera design in use with anthropomorphic phantom CT data. Particularly, 4115 error scenarios were simulated for the learning model. PCA was applied to the training input randomly chosen from 500 scenarios for eliminating data collinearities. A total variance of 99.95% was used for representing the testing input from 3615 scenarios. This model improved the BP shift estimation by an average of 63+/-19% in a range between -2.5% and 86%, comparing to our previous profile shift (PS) method. The robustness of our method was demonstrated by a comparative study conducted by applying 1000 times Poisson noise to each profile. 67% cases obtained by the learning model had lower prediction errors than those obtained by PS method. The estimation accuracy ranged between 0.31 +/- 0.22 mm and 1.84 +/- 8.98 mm for the learning model, while for PS method it ranged between 0.3 +/- 0.25 mm and 20.71 +/- 8.38 mm.

  12. PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE

    Directory of Open Access Journals (Sweden)

    Maria Angeles Gallego

    2014-11-01

    Full Text Available Many medical and biological problems require to extract information from microscopical images. Boolean models have been extensively used to analyze binary images of random clumps in many scientific fields. In this paper, a particular type of Boolean model with an underlying non-stationary point process is considered. The intensity of the underlying point process is formulated as a fixed function of the distance to a region of interest. A method to estimate the parameters of this Boolean model is introduced, and its performance is checked in two different settings. Firstly, a comparative study with other existent methods is done using simulated data. Secondly, the method is applied to analyze the longleaf data set, which is a very popular data set in the context of point processes included in the R package spatstat. Obtained results show that the new method provides as accurate estimates as those obtained with more complex methods developed for the general case. Finally, to illustrate the application of this model and this method, a particular type of phytopathological images are analyzed. These images show callose depositions in leaves of Arabidopsis plants. The analysis of callose depositions, is very popular in the phytopathological literature to quantify activity of plant immunity.

  13. Application of random effects to the study of resource selection by animals.

    Science.gov (United States)

    Gillies, Cameron S; Hebblewhite, Mark; Nielsen, Scott E; Krawchuk, Meg A; Aldridge, Cameron L; Frair, Jacqueline L; Saher, D Joanne; Stevens, Cameron E; Jerde, Christopher L

    2006-07-01

    1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions

  14. Estimators of the Relations of Equivalence, Tolerance and Preference Based on Pairwise Comparisons with Random Errors

    Directory of Open Access Journals (Sweden)

    Leszek Klukowski

    2012-01-01

    Full Text Available This paper presents a review of results of the author in the area of estimation of the relations of equivalence, tolerance and preference within a finite set based on multiple, independent (in a stochastic way pairwise comparisons with random errors, in binary and multivalent forms. These estimators require weaker assumptions than those used in the literature on the subject. Estimates of the relations are obtained based on solutions to problems from discrete optimization. They allow application of both types of comparisons - binary and multivalent (this fact relates to the tolerance and preference relations. The estimates can be verified in a statistical way; in particular, it is possible to verify the type of the relation. The estimates have been applied by the author to problems regarding forecasting, financial engineering and bio-cybernetics. (original abstract

  15. Analyzing Health-Related Quality of Life Data to Estimate Parameters for Cost-Effectiveness Models: An Example Using Longitudinal EQ-5D Data from the SHIFT Randomized Controlled Trial.

    Science.gov (United States)

    Griffiths, Alison; Paracha, Noman; Davies, Andrew; Branscombe, Neil; Cowie, Martin R; Sculpher, Mark

    2017-03-01

    The aim of this article is to discuss methods used to analyze health-related quality of life (HRQoL) data from randomized controlled trials (RCTs) for decision analytic models. The analysis presented in this paper was used to provide HRQoL data for the ivabradine health technology assessment (HTA) submission in chronic heart failure. We have used a large, longitudinal EuroQol five-dimension questionnaire (EQ-5D) dataset from the Systolic Heart Failure Treatment with the I f Inhibitor Ivabradine Trial (SHIFT) (clinicaltrials.gov: NCT02441218) to illustrate issues and methods. HRQoL weights (utility values) were estimated from a mixed regression model developed using SHIFT EQ-5D data (n = 5313 patients). The regression model was used to predict HRQoL outcomes according to treatment, patient characteristics, and key clinical outcomes for patients with a heart rate ≥75 bpm. Ivabradine was associated with an HRQoL weight gain of 0.01. HRQoL weights differed according to New York Heart Association (NYHA) class (NYHA I-IV, no hospitalization: standard care 0.82-0.46; ivabradine 0.84-0.47). A reduction in HRQoL weight was associated with hospitalizations within 30 days of an HRQoL assessment visit, with this reduction varying by NYHA class [-0.07 (NYHA I) to -0.21 (NYHA IV)]. The mixed model explained variation in EQ-5D data according to key clinical outcomes and patient characteristics, providing essential information for long-term predictions of patient HRQoL in the cost-effectiveness model. This model was also used to estimate the loss in HRQoL associated with hospitalizations. In SHIFT many hospitalizations did not occur close to EQ-5D visits; hence, any temporary changes in HRQoL associated with such events would not be captured fully in observed RCT evidence, but could be predicted in our cost-effectiveness analysis using the mixed model. Given the large reduction in hospitalizations associated with ivabradine this was an important feature of the analysis. The

  16. Estimation of Stochastic Volatility Models by Nonparametric Filtering

    DEFF Research Database (Denmark)

    Kanaya, Shin; Kristensen, Dennis

    2016-01-01

    /estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...

  17. Dispersal kernel estimation: A comparison of empirical and modelled particle dispersion in a coastal marine system

    Science.gov (United States)

    Hrycik, Janelle M.; Chassé, Joël; Ruddick, Barry R.; Taggart, Christopher T.

    2013-11-01

    Early life-stage dispersal influences recruitment and is of significance in explaining the distribution and connectivity of marine species. Motivations for quantifying dispersal range from biodiversity conservation to the design of marine reserves and the mitigation of species invasions. Here we compare estimates of real particle dispersion in a coastal marine environment with similar estimates provided by hydrodynamic modelling. We do so by using a system of magnetically attractive particles (MAPs) and a magnetic-collector array that provides measures of Lagrangian dispersion based on the time-integration of MAPs dispersing through the array. MAPs released as a point source in a coastal marine location dispersed through the collector array over a 5-7 d period. A virtual release and observed (real-time) environmental conditions were used in a high-resolution three-dimensional hydrodynamic model to estimate the dispersal of virtual particles (VPs). The number of MAPs captured throughout the collector array and the number of VPs that passed through each corresponding model location were enumerated and compared. Although VP dispersal reflected several aspects of the observed MAP dispersal, the comparisons demonstrated model sensitivity to the small-scale (random-walk) particle diffusivity parameter (Kp). The one-dimensional dispersal kernel for the MAPs had an e-folding scale estimate in the range of 5.19-11.44 km, while those from the model simulations were comparable at 1.89-6.52 km, and also demonstrated sensitivity to Kp. Variations among comparisons are related to the value of Kp used in modelling and are postulated to be related to MAP losses from the water column and (or) shear dispersion acting on the MAPs; a process that is constrained in the model. Our demonstration indicates a promising new way of 1) quantitatively and empirically estimating the dispersal kernel in aquatic systems, and 2) quantitatively assessing and (or) improving regional hydrodynamic

  18. On the number of subgraphs of the Barabási-Albert random graph

    International Nuclear Information System (INIS)

    Ryabchenko, Aleksandr A; Samosvat, Egor A

    2012-01-01

    We study a model of a random graph of the type of the Barabási-Albert preferential attachment model. We develop a technique that makes it possible to estimate the mathematical expectation for a fairly wide class of random variables in the model under consideration. We use this technique to prove a theorem on the asymptotics of the mathematical expectation of the number of subgraphs isomorphic to a certain fixed graph in the random graphs of this model.

  19. Premium Pricing of Liability Insurance Using Random Sum Model

    Directory of Open Access Journals (Sweden)

    Mujiati Dwi Kartikasari

    2017-03-01

    Full Text Available Premium pricing is one of important activities in insurance. Nonlife insurance premium is calculated from expected value of historical data claims. The historical data claims are collected so that it forms a sum of independent random number which is called random sum. In premium pricing using random sum, claim frequency distribution and claim severity distribution are combined. The combination of these distributions is called compound distribution. By using liability claim insurance data, we analyze premium pricing using random sum model based on compound distribution

  20. Random matrix approach to plasmon resonances in the random impedance network model of disordered nanocomposites

    Science.gov (United States)

    Olekhno, N. A.; Beltukov, Y. M.

    2018-05-01

    Random impedance networks are widely used as a model to describe plasmon resonances in disordered metal-dielectric and other two-component nanocomposites. In the present work, the spectral properties of resonances in random networks are studied within the framework of the random matrix theory. We have shown that the appropriate ensemble of random matrices for the considered problem is the Jacobi ensemble (the MANOVA ensemble). The obtained analytical expressions for the density of states in such resonant networks show a good agreement with the results of numerical simulations in a wide range of metal filling fractions 0

  1. Parameter estimation in stochastic rainfall-runoff models

    DEFF Research Database (Denmark)

    Jonsdottir, Harpa; Madsen, Henrik; Palsson, Olafur Petur

    2006-01-01

    A parameter estimation method for stochastic rainfall-runoff models is presented. The model considered in the paper is a conceptual stochastic model, formulated in continuous-discrete state space form. The model is small and a fully automatic optimization is, therefore, possible for estimating all...... the parameter values are optimal for simulation or prediction. The data originates from Iceland and the model is designed for Icelandic conditions, including a snow routine for mountainous areas. The model demands only two input data series, precipitation and temperature and one output data series...

  2. Random Interchange of Magnetic Connectivity

    Science.gov (United States)

    Matthaeus, W. H.; Ruffolo, D. J.; Servidio, S.; Wan, M.; Rappazzo, A. F.

    2015-12-01

    Magnetic connectivity, the connection between two points along a magnetic field line, has a stochastic character associated with field lines random walking in space due to magnetic fluctuations, but connectivity can also change in time due to dynamical activity [1]. For fluctuations transverse to a strong mean field, this connectivity change be caused by stochastic interchange due to component reconnection. The process may be understood approximately by formulating a diffusion-like Fokker-Planck coefficient [2] that is asymptotically related to standard field line random walk. Quantitative estimates are provided, for transverse magnetic field models and anisotropic models such as reduced magnetohydrodynamics. In heliospheric applications, these estimates may be useful for understanding mixing between open and close field line regions near coronal hole boundaries, and large latitude excursions of connectivity associated with turbulence. [1] A. F. Rappazzo, W. H. Matthaeus, D. Ruffolo, S. Servidio & M. Velli, ApJL, 758, L14 (2012) [2] D. Ruffolo & W. Matthaeus, ApJ, 806, 233 (2015)

  3. Fast state estimation subject to random data loss in discrete-time nonlinear stochastic systems

    Science.gov (United States)

    Mahdi Alavi, S. M.; Saif, Mehrdad

    2013-12-01

    This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.

  4. Uncertainty estimation of the velocity model for stations of the TrigNet GPS network

    Science.gov (United States)

    Hackl, M.; Malservisi, R.; Hugentobler, U.

    2010-12-01

    Satellite based geodetic techniques - above all GPS - provide an outstanding tool to measure crustal motions. They are widely used to derive geodetic velocity models that are applied in geodynamics to determine rotations of tectonic blocks, to localize active geological features, and to estimate rheological properties of the crust and the underlying asthenosphere. However, it is not a trivial task to derive GPS velocities and their uncertainties from positioning time series. In general time series are assumed to be represented by linear models (sometimes offsets, annual, and semi-annual signals are included) and noise. It has been shown that error models accounting only for white noise tend to underestimate the uncertainties of rates derived from long time series and that different colored noise components (flicker noise, random walk, etc.) need to be considered. However, a thorough error analysis including power spectra analyses and maximum likelihood estimates is computationally expensive and is usually not carried out for every site, but the uncertainties are scaled by latitude dependent factors. Analyses of the South Africa continuous GPS network TrigNet indicate that the scaled uncertainties overestimate the velocity errors. So we applied a method similar to the Allan Variance that is commonly used in the estimation of clock uncertainties and is able to account for time dependent probability density functions (colored noise) to the TrigNet time series. Comparisons with synthetic data show that the noise can be represented quite well by a power law model in combination with a seasonal signal in agreement with previous studies, which allows for a reliable estimation of the velocity error. Finally, we compared these estimates to the results obtained by spectral analyses using CATS. Small differences may originate from non-normal distribution of the noise.

  5. Genetic correlations among body condition score, yield and fertility in multiparous cows using random regression models

    OpenAIRE

    Bastin, Catherine; Gillon, Alain; Massart, Xavier; Bertozzi, Carlo; Vanderick, Sylvie; Gengler, Nicolas

    2010-01-01

    Genetic correlations between body condition score (BCS) in lactation 1 to 3 and four economically important traits (days open, 305-days milk, fat, and protein yields recorded in the first 3 lactations) were estimated on about 12,500 Walloon Holstein cows using 4-trait random regression models. Results indicated moderate favorable genetic correlations between BCS and days open (from -0.46 to -0.62) and suggested the use of BCS for indirect selection on fertility. However, unfavorable genetic c...

  6. Random variables in forest policy: A systematic sensitivity analysis using CGE models

    International Nuclear Information System (INIS)

    Alavalapati, J.R.R.

    1999-01-01

    Computable general equilibrium (CGE) models are extensively used to simulate economic impacts of forest policies. Parameter values used in these models often play a central role in their outcome. Since econometric studies and best guesses are the main sources of these parameters, some randomness exists about the 'true' values of these parameters. Failure to incorporate this randomness into these models may limit the degree of confidence in the validity of the results. In this study, we conduct a systematic sensitivity analysis (SSA) to assess the economic impacts of: 1) a 1 % increase in tax on Canadian lumber and wood products exports to the United States (US), and 2) a 1% decrease in technical change in the lumber and wood products and pulp and paper sectors of the US and Canada. We achieve this task by using an aggregated version of global trade model developed by Hertel (1997) and the automated SSA procedure developed by Arndt and Pearson (1996). The estimated means and standard deviations suggest that certain impacts are more likely than others. For example, an increase in export tax is likely to cause a decrease in Canadian income, while an increase in US income is unlikely. On the other hand, a decrease in US welfare is likely, while an increase in Canadian welfare is unlikely, in response to an increase in tax. It is likely that income and welfare both fall in Canada and the US in response to a decrease in the technical change in lumber and wood products and pulp and paper sectors 21 refs, 1 fig, 5 tabs

  7. A guide for estimating dynamic panel models: the macroeconomics models specifiness

    International Nuclear Information System (INIS)

    Coletta, Gaetano

    2005-10-01

    The aim of this paper is to review estimators for dynamic panel data models, a topic in which the interest has grown recently. As a consequence 01 this late interest, different estimation techniques have been proposed in the last few years and, given the last development of the subject, there is still a lack 01 a comprehensive guide for panel data applications, and for macroeconomics panel data models in particular. Finally, we also provide some indications about the Stata software commands to estimate dynamic panel data models with the techniques illustrated in the paper [it

  8. CHIRP-Like Signals: Estimation, Detection and Processing A Sequential Model-Based Approach

    Energy Technology Data Exchange (ETDEWEB)

    Candy, J. V. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2016-08-04

    Chirp signals have evolved primarily from radar/sonar signal processing applications specifically attempting to estimate the location of a target in surveillance/tracking volume. The chirp, which is essentially a sinusoidal signal whose phase changes instantaneously at each time sample, has an interesting property in that its correlation approximates an impulse function. It is well-known that a matched-filter detector in radar/sonar estimates the target range by cross-correlating a replicant of the transmitted chirp with the measurement data reflected from the target back to the radar/sonar receiver yielding a maximum peak corresponding to the echo time and therefore enabling the desired range estimate. In this application, we perform the same operation as a radar or sonar system, that is, we transmit a “chirp-like pulse” into the target medium and attempt to first detect its presence and second estimate its location or range. Our problem is complicated by the presence of disturbance signals from surrounding broadcast stations as well as extraneous sources of interference in our frequency bands and of course the ever present random noise from instrumentation. First, we discuss the chirp signal itself and illustrate its inherent properties and then develop a model-based processing scheme enabling both the detection and estimation of the signal from noisy measurement data.

  9. Comparison of variance estimators for metaanalysis of instrumental variable estimates

    NARCIS (Netherlands)

    Schmidt, A. F.; Hingorani, A. D.; Jefferis, B. J.; White, J.; Groenwold, R. H H; Dudbridge, F.; Ben-Shlomo, Y.; Chaturvedi, N.; Engmann, J.; Hughes, A.; Humphries, S.; Hypponen, E.; Kivimaki, M.; Kuh, D.; Kumari, M.; Menon, U.; Morris, R.; Power, C.; Price, J.; Wannamethee, G.; Whincup, P.

    2016-01-01

    Background: Mendelian randomization studies perform instrumental variable (IV) analysis using genetic IVs. Results of individual Mendelian randomization studies can be pooled through meta-analysis. We explored how different variance estimators influence the meta-analysed IV estimate. Methods: Two

  10. An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk

    OpenAIRE

    Rushworth, Alastair; Lee, Duncan; Sarran, Christophe

    2014-01-01

    Statistical models used to estimate the spatio-temporal pattern in disease\\ud risk from areal unit data represent the risk surface for each time period with known\\ud covariates and a set of spatially smooth random effects. The latter act as a proxy\\ud for unmeasured spatial confounding, whose spatial structure is often characterised by\\ud a spatially smooth evolution between some pairs of adjacent areal units while other\\ud pairs exhibit large step changes. This spatial heterogeneity is not c...

  11. Data-Driven Robust RVFLNs Modeling of a Blast Furnace Iron-Making Process Using Cauchy Distribution Weighted M-Estimation

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Ping; Lv, Youbin; Wang, Hong; Chai, Tianyou

    2017-09-01

    Optimal operation of a practical blast furnace (BF) ironmaking process depends largely on a good measurement of molten iron quality (MIQ) indices. However, measuring the MIQ online is not feasible using the available techniques. In this paper, a novel data-driven robust modeling is proposed for online estimation of MIQ using improved random vector functional-link networks (RVFLNs). Since the output weights of traditional RVFLNs are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with outliers. This affects the modeling accuracy of RVFLNs. To solve this problem, a Cauchy distribution weighted M-estimation based robust RFVLNs is proposed. Since the weights of different outlier data are properly determined by the Cauchy distribution, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Moreover, given that the BF is a complex nonlinear system with numerous coupling variables, the data-driven canonical correlation analysis is employed to identify the most influential components from multitudinous factors that affect the MIQ indices to reduce the model dimension. Finally, experiments using industrial data and comparative studies have demonstrated that the obtained model produces a better modeling and estimating accuracy and stronger robustness than other modeling methods.

  12. A simulation of water pollution model parameter estimation

    Science.gov (United States)

    Kibler, J. F.

    1976-01-01

    A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.

  13. Estimation of direct effects for survival data by using the Aalen additive hazards model

    DEFF Research Database (Denmark)

    Martinussen, Torben; Vansteelandt, Stijn; Gerster, Mette

    2011-01-01

    We extend the definition of the controlled direct effect of a point exposure on a survival outcome, other than through some given, time-fixed intermediate variable, to the additive hazard scale. We propose two-stage estimators for this effect when the exposure is dichotomous and randomly assigned...... Aalen's additive regression for the event time, given exposure, intermediate variable and confounders. The second stage involves applying Aalen's additive model, given the exposure alone, to a modified stochastic process (i.e. a modification of the observed counting process based on the first...

  14. Bias analysis applied to Agricultural Health Study publications to estimate non-random sources of uncertainty.

    Science.gov (United States)

    Lash, Timothy L

    2007-11-26

    The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a qualitative description of study limitations. The latter approach is

  15. Bias analysis applied to Agricultural Health Study publications to estimate non-random sources of uncertainty

    Directory of Open Access Journals (Sweden)

    Lash Timothy L

    2007-11-01

    Full Text Available Abstract Background The associations of pesticide exposure with disease outcomes are estimated without the benefit of a randomized design. For this reason and others, these studies are susceptible to systematic errors. I analyzed studies of the associations between alachlor and glyphosate exposure and cancer incidence, both derived from the Agricultural Health Study cohort, to quantify the bias and uncertainty potentially attributable to systematic error. Methods For each study, I identified the prominent result and important sources of systematic error that might affect it. I assigned probability distributions to the bias parameters that allow quantification of the bias, drew a value at random from each assigned distribution, and calculated the estimate of effect adjusted for the biases. By repeating the draw and adjustment process over multiple iterations, I generated a frequency distribution of adjusted results, from which I obtained a point estimate and simulation interval. These methods were applied without access to the primary record-level dataset. Results The conventional estimates of effect associating alachlor and glyphosate exposure with cancer incidence were likely biased away from the null and understated the uncertainty by quantifying only random error. For example, the conventional p-value for a test of trend in the alachlor study equaled 0.02, whereas fewer than 20% of the bias analysis iterations yielded a p-value of 0.02 or lower. Similarly, the conventional fully-adjusted result associating glyphosate exposure with multiple myleoma equaled 2.6 with 95% confidence interval of 0.7 to 9.4. The frequency distribution generated by the bias analysis yielded a median hazard ratio equal to 1.5 with 95% simulation interval of 0.4 to 8.9, which was 66% wider than the conventional interval. Conclusion Bias analysis provides a more complete picture of true uncertainty than conventional frequentist statistical analysis accompanied by a

  16. Flood damage estimation of companies: A comparison of Stage-Damage-Functions and Random Forests

    Science.gov (United States)

    Sieg, Tobias; Kreibich, Heidi; Vogel, Kristin; Merz, Bruno

    2017-04-01

    The development of appropriate flood damage models plays an important role not only for the damage assessment after an event but also to develop adaptation and risk mitigation strategies. So called Stage-Damage-Functions (SDFs) are often applied as a standard approach to estimate flood damage. These functions assign a certain damage to the water depth depending on the use or other characteristics of the exposed objects. Recent studies apply machine learning algorithms like Random Forests (RFs) to model flood damage. These algorithms usually consider more influencing variables and promise to depict a more detailed insight into the damage processes. In addition they provide an inherent validation scheme. Our study focuses on direct, tangible damage of single companies. The objective is to model and validate the flood damage suffered by single companies with SDFs and RFs. The data sets used are taken from two surveys conducted after the floods in the Elbe and Danube catchments in the years 2002 and 2013 in Germany. Damage to buildings (n = 430), equipment (n = 651) as well as goods and stock (n = 530) are taken into account. The model outputs are validated via a comparison with the actual flood damage acquired by the surveys and subsequently compared with each other. This study investigates the gain in model performance with the use of additional data and the advantages and disadvantages of the RFs compared to SDFs. RFs show an increase in model performance with an increasing amount of data records over a comparatively large range, while the model performance of the SDFs is already saturated for a small set of records. In addition, the RFs are able to identify damage influencing variables, which improves the understanding of damage processes. Hence, RFs can slightly improve flood damage predictions and provide additional insight into the underlying mechanisms compared to SDFs.

  17. Recent developments in exponential random graph (p*) models for social networks

    NARCIS (Netherlands)

    Robins, Garry; Snijders, Tom; Wang, Peng; Handcock, Mark; Pattison, Philippa

    This article reviews new specifications for exponential random graph models proposed by Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handcock, M., 2006. New specifications for exponential random graph models. Sociological Methodology] and demonstrates their improvement over

  18. Random blebbing motion: A simple model linking cell structural properties to migration characteristics

    Science.gov (United States)

    Woolley, Thomas E.; Gaffney, Eamonn A.; Goriely, Alain

    2017-07-01

    If the plasma membrane of a cell is able to delaminate locally from its actin cortex, a cellular bleb can be produced. Blebs are pressure-driven protrusions, which are noteworthy for their ability to produce cellular motion. Starting from a general continuum mechanics description, we restrict ourselves to considering cell and bleb shapes that maintain approximately spherical forms. From this assumption, we obtain a tractable algebraic system for bleb formation. By including cell-substrate adhesions, we can model blebbing cell motility. Further, by considering mechanically isolated blebbing events, which are randomly distributed over the cell, we can derive equations linking the macroscopic migration characteristics to the microscopic structural parameters of the cell. This multiscale modeling framework is then used to provide parameter estimates, which are in agreement with current experimental data. In summary, the construction of the mathematical model provides testable relationships between the bleb size and cell motility.

  19. Random blebbing motion: A simple model linking cell structural properties to migration characteristics.

    Science.gov (United States)

    Woolley, Thomas E; Gaffney, Eamonn A; Goriely, Alain

    2017-07-01

    If the plasma membrane of a cell is able to delaminate locally from its actin cortex, a cellular bleb can be produced. Blebs are pressure-driven protrusions, which are noteworthy for their ability to produce cellular motion. Starting from a general continuum mechanics description, we restrict ourselves to considering cell and bleb shapes that maintain approximately spherical forms. From this assumption, we obtain a tractable algebraic system for bleb formation. By including cell-substrate adhesions, we can model blebbing cell motility. Further, by considering mechanically isolated blebbing events, which are randomly distributed over the cell, we can derive equations linking the macroscopic migration characteristics to the microscopic structural parameters of the cell. This multiscale modeling framework is then used to provide parameter estimates, which are in agreement with current experimental data. In summary, the construction of the mathematical model provides testable relationships between the bleb size and cell motility.

  20. The performance of random coefficient regression in accounting for residual confounding.

    Science.gov (United States)

    Gustafson, Paul; Greenland, Sander

    2006-09-01

    Greenland (2000, Biometrics 56, 915-921) describes the use of random coefficient regression to adjust for residual confounding in a particular setting. We examine this setting further, giving theoretical and empirical results concerning the frequentist and Bayesian performance of random coefficient regression. Particularly, we compare estimators based on this adjustment for residual confounding to estimators based on the assumption of no residual confounding. This devolves to comparing an estimator from a nonidentified but more realistic model to an estimator from a less realistic but identified model. The approach described by Gustafson (2005, Statistical Science 20, 111-140) is used to quantify the performance of a Bayesian estimator arising from a nonidentified model. From both theoretical calculations and simulations we find support for the idea that superior performance can be obtained by replacing unrealistic identifying constraints with priors that allow modest departures from those constraints. In terms of point-estimator bias this superiority arises when the extent of residual confounding is substantial, but the advantage is much broader in terms of interval estimation. The benefit from modeling residual confounding is maintained when the prior distributions employed only roughly correspond to reality, for the standard identifying constraints are equivalent to priors that typically correspond much worse.

  1. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data.

    Science.gov (United States)

    Yavorska, Olena O; Burgess, Stephen

    2017-12-01

    MendelianRandomization is a software package for the R open-source software environment that performs Mendelian randomization analyses using summarized data. The core functionality is to implement the inverse-variance weighted, MR-Egger and weighted median methods for multiple genetic variants. Several options are available to the user, such as the use of robust regression, fixed- or random-effects models and the penalization of weights for genetic variants with heterogeneous causal estimates. Extensions to these methods, such as allowing for variants to be correlated, can be chosen if appropriate. Graphical commands allow summarized data to be displayed in an interactive graph, or the plotting of causal estimates from multiple methods, for comparison. Although the main method of data entry is directly by the user, there is also an option for allowing summarized data to be incorporated from the PhenoScanner database of genotype-phenotype associations. We hope to develop this feature in future versions of the package. The R software environment is available for download from [https://www.r-project.org/]. The MendelianRandomization package can be downloaded from the Comprehensive R Archive Network (CRAN) within R, or directly from [https://cran.r-project.org/web/packages/MendelianRandomization/]. Both R and the MendelianRandomization package are released under GNU General Public Licenses (GPL-2|GPL-3). © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association.

  2. An Analysis of Spherical Particles Distribution Randomly Packed in a Medium for the Monte Carlo Implicit Modeling

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Jae Yong; Kim, Song Hyun; Shin, Chang Ho; Kim, Jong Kyung [Hanyang Univ., Seoul (Korea, Republic of)

    2014-05-15

    In this study, as a preliminary study to develop an implicit method having high accuracy, the distribution characteristics of spherical particles were evaluated by using explicit modeling techniques in various volume packing fractions. This study was performed to evaluate implicitly simulated distribution of randomly packed spheres in a medium. At first, an explicit modeling method to simulate random packed spheres in a hexahedron medium was proposed. The distributed characteristics of l{sub p} and r{sub p}, which are used in the particle position sampling, was estimated. It is analyzed that the use of the direct exponential distribution, which is generally used in the implicit modeling, can cause the distribution bias of the spheres. It is expected that the findings in this study can be utilized for improving the accuracy in using the implicit method. Spherical particles, which are randomly distributed in medium, are utilized for the radiation shields, fusion reactor blanket, fuels of VHTR reactors. Due to the difficulty on the simulation of the stochastic distribution, Monte Carlo (MC) method has been mainly considered as the tool for the analysis of the particle transport. For the MC modeling of the spherical particles, three methods are known; repeated structure, explicit modeling, and implicit modeling. Implicit method (called as the track length sampling method) is a modeling method that is the sampling based modeling technique of each spherical geometry (or track length of the sphere) during the MC simulation. Implicit modeling method has advantages in high computational efficiency and user convenience. However, it is noted that the implicit method has lower modeling accuracy in various finite mediums.

  3. Disorder Identification in Hysteresis Data: Recognition Analysis of the Random-Bond-Random-Field Ising Model

    International Nuclear Information System (INIS)

    Ovchinnikov, O. S.; Jesse, S.; Kalinin, S. V.; Bintacchit, P.; Trolier-McKinstry, S.

    2009-01-01

    An approach for the direct identification of disorder type and strength in physical systems based on recognition analysis of hysteresis loop shape is developed. A large number of theoretical examples uniformly distributed in the parameter space of the system is generated and is decorrelated using principal component analysis (PCA). The PCA components are used to train a feed-forward neural network using the model parameters as targets. The trained network is used to analyze hysteresis loops for the investigated system. The approach is demonstrated using a 2D random-bond-random-field Ising model, and polarization switching in polycrystalline ferroelectric capacitors.

  4. A random walk model for evaluating clinical trials involving serial observations.

    Science.gov (United States)

    Hopper, J L; Young, G P

    1988-05-01

    For clinical trials where the variable of interest is ordered and categorical (for example, disease severity, symptom scale), and where measurements are taken at intervals, it might be possible to achieve a greater discrimination between the efficacy of treatments by modelling each patient's progress as a stochastic process. The random walk is a simple, easily interpreted model that can be fitted by maximum likelihood using a maximization routine with inference based on standard likelihood theory. In general the model can allow for randomly censored data, incorporates measured prognostic factors, and inference is conditional on the (possibly non-random) allocation of patients. Tests of fit and of model assumptions are proposed, and application to two therapeutic trials of gastroenterological disorders are presented. The model gave measures of the rate of, and variability in, improvement for patients under different treatments. A small simulation study suggested that the model is more powerful than considering the difference between initial and final scores, even when applied to data generated by a mechanism other than the random walk model assumed in the analysis. It thus provides a useful additional statistical method for evaluating clinical trials.

  5. Differences among estimates of critical power and anaerobic work capacity derived from five mathematical models and the three-minute all-out test.

    Science.gov (United States)

    Bergstrom, Haley C; Housh, Terry J; Zuniga, Jorge M; Traylor, Daniel A; Lewis, Robert W; Camic, Clayton L; Schmidt, Richard J; Johnson, Glen O

    2014-03-01

    Estimates of critical power (CP) and anaerobic work capacity (AWC) from the power output vs. time relationship have been derived from various mathematical models. The purpose of this study was to examine estimates of CP and AWC from the multiple work bout, 2- and 3-parameter models, and those from the 3-minute all-out CP (CP3min) test. Nine college-aged subjects performed a maximal incremental test to determine the peak oxygen consumption rate and the gas exchange threshold. On separate days, each subject completed 4 randomly ordered constant power output rides to exhaustion to estimate CP and AWC from 5 regression models (2 linear, 2 nonlinear, and 1 exponential). During the final visit, CP and AWC were estimated from the CP3min test. The nonlinear 3-parameter (Nonlinear-3) model produced the lowest estimate of CP. The exponential (EXP) model and the CP3min test were not statistically different and produced the highest estimates of CP. Critical power estimated from the Nonlinear-3 model was 14% less than those from the EXP model and the CP3min test and 4-6% less than those from the linear models. Furthermore, the Nonlinear-3 and nonlinear 2-parameter (Nonlinear-2) models produced significantly greater estimates of AWC than did the linear models and CP3min. The current findings suggested that the Nonlinear-3 model may provide estimates of CP and AWC that more accurately reflect the asymptote of the power output vs. time relationship, the demarcation of the heavy and severe exercise intensity domains, and anaerobic capabilities than will the linear models and CP3min test.

  6. Model-Based Estimation of Ankle Joint Stiffness

    Directory of Open Access Journals (Sweden)

    Berno J. E. Misgeld

    2017-03-01

    Full Text Available We address the estimation of biomechanical parameters with wearable measurement technologies. In particular, we focus on the estimation of sagittal plane ankle joint stiffness in dorsiflexion/plantar flexion. For this estimation, a novel nonlinear biomechanical model of the lower leg was formulated that is driven by electromyographic signals. The model incorporates a two-dimensional kinematic description in the sagittal plane for the calculation of muscle lever arms and torques. To reduce estimation errors due to model uncertainties, a filtering algorithm is necessary that employs segmental orientation sensor measurements. Because of the model’s inherent nonlinearities and nonsmooth dynamics, a square-root cubature Kalman filter was developed. The performance of the novel estimation approach was evaluated in silico and in an experimental procedure. The experimental study was conducted with body-worn sensors and a test-bench that was specifically designed to obtain reference angle and torque measurements for a single joint. Results show that the filter is able to reconstruct joint angle positions, velocities and torque, as well as, joint stiffness during experimental test bench movements.

  7. Model-Based Estimation of Ankle Joint Stiffness

    Science.gov (United States)

    Misgeld, Berno J. E.; Zhang, Tony; Lüken, Markus J.; Leonhardt, Steffen

    2017-01-01

    We address the estimation of biomechanical parameters with wearable measurement technologies. In particular, we focus on the estimation of sagittal plane ankle joint stiffness in dorsiflexion/plantar flexion. For this estimation, a novel nonlinear biomechanical model of the lower leg was formulated that is driven by electromyographic signals. The model incorporates a two-dimensional kinematic description in the sagittal plane for the calculation of muscle lever arms and torques. To reduce estimation errors due to model uncertainties, a filtering algorithm is necessary that employs segmental orientation sensor measurements. Because of the model’s inherent nonlinearities and nonsmooth dynamics, a square-root cubature Kalman filter was developed. The performance of the novel estimation approach was evaluated in silico and in an experimental procedure. The experimental study was conducted with body-worn sensors and a test-bench that was specifically designed to obtain reference angle and torque measurements for a single joint. Results show that the filter is able to reconstruct joint angle positions, velocities and torque, as well as, joint stiffness during experimental test bench movements. PMID:28353683

  8. AMEM-ADL Polymer Migration Estimation Model User's Guide

    Science.gov (United States)

    The user's guide of the Arthur D. Little Polymer Migration Estimation Model (AMEM) provides the information on how the model estimates the fraction of a chemical additive that diffuses through polymeric matrices.

  9. A nonparametric mixture model for cure rate estimation.

    Science.gov (United States)

    Peng, Y; Dear, K B

    2000-03-01

    Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.

  10. Statistical Model-Based Face Pose Estimation

    Institute of Scientific and Technical Information of China (English)

    GE Xinliang; YANG Jie; LI Feng; WANG Huahua

    2007-01-01

    A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.

  11. Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

    Science.gov (United States)

    Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

    2016-01-01

    Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

  12. Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

    Science.gov (United States)

    Hoffman, A.; Forest, C. E.; Kemanian, A.

    2016-12-01

    A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with

  13. Variance estimation for generalized Cavalieri estimators

    OpenAIRE

    Johanna Ziegel; Eva B. Vedel Jensen; Karl-Anton Dorph-Petersen

    2011-01-01

    The precision of stereological estimators based on systematic sampling is of great practical importance. This paper presents methods of data-based variance estimation for generalized Cavalieri estimators where errors in sampling positions may occur. Variance estimators are derived under perturbed systematic sampling, systematic sampling with cumulative errors and systematic sampling with random dropouts. Copyright 2011, Oxford University Press.

  14. Efficiently adapting graphical models for selectivity estimation

    DEFF Research Database (Denmark)

    Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.

    2013-01-01

    cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss...... in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate...

  15. Money creation process in a random redistribution model

    Science.gov (United States)

    Chen, Siyan; Wang, Yougui; Li, Keqiang; Wu, Jinshan

    2014-01-01

    In this paper, the dynamical process of money creation in a random exchange model with debt is investigated. The money creation kinetics are analyzed by both the money-transfer matrix method and the diffusion method. From both approaches, we attain the same conclusion: the source of money creation in the case of random exchange is the agents with neither money nor debt. These analytical results are demonstrated by computer simulations.

  16. Radiation risk estimation based on measurement error models

    CERN Document Server

    Masiuk, Sergii; Shklyar, Sergiy; Chepurny, Mykola; Likhtarov, Illya

    2017-01-01

    This monograph discusses statistics and risk estimates applied to radiation damage under the presence of measurement errors. The first part covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk estimation in models with measurement errors is considered. Efficiency of the methods presented is verified using data from radio-epidemiological studies.

  17. Model-based estimation for dynamic cardiac studies using ECT.

    Science.gov (United States)

    Chiao, P C; Rogers, W L; Clinthorne, N H; Fessler, J A; Hero, A O

    1994-01-01

    The authors develop a strategy for joint estimation of physiological parameters and myocardial boundaries using ECT (emission computed tomography). They construct an observation model to relate parameters of interest to the projection data and to account for limited ECT system resolution and measurement noise. The authors then use a maximum likelihood (ML) estimator to jointly estimate all the parameters directly from the projection data without reconstruction of intermediate images. They also simulate myocardial perfusion studies based on a simplified heart model to evaluate the performance of the model-based joint ML estimator and compare this performance to the Cramer-Rao lower bound. Finally, the authors discuss model assumptions and potential uses of the joint estimation strategy.

  18. Angular Random Walk Estimation of a Time-Domain Switching Micromachined Gyroscope

    Science.gov (United States)

    2016-10-19

    angular random walk (ARW), bias instability, and scale factor instability. While there are methods to address issues with bias and scale factor...effects. Thus, it is expected that it will have low bias and scale factor instabilities. Simulated ARW performance of a particular incarnation of the...1 2. PARAMETRIC SYSTEM IDENTIFICATION BASED ON TIME-DOMAIN SWITCHING ........ 2 3. FINITE ELEMENT MODELING OF RESONATOR

  19. Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan

    Directory of Open Access Journals (Sweden)

    Muhammad Aslam

    2007-07-01

    Full Text Available For the problem of estimation of Money demand model of Pakistan, money supply (M1 shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons are made on the basis standard errors of the estimated coefficients, standard error of regression, Akaike Information Criteria (AIC value, and the Durban-Watson statistic for autocorrelation. We further show that nearest neighbour regression estimator performs better when comparing with the other nonparametric kernel estimator.

  20. Random a-adic groups and random net fractals

    Energy Technology Data Exchange (ETDEWEB)

    Li Yin [Department of Mathematics, Nanjing University, Nanjing 210093 (China)], E-mail: Lyjerry7788@hotmail.com; Su Weiyi [Department of Mathematics, Nanjing University, Nanjing 210093 (China)], E-mail: suqiu@nju.edu.cn

    2008-08-15

    Based on random a-adic groups, this paper investigates the relationship between the existence conditions of a positive flow in a random network and the estimation of the Hausdorff dimension of a proper random net fractal. Subsequently we describe some particular random fractals for which our results can be applied. Finally the Mauldin and Williams theorem is shown to be very important example for a random Cantor set with application in physics as shown in E-infinity theory.

  1. Cokriging model for estimation of water table elevation

    International Nuclear Information System (INIS)

    Hoeksema, R.J.; Clapp, R.B.; Thomas, A.L.; Hunley, A.E.; Farrow, N.D.; Dearstone, K.C.

    1989-01-01

    In geological settings where the water table is a subdued replica of the ground surface, cokriging can be used to estimate the water table elevation at unsampled locations on the basis of values of water table elevation and ground surface elevation measured at wells and at points along flowing streams. The ground surface elevation at the estimation point must also be determined. In the proposed method, separate models are generated for the spatial variability of the water table and ground surface elevation and for the dependence between these variables. After the models have been validated, cokriging or minimum variance unbiased estimation is used to obtain the estimated water table elevations and their estimation variances. For the Pits and Trenches area (formerly a liquid radioactive waste disposal facility) near Oak Ridge National Laboratory, water table estimation along a linear section, both with and without the inclusion of ground surface elevation as a statistical predictor, illustrate the advantages of the cokriging model

  2. A label field fusion bayesian model and its penalized maximum rand estimator for image segmentation.

    Science.gov (United States)

    Mignotte, Max

    2010-06-01

    This paper presents a novel segmentation approach based on a Markov random field (MRF) fusion model which aims at combining several segmentation results associated with simpler clustering models in order to achieve a more reliable and accurate segmentation result. The proposed fusion model is derived from the recently introduced probabilistic Rand measure for comparing one segmentation result to one or more manual segmentations of the same image. This non-parametric measure allows us to easily derive an appealing fusion model of label fields, easily expressed as a Gibbs distribution, or as a nonstationary MRF model defined on a complete graph. Concretely, this Gibbs energy model encodes the set of binary constraints, in terms of pairs of pixel labels, provided by each segmentation results to be fused. Combined with a prior distribution, this energy-based Gibbs model also allows for definition of an interesting penalized maximum probabilistic rand estimator with which the fusion of simple, quickly estimated, segmentation results appears as an interesting alternative to complex segmentation models existing in the literature. This fusion framework has been successfully applied on the Berkeley image database. The experiments reported in this paper demonstrate that the proposed method is efficient in terms of visual evaluation and quantitative performance measures and performs well compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.

  3. Stochastic equilibria of an asset pricing model with heterogeneous beliefs and random dividends

    NARCIS (Netherlands)

    Zhu, M.; Wang, D.; Guo, M.

    2011-01-01

    We investigate dynamical properties of a heterogeneous agent model with random dividends and further study the relationship between dynamical properties of the random model and those of the corresponding deterministic skeleton, which is obtained by setting the random dividends as their constant mean

  4. Modelling maximum likelihood estimation of availability

    International Nuclear Information System (INIS)

    Waller, R.A.; Tietjen, G.L.; Rock, G.W.

    1975-01-01

    Suppose the performance of a nuclear powered electrical generating power plant is continuously monitored to record the sequence of failure and repairs during sustained operation. The purpose of this study is to assess one method of estimating the performance of the power plant when the measure of performance is availability. That is, we determine the probability that the plant is operational at time t. To study the availability of a power plant, we first assume statistical models for the variables, X and Y, which denote the time-to-failure and the time-to-repair variables, respectively. Once those statistical models are specified, the availability, A(t), can be expressed as a function of some or all of their parameters. Usually those parameters are unknown in practice and so A(t) is unknown. This paper discusses the maximum likelihood estimator of A(t) when the time-to-failure model for X is an exponential density with parameter, lambda, and the time-to-repair model for Y is an exponential density with parameter, theta. Under the assumption of exponential models for X and Y, it follows that the instantaneous availability at time t is A(t)=lambda/(lambda+theta)+theta/(lambda+theta)exp[-[(1/lambda)+(1/theta)]t] with t>0. Also, the steady-state availability is A(infinity)=lambda/(lambda+theta). We use the observations from n failure-repair cycles of the power plant, say X 1 , X 2 , ..., Xsub(n), Y 1 , Y 2 , ..., Ysub(n) to present the maximum likelihood estimators of A(t) and A(infinity). The exact sampling distributions for those estimators and some statistical properties are discussed before a simulation model is used to determine 95% simulation intervals for A(t). The methodology is applied to two examples which approximate the operating history of two nuclear power plants. (author)

  5. Random cyclic stress-strain responses of a stainless steel pipe-weld metal. II. A modeling

    International Nuclear Information System (INIS)

    Zhao, Y.X.; Wang, J.N.

    2000-01-01

    For pt.I see ibid., vol.199, p.303-14, 2000. This paper pays special attention to an issue that there is a significant scatter of the stress-strain responses of a nuclear engineering material, 1Cr18Ni9Ti stainless steel pipe-weld metal. Efforts are made to reveal the random fatigue damage character by fracture surface observations and to model the random responses by introducing probability-based stress-strain curves of Ramberg-Osgood relation and its modified form. Results reveal that the fatigue damage is subjected to, 3-D interacting and involved microcracks. The three stages, namely microstructural short cracks (MSC), physical short cracks (PSC) and long cracks (LC) subdivided by Miller and de los Rios, can give a good characterization of the damage process. Both micro- and macro-behaviour of the material have the character of three stages. The 3-D effects are strong in the MSC stage, tend to a gradual decrease in the PSC stage, and then show saturation after going to the LC stage. Intrinsic causes of the random behaviour are the difference and evolution of the microstructural conditions ahead of the dominant crack tips. The 'effectively short fatigue crack criterion' introduced by Zhao et al. in observing the material surface short crack behaviour could facilitate an understanding of the mechanism of interaction and evolution. Based on the previous obtained appropriate assumed distribution, normal model, for the cyclic stress amplitude, the probability-based curves are approximated by the mean value and standard deviation cyclic stress-strain curves. Then, fatigue analysis at arbitrarily given reliability can be conveniently made according to the normal distribution function. To estimate these curves, a maximum likelihood method is developed. The analysis reveals that the curves could give a good modeling of the random responses of material. (orig.)

  6. Black-box modeling to estimate tissue temperature during radiofrequency catheter cardiac ablation: feasibility study on an agar phantom model

    International Nuclear Information System (INIS)

    Blasco-Gimenez, Ramón; Lequerica, Juan L; Herrero, Maria; Hornero, Fernando; Berjano, Enrique J

    2010-01-01

    The aim of this work was to study linear deterministic models to predict tissue temperature during radiofrequency cardiac ablation (RFCA) by measuring magnitudes such as electrode temperature, power and impedance between active and dispersive electrodes. The concept involves autoregressive models with exogenous input (ARX), which is a particular case of the autoregressive moving average model with exogenous input (ARMAX). The values of the mode parameters were determined from a least-squares fit of experimental data. The data were obtained from radiofrequency ablations conducted on agar models with different contact pressure conditions between electrode and agar (0 and 20 g) and different flow rates around the electrode (1, 1.5 and 2 L min −1 ). Half of all the ablations were chosen randomly to be used for identification (i.e. determination of model parameters) and the other half were used for model validation. The results suggest that (1) a linear model can be developed to predict tissue temperature at a depth of 4.5 mm during RF cardiac ablation by using the variables applied power, impedance and electrode temperature; (2) the best model provides a reasonably accurate estimate of tissue temperature with a 60% probability of achieving average errors better than 5 °C; (3) substantial errors (larger than 15 °C) were found only in 6.6% of cases and were associated with abnormal experiments (e.g. those involving the displacement of the ablation electrode) and (4) the impact of measuring impedance on the overall estimate is negligible (around 1 °C)

  7. Risk estimation using probability machines

    Science.gov (United States)

    2014-01-01

    Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

  8. Marginal and Random Intercepts Models for Longitudinal Binary Data with Examples from Criminology

    Science.gov (United States)

    Long, Jeffrey D.; Loeber, Rolf; Farrington, David P.

    2009-01-01

    Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides…

  9. Estimating the joint survival probabilities of married individuals

    NARCIS (Netherlands)

    Sanders, Lisanne; Melenberg, Bertrand

    We estimate the joint survival probability of spouses using a large random sample drawn from a Dutch census. As benchmarks we use two bivariate Weibull models. We consider more flexible models, using a semi-nonparametric approach, by extending the independent Weibull distribution using squared

  10. Quantum random oracle model for quantum digital signature

    Science.gov (United States)

    Shang, Tao; Lei, Qi; Liu, Jianwei

    2016-10-01

    The goal of this work is to provide a general security analysis tool, namely, the quantum random oracle (QRO), for facilitating the security analysis of quantum cryptographic protocols, especially protocols based on quantum one-way function. QRO is used to model quantum one-way function and different queries to QRO are used to model quantum attacks. A typical application of quantum one-way function is the quantum digital signature, whose progress has been hampered by the slow pace of the experimental realization. Alternatively, we use the QRO model to analyze the provable security of a quantum digital signature scheme and elaborate the analysis procedure. The QRO model differs from the prior quantum-accessible random oracle in that it can output quantum states as public keys and give responses to different queries. This tool can be a test bed for the cryptanalysis of more quantum cryptographic protocols based on the quantum one-way function.

  11. Re-evaluating neonatal-age models for ungulates: does model choice affect survival estimates?

    Directory of Open Access Journals (Sweden)

    Troy W Grovenburg

    Full Text Available New-hoof growth is regarded as the most reliable metric for predicting age of newborn ungulates, but variation in estimated age among hoof-growth equations that have been developed may affect estimates of survival in staggered-entry models. We used known-age newborns to evaluate variation in age estimates among existing hoof-growth equations and to determine the consequences of that variation on survival estimates. During 2001-2009, we captured and radiocollared 174 newborn (≤24-hrs old ungulates: 76 white-tailed deer (Odocoileus virginianus in Minnesota and South Dakota, 61 mule deer (O. hemionus in California, and 37 pronghorn (Antilocapra americana in South Dakota. Estimated age of known-age newborns differed among hoof-growth models and varied by >15 days for white-tailed deer, >20 days for mule deer, and >10 days for pronghorn. Accuracy (i.e., the proportion of neonates assigned to the correct age in aging newborns using published equations ranged from 0.0% to 39.4% in white-tailed deer, 0.0% to 3.3% in mule deer, and was 0.0% for pronghorns. Results of survival modeling indicated that variability in estimates of age-at-capture affected short-term estimates of survival (i.e., 30 days for white-tailed deer and mule deer, and survival estimates over a longer time frame (i.e., 120 days for mule deer. Conversely, survival estimates for pronghorn were not affected by estimates of age. Our analyses indicate that modeling survival in daily intervals is too fine a temporal scale when age-at-capture is unknown given the potential inaccuracies among equations used to estimate age of neonates. Instead, weekly survival intervals are more appropriate because most models accurately predicted ages within 1 week of the known age. Variation among results of neonatal-age models on short- and long-term estimates of survival for known-age young emphasizes the importance of selecting an appropriate hoof-growth equation and appropriately defining intervals (i

  12. Random-Effects Models for Meta-Analytic Structural Equation Modeling: Review, Issues, and Illustrations

    Science.gov (United States)

    Cheung, Mike W.-L.; Cheung, Shu Fai

    2016-01-01

    Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM.…

  13. Random isotropic one-dimensional XY-model

    Science.gov (United States)

    Gonçalves, L. L.; Vieira, A. P.

    1998-01-01

    The 1D isotropic s = ½XY-model ( N sites), with random exchange interaction in a transverse random field is considered. The random variables satisfy bimodal quenched distributions. The solution is obtained by using the Jordan-Wigner fermionization and a canonical transformation, reducing the problem to diagonalizing an N × N matrix, corresponding to a system of N noninteracting fermions. The calculations are performed numerically for N = 1000, and the field-induced magnetization at T = 0 is obtained by averaging the results for the different samples. For the dilute case, in the uniform field limit, the magnetization exhibits various discontinuities, which are the consequence of the existence of disconnected finite clusters distributed along the chain. Also in this limit, for finite exchange constants J A and J B, as the probability of J A varies from one to zero, the saturation field is seen to vary from Γ A to Γ B, where Γ A(Γ B) is the value of the saturation field for the pure case with exchange constant equal to J A(J B) .

  14. Model-based estimation for dynamic cardiac studies using ECT

    International Nuclear Information System (INIS)

    Chiao, P.C.; Rogers, W.L.; Clinthorne, N.H.; Fessler, J.A.; Hero, A.O.

    1994-01-01

    In this paper, the authors develop a strategy for joint estimation of physiological parameters and myocardial boundaries using ECT (Emission Computed Tomography). The authors construct an observation model to relate parameters of interest to the projection data and to account for limited ECT system resolution and measurement noise. The authors then use a maximum likelihood (ML) estimator to jointly estimate all the parameters directly from the projection data without reconstruction of intermediate images. The authors also simulate myocardial perfusion studies based on a simplified heart model to evaluate the performance of the model-based joint ML estimator and compare this performance to the Cramer-Rao lower bound. Finally, model assumptions and potential uses of the joint estimation strategy are discussed

  15. Estimation of some stochastic models used in reliability engineering

    International Nuclear Information System (INIS)

    Huovinen, T.

    1989-04-01

    The work aims to study the estimation of some stochastic models used in reliability engineering. In reliability engineering continuous probability distributions have been used as models for the lifetime of technical components. We consider here the following distributions: exponential, 2-mixture exponential, conditional exponential, Weibull, lognormal and gamma. Maximum likelihood method is used to estimate distributions from observed data which may be either complete or censored. We consider models based on homogeneous Poisson processes such as gamma-poisson and lognormal-poisson models for analysis of failure intensity. We study also a beta-binomial model for analysis of failure probability. The estimators of the parameters for three models are estimated by the matching moments method and in the case of gamma-poisson and beta-binomial models also by maximum likelihood method. A great deal of mathematical or statistical problems that arise in reliability engineering can be solved by utilizing point processes. Here we consider the statistical analysis of non-homogeneous Poisson processes to describe the failing phenomena of a set of components with a Weibull intensity function. We use the method of maximum likelihood to estimate the parameters of the Weibull model. A common cause failure can seriously reduce the reliability of a system. We consider a binomial failure rate (BFR) model as an application of the marked point processes for modelling common cause failure in a system. The parameters of the binomial failure rate model are estimated with the maximum likelihood method

  16. Efficient estimation of semiparametric copula models for bivariate survival data

    KAUST Repository

    Cheng, Guang

    2014-01-01

    A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When the survival data are censored, semiparametric efficient estimation has only been considered for some specific copula models such as the Gaussian copulas. In this paper, we obtain the semiparametric efficiency bound and efficient estimation for general semiparametric copula models for possibly censored data. We construct an approximate maximum likelihood estimator by approximating the log baseline hazard functions with spline functions. We show that our estimates of the copula dependence parameter and the survival functions are asymptotically normal and efficient. Simple consistent covariance estimators are also provided. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2013 Elsevier Inc.

  17. Direct Importance Estimation with Gaussian Mixture Models

    Science.gov (United States)

    Yamada, Makoto; Sugiyama, Masashi

    The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method — which we call the Gaussian mixture KLIEP (GM-KLIEP) — is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.

  18. Estimating temporary emigration and breeding proportions using capture-recapture data with Pollock's robust design

    Science.gov (United States)

    Kendall, W.L.; Nichols, J.D.; Hines, J.E.

    1997-01-01

    Statistical inference for capture-recapture studies of open animal populations typically relies on the assumption that all emigration from the studied population is permanent. However, there are many instances in which this assumption is unlikely to be met. We define two general models for the process of temporary emigration, completely random and Markovian. We then consider effects of these two types of temporary emigration on Jolly-Seber (Seber 1982) estimators and on estimators arising from the full-likelihood approach of Kendall et al. (1995) to robust design data. Capture-recapture data arising from Pollock's (1982) robust design provide the basis for obtaining unbiased estimates of demographic parameters in the presence of temporary emigration and for estimating the probability of temporary emigration. We present a likelihood-based approach to dealing with temporary emigration that permits estimation under different models of temporary emigration and yields tests for completely random and Markovian emigration. In addition, we use the relationship between capture probability estimates based on closed and open models under completely random temporary emigration to derive three ad hoc estimators for the probability of temporary emigration, two of which should be especially useful in situations where capture probabilities are heterogeneous among individual animals. Ad hoc and full-likelihood estimators are illustrated for small mammal capture-recapture data sets. We believe that these models and estimators will be useful for testing hypotheses about the process of temporary emigration, for estimating demographic parameters in the presence of temporary emigration, and for estimating probabilities of temporary emigration. These latter estimates are frequently of ecological interest as indicators of animal movement and, in some sampling situations, as direct estimates of breeding probabilities and proportions.

  19. Studies in astronomical time series analysis: Modeling random processes in the time domain

    Science.gov (United States)

    Scargle, J. D.

    1979-01-01

    Random process models phased in the time domain are used to analyze astrophysical time series data produced by random processes. A moving average (MA) model represents the data as a sequence of pulses occurring randomly in time, with random amplitudes. An autoregressive (AR) model represents the correlations in the process in terms of a linear function of past values. The best AR model is determined from sampled data and transformed to an MA for interpretation. The randomness of the pulse amplitudes is maximized by a FORTRAN algorithm which is relatively stable numerically. Results of test cases are given to study the effects of adding noise and of different distributions for the pulse amplitudes. A preliminary analysis of the optical light curve of the quasar 3C 273 is given.

  20. Estimators for longitudinal latent exposure models: examining measurement model assumptions.

    Science.gov (United States)

    Sánchez, Brisa N; Kim, Sehee; Sammel, Mary D

    2017-06-15

    Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  1. A probabilistic graphical model based stochastic input model construction

    International Nuclear Information System (INIS)

    Wan, Jiang; Zabaras, Nicholas

    2014-01-01

    Model reduction techniques have been widely used in modeling of high-dimensional stochastic input in uncertainty quantification tasks. However, the probabilistic modeling of random variables projected into reduced-order spaces presents a number of computational challenges. Due to the curse of dimensionality, the underlying dependence relationships between these random variables are difficult to capture. In this work, a probabilistic graphical model based approach is employed to learn the dependence by running a number of conditional independence tests using observation data. Thus a probabilistic model of the joint PDF is obtained and the PDF is factorized into a set of conditional distributions based on the dependence structure of the variables. The estimation of the joint PDF from data is then transformed to estimating conditional distributions under reduced dimensions. To improve the computational efficiency, a polynomial chaos expansion is further applied to represent the random field in terms of a set of standard random variables. This technique is combined with both linear and nonlinear model reduction methods. Numerical examples are presented to demonstrate the accuracy and efficiency of the probabilistic graphical model based stochastic input models. - Highlights: • Data-driven stochastic input models without the assumption of independence of the reduced random variables. • The problem is transformed to a Bayesian network structure learning problem. • Examples are given in flows in random media

  2. Estimation of a multivariate mean under model selection uncertainty

    Directory of Open Access Journals (Sweden)

    Georges Nguefack-Tsague

    2014-05-01

    Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty.  When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.

  3. On modeling HIV and T cells in vivo: assessing causal estimators in vaccine trials.

    Directory of Open Access Journals (Sweden)

    W David Wick

    2006-06-01

    Full Text Available The first efficacy trials--named STEP--of a T cell vaccine against HIV/AIDS began in 2004. The unprecedented structure of these trials raised new modeling and statistical challenges. Is it plausible that memory T cells, as opposed to antibodies, can actually prevent infection? If they fail at prevention, to what extent can they ameliorate disease? And how do we estimate efficacy in a vaccine trial with two primary endpoints, one traditional, one entirely novel (viral load after infection, and where the latter may be influenced by selection bias due to the former? In preparation for the STEP trials, biostatisticians developed novel techniques for estimating a causal effect of a vaccine on viral load, while accounting for post-randomization selection bias. But these techniques have not been tested in biologically plausible scenarios. We introduce new stochastic models of T cell and HIV kinetics, making use of new estimates of the rate that cytotoxic T lymphocytes--CTLs; the so-called killer T cells--can kill HIV-infected cells. Based on these models, we make the surprising discovery that it is not entirely implausible that HIV-specific CTLs might prevent infection--as the designers explicitly acknowledged when they chose the endpoints of the STEP trials. By simulating thousands of trials, we demonstrate that the new statistical methods can correctly identify an efficacious vaccine, while protecting against a false conclusion that the vaccine exacerbates disease. In addition to uncovering a surprising immunological scenario, our results illustrate the utility of mechanistic modeling in biostatistics.

  4. Fixed-location hydroacoustic monitoring designs for estimating fish passage using stratified random and systematic sampling

    International Nuclear Information System (INIS)

    Skalski, J.R.; Hoffman, A.; Ransom, B.H.; Steig, T.W.

    1993-01-01

    Five alternate sampling designs are compared using 15 d of 24-h continuous hydroacoustic data to identify the most favorable approach to fixed-location hydroacoustic monitoring of salmonid outmigrants. Four alternative aproaches to systematic sampling are compared among themselves and with stratified random sampling (STRS). Stratifying systematic sampling (STSYS) on a daily basis is found to reduce sampling error in multiday monitoring studies. Although sampling precision was predictable with varying levels of effort in STRS, neither magnitude nor direction of change in precision was predictable when effort was varied in systematic sampling (SYS). Furthermore, modifying systematic sampling to include replicated (e.g., nested) sampling (RSYS) is further shown to provide unbiased point and variance estimates as does STRS. Numerous short sampling intervals (e.g., 12 samples of 1-min duration per hour) must be monitored hourly using RSYS to provide efficient, unbiased point and interval estimates. For equal levels of effort, STRS outperformed all variations of SYS examined. Parametric approaches to confidence interval estimates are found to be superior to nonparametric interval estimates (i.e., bootstrap and jackknife) in estimating total fish passage. 10 refs., 1 fig., 8 tabs

  5. Parameter Estimation of Nonlinear Models in Forestry.

    OpenAIRE

    Fekedulegn, Desta; Mac Siúrtáin, Máirtín Pádraig; Colbert, Jim J.

    1999-01-01

    Partial derivatives of the negative exponential, monomolecular, Mitcherlich, Gompertz, logistic, Chapman-Richards, von Bertalanffy, Weibull and the Richard’s nonlinear growth models are presented. The application of these partial derivatives in estimating the model parameters is illustrated. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating top height to age of Norway spruce (Picea abies L.) from the Bowmont Norway Spruce Thinnin...

  6. Efficient Estimation of Non-Linear Dynamic Panel Data Models with Application to Smooth Transition Models

    DEFF Research Database (Denmark)

    Gørgens, Tue; Skeels, Christopher L.; Wurtz, Allan

    This paper explores estimation of a class of non-linear dynamic panel data models with additive unobserved individual-specific effects. The models are specified by moment restrictions. The class includes the panel data AR(p) model and panel smooth transition models. We derive an efficient set...... of moment restrictions for estimation and apply the results to estimation of panel smooth transition models with fixed effects, where the transition may be determined endogenously. The performance of the GMM estimator, both in terms of estimation precision and forecasting performance, is examined in a Monte...

  7. A generalized model via random walks for information filtering

    Science.gov (United States)

    Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng

    2016-08-01

    There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation.

  8. Modeling and Parameter Estimation of a Small Wind Generation System

    Directory of Open Access Journals (Sweden)

    Carlos A. Ramírez Gómez

    2013-11-01

    Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.

  9. Continuous energy Monte Carlo calculations for randomly distributed spherical fuels based on statistical geometry model

    Energy Technology Data Exchange (ETDEWEB)

    Murata, Isao [Osaka Univ., Suita (Japan); Mori, Takamasa; Nakagawa, Masayuki; Itakura, Hirofumi

    1996-03-01

    The method to calculate neutronics parameters of a core composed of randomly distributed spherical fuels has been developed based on a statistical geometry model with a continuous energy Monte Carlo method. This method was implemented in a general purpose Monte Carlo code MCNP, and a new code MCNP-CFP had been developed. This paper describes the model and method how to use it and the validation results. In the Monte Carlo calculation, the location of a spherical fuel is sampled probabilistically along the particle flight path from the spatial probability distribution of spherical fuels, called nearest neighbor distribution (NND). This sampling method was validated through the following two comparisons: (1) Calculations of inventory of coated fuel particles (CFPs) in a fuel compact by both track length estimator and direct evaluation method, and (2) Criticality calculations for ordered packed geometries. This method was also confined by applying to an analysis of the critical assembly experiment at VHTRC. The method established in the present study is quite unique so as to a probabilistic model of the geometry with a great number of spherical fuels distributed randomly. Realizing the speed-up by vector or parallel computations in future, it is expected to be widely used in calculation of a nuclear reactor core, especially HTGR cores. (author).

  10. Genetic Analysis of Daily Maximum Milking Speed by a Random Walk Model in Dairy Cows

    DEFF Research Database (Denmark)

    Karacaören, Burak; Janss, Luc; Kadarmideen, Haja

    Data were obtained from dairy cows stationed at research farm ETH Zurich for maximum milking speed. The main aims of this paper are a) to evaluate if the Wood curve is suitable to model mean lactation curve b) to predict longitudinal breeding values by random regression and random walk models of ...... filter applications: random walk model could give online prediction of breeding values. Hence without waiting for whole lactation records, genetic evaluation could be made when the daily or monthly data is available......Data were obtained from dairy cows stationed at research farm ETH Zurich for maximum milking speed. The main aims of this paper are a) to evaluate if the Wood curve is suitable to model mean lactation curve b) to predict longitudinal breeding values by random regression and random walk models...... of maximum milking speed. Wood curve did not provide a good fit to the data set. Quadratic random regressions gave better predictions compared with the random walk model. However random walk model does not need to be evaluated for different orders of regression coefficients. In addition with the Kalman...

  11. Estimating Lead (Pb) Bioavailability In A Mouse Model

    Science.gov (United States)

    Children are exposed to Pb through ingestion of Pb-contaminated soil. Soil Pb bioavailability is estimated using animal models or with chemically defined in vitro assays that measure bioaccessibility. However, bioavailability estimates in a large animal model (e.g., swine) can be...

  12. Information matrix estimation procedures for cognitive diagnostic models.

    Science.gov (United States)

    Liu, Yanlou; Xin, Tao; Andersson, Björn; Tian, Wei

    2018-03-06

    Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich-type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross-product information matrix, the sandwich-type covariance matrix and the two approaches proposed by de la Torre (2009, J. Educ. Behav. Stat., 34, 115) are discussed. Simulation results show that, for a correctly specified CDM and Q-matrix or with a slightly misspecified probability model, the observed information matrix and the sandwich-type covariance matrix exhibit good performance with respect to providing consistent standard errors of item parameter estimates. However, with substantial model misspecification only the sandwich-type covariance matrix exhibits robust performance. © 2018 The British Psychological Society.

  13. Lamplighter model of a random copolymer adsorption on a line

    Directory of Open Access Journals (Sweden)

    L.I. Nazarov

    2014-09-01

    Full Text Available We present a model of an AB-diblock random copolymer sequential self-packaging with local quenched interactions on a one-dimensional infinite sticky substrate. It is assumed that the A-A and B-B contacts are favorable, while A-B are not. The position of a newly added monomer is selected in view of the local contact energy minimization. The model demonstrates a self-organization behavior with the nontrivial dependence of the total energy, E (the number of unfavorable contacts, on the number of chain monomers, N: E ~ N^3/4 for quenched random equally probable distribution of A- and B-monomers along the chain. The model is treated by mapping it onto the "lamplighter" random walk and the diffusion-controlled chemical reaction of X+X → 0 type with the subdiffusive motion of reagents.

  14. Random effect selection in generalised linear models

    DEFF Research Database (Denmark)

    Denwood, Matt; Houe, Hans; Forkman, Björn

    We analysed abattoir recordings of meat inspection codes with possible relevance to onfarm animal welfare in cattle. Random effects logistic regression models were used to describe individual-level data obtained from 461,406 cattle slaughtered in Denmark. Our results demonstrate that the largest...

  15. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2011-01-01

    In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator

  16. Perspectives on Modelling BIM-enabled Estimating Practices

    Directory of Open Access Journals (Sweden)

    Willy Sher

    2014-12-01

    Full Text Available BIM-enabled estimating processes do not replace or provide a substitute for the traditional approaches used in the architecture, engineering and construction industries. This paper explores the impact of BIM on these traditional processes.  It identifies differences between the approaches used with BIM and other conventional methods, and between the various construction professionals that prepare estimates. We interviewed 17 construction professionals from client organizations, contracting organizations, consulting practices and specialist-project firms. Our analyses highlight several logical relationships between estimating processes and BIM attributes. Estimators need to respond to the challenges BIM poses to traditional estimating practices. BIM-enabled estimating circumvents long-established conventions and traditional approaches, and focuses on data management.  Consideration needs to be given to the model data required for estimating, to the means by which these data may be harnessed when exported, to the means by which the integrity of model data are protected, to the creation and management of tools that work effectively and efficiently in multi-disciplinary settings, and to approaches that narrow the gap between virtual reality and actual reality.  Areas for future research are also identified in the paper.

  17. Annealed central limit theorems for the ising model on random graphs

    NARCIS (Netherlands)

    Giardinà, C.; Giberti, C.; van der Hofstad, R.W.; Prioriello, M.L.

    2016-01-01

    The aim of this paper is to prove central limit theorems with respect to the annealed measure for the magnetization rescaled by √N of Ising models on random graphs. More precisely, we consider the general rank-1 inhomogeneous random graph (or generalized random graph), the 2-regular configuration

  18. Single-cluster dynamics for the random-cluster model

    NARCIS (Netherlands)

    Deng, Y.; Qian, X.; Blöte, H.W.J.

    2009-01-01

    We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those

  19. Comparing estimates of genetic variance across different relationship models.

    Science.gov (United States)

    Legarra, Andres

    2016-02-01

    Use of relationships between individuals to estimate genetic variances and heritabilities via mixed models is standard practice in human, plant and livestock genetics. Different models or information for relationships may give different estimates of genetic variances. However, comparing these estimates across different relationship models is not straightforward as the implied base populations differ between relationship models. In this work, I present a method to compare estimates of variance components across different relationship models. I suggest referring genetic variances obtained using different relationship models to the same reference population, usually a set of individuals in the population. Expected genetic variance of this population is the estimated variance component from the mixed model times a statistic, Dk, which is the average self-relationship minus the average (self- and across-) relationship. For most typical models of relationships, Dk is close to 1. However, this is not true for very deep pedigrees, for identity-by-state relationships, or for non-parametric kernels, which tend to overestimate the genetic variance and the heritability. Using mice data, I show that heritabilities from identity-by-state and kernel-based relationships are overestimated. Weighting these estimates by Dk scales them to a base comparable to genomic or pedigree relationships, avoiding wrong comparisons, for instance, "missing heritabilities". Copyright © 2015 Elsevier Inc. All rights reserved.

  20. A Generalized Model for Indoor Location Estimation Using Environmental Sound from Human Activity Recognition

    Directory of Open Access Journals (Sweden)

    Carlos E. Galván-Tejada

    2018-02-01

    Full Text Available The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc. were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS that uses contextual information from human activities (identified with data provided from the environmental sound can achieve an estimation that is 95% correct.

  1. Advanced fuel cycle cost estimation model and its cost estimation results for three nuclear fuel cycles using a dynamic model in Korea

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sungki, E-mail: sgkim1@kaeri.re.kr [Korea Atomic Energy Research Institute, 1045 Daedeokdaero, Yuseong-gu, Daejeon 305-353 (Korea, Republic of); Ko, Wonil [Korea Atomic Energy Research Institute, 1045 Daedeokdaero, Yuseong-gu, Daejeon 305-353 (Korea, Republic of); Youn, Saerom; Gao, Ruxing [University of Science and Technology, 217 Gajungro, Yuseong-gu, Daejeon 305-350 (Korea, Republic of); Bang, Sungsig, E-mail: ssbang@kaist.ac.kr [Korea Advanced Institute of Science and Technology, Department of Business and Technology Management, 291 Deahak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of)

    2015-11-15

    Highlights: • The nuclear fuel cycle cost using a new cost estimation model was analyzed. • The material flows of three nuclear fuel cycle options were calculated. • The generation cost of once-through was estimated to be 66.88 mills/kW h. • The generation cost of pyro-SFR recycling was estimated to be 78.06 mills/kW h. • The reactor cost was identified as the main cost driver of pyro-SFR recycling. - Abstract: The present study analyzes advanced nuclear fuel cycle cost estimation models such as the different discount rate model and its cost estimation results. To do so, an analysis of the nuclear fuel cycle cost of three options (direct disposal (once through), PWR–MOX (Mixed OXide fuel), and Pyro-SFR (Sodium-cooled Fast Reactor)) from the viewpoint of economic sense, focusing on the cost estimation model, was conducted using a dynamic model. From an analysis of the fuel cycle cost estimation results, it was found that some cost gap exists between the traditional same discount rate model and the advanced different discount rate model. However, this gap does not change the priority of the nuclear fuel cycle option from the viewpoint of economics. In addition, the fuel cycle costs of OT (Once-Through) and Pyro-SFR recycling based on the most likely value using a probabilistic cost estimation except for reactor costs were calculated to be 8.75 mills/kW h and 8.30 mills/kW h, respectively. Namely, the Pyro-SFR recycling option was more economical than the direct disposal option. However, if the reactor cost is considered, the economic sense in the generation cost between the two options (direct disposal vs. Pyro-SFR recycling) can be changed because of the high reactor cost of an SFR.

  2. Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models.

    Science.gov (United States)

    De Haas, Y; Janss, L L G; Kadarmideen, H N

    2007-10-01

    Genetic correlations between body condition score (BCS) and fertility traits in dairy cattle were estimated using bivariate random regression models. BCS was recorded by the Swiss Holstein Association on 22,075 lactating heifers (primiparous cows) from 856 sires. Fertility data during first lactation were extracted for 40,736 cows. The fertility traits were days to first service (DFS), days between first and last insemination (DFLI), calving interval (CI), number of services per conception (NSPC) and conception rate to first insemination (CRFI). A bivariate model was used to estimate genetic correlations between BCS as a longitudinal trait by random regression components, and daughter's fertility at the sire level as a single lactation measurement. Heritability of BCS was 0.17, and heritabilities for fertility traits were low (0.01-0.08). Genetic correlations between BCS and fertility over the lactation varied from: -0.45 to -0.14 for DFS; -0.75 to 0.03 for DFLI; from -0.59 to -0.02 for CI; from -0.47 to 0.33 for NSPC and from 0.08 to 0.82 for CRFI. These results show (genetic) interactions between fat reserves and reproduction along the lactation trajectory of modern dairy cows, which can be useful in genetic selection as well as in management. Maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in mid lactation when the genetic variance for BCS is largest, and the genetic correlations between BCS and fertility is strongest.

  3. Estimates of Intraclass Correlation Coefficients from Longitudinal Group-Randomized Trials of Adolescent HIV/STI/Pregnancy Prevention Programs

    Science.gov (United States)

    Glassman, Jill R.; Potter, Susan C.; Baumler, Elizabeth R.; Coyle, Karin K.

    2015-01-01

    Introduction: Group-randomized trials (GRTs) are one of the most rigorous methods for evaluating the effectiveness of group-based health risk prevention programs. Efficiently designing GRTs with a sample size that is sufficient for meeting the trial's power and precision goals while not wasting resources exceeding them requires estimates of the…

  4. Test models for improving filtering with model errors through stochastic parameter estimation

    International Nuclear Information System (INIS)

    Gershgorin, B.; Harlim, J.; Majda, A.J.

    2010-01-01

    The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.

  5. Randomization of grab-sampling strategies for estimating the annual exposure of U miners to Rn daughters.

    Science.gov (United States)

    Borak, T B

    1986-04-01

    Periodic grab sampling in combination with time-of-occupancy surveys has been the accepted procedure for estimating the annual exposure of underground U miners to Rn daughters. Temporal variations in the concentration of potential alpha energy in the mine generate uncertainties in this process. A system to randomize the selection of locations for measurement is described which can reduce uncertainties and eliminate systematic biases in the data. In general, a sample frequency of 50 measurements per year is sufficient to satisfy the criteria that the annual exposure be determined in working level months to within +/- 50% of the true value with a 95% level of confidence. Suggestions for implementing this randomization scheme are presented.

  6. Estimating Canopy Dark Respiration for Crop Models

    Science.gov (United States)

    Monje Mejia, Oscar Alberto

    2014-01-01

    Crop production is obtained from accurate estimates of daily carbon gain.Canopy gross photosynthesis (Pgross) can be estimated from biochemical models of photosynthesis using sun and shaded leaf portions and the amount of intercepted photosyntheticallyactive radiation (PAR).In turn, canopy daily net carbon gain can be estimated from canopy daily gross photosynthesis when canopy dark respiration (Rd) is known.

  7. Growth and renewable energy in Europe: A random effect model with evidence for neutrality hypothesis

    International Nuclear Information System (INIS)

    Menegaki, Angeliki N.

    2011-01-01

    This is an empirical study on the causal relationship between economic growth and renewable energy for 27 European countries in a multivariate panel framework over the period 1997-2007 using a random effect model and including final energy consumption, greenhouse gas emissions and employment as additional independent variables in the model. Empirical results do not confirm causality between renewable energy consumption and GDP, although panel causality tests unfold short-run relationships between renewable energy and greenhouse gas emissions and employment. The estimated cointegration factor refrains from unity, indicating only a weak, if any, relationship between economic growth and renewable energy consumption in Europe, suggesting evidence of the neutrality hypothesis, which can partly be explained by the uneven and insufficient exploitation of renewable energy sources across Europe.

  8. Estimation methods for nonlinear state-space models in ecology

    DEFF Research Database (Denmark)

    Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro

    2011-01-01

    The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta...... logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden...... Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance...

  9. Estimation and prediction under local volatility jump-diffusion model

    Science.gov (United States)

    Kim, Namhyoung; Lee, Younhee

    2018-02-01

    Volatility is an important factor in operating a company and managing risk. In the portfolio optimization and risk hedging using the option, the value of the option is evaluated using the volatility model. Various attempts have been made to predict option value. Recent studies have shown that stochastic volatility models and jump-diffusion models reflect stock price movements accurately. However, these models have practical limitations. Combining them with the local volatility model, which is widely used among practitioners, may lead to better performance. In this study, we propose a more effective and efficient method of estimating option prices by combining the local volatility model with the jump-diffusion model and apply it using both artificial and actual market data to evaluate its performance. The calibration process for estimating the jump parameters and local volatility surfaces is divided into three stages. We apply the local volatility model, stochastic volatility model, and local volatility jump-diffusion model estimated by the proposed method to KOSPI 200 index option pricing. The proposed method displays good estimation and prediction performance.

  10. Sampling designs and methods for estimating fish-impingement losses at cooling-water intakes

    International Nuclear Information System (INIS)

    Murarka, I.P.; Bodeau, D.J.

    1977-01-01

    Several systems for estimating fish impingement at power plant cooling-water intakes are compared to determine the most statistically efficient sampling designs and methods. Compared to a simple random sampling scheme the stratified systematic random sampling scheme, the systematic random sampling scheme, and the stratified random sampling scheme yield higher efficiencies and better estimators for the parameters in two models of fish impingement as a time-series process. Mathematical results and illustrative examples of the applications of the sampling schemes to simulated and real data are given. Some sampling designs applicable to fish-impingement studies are presented in appendixes

  11. The reverse effects of random perturbation on discrete systems for single and multiple population models

    International Nuclear Information System (INIS)

    Kang, Li; Tang, Sanyi

    2016-01-01

    Highlights: • The discrete single species and multiple species models with random perturbation are proposed. • The complex dynamics and interesting bifurcation behavior have been investigated. • The reverse effects of random perturbation on discrete systems have been discussed and revealed. • The main results can be applied for pest control and resources management. - Abstract: The natural species are likely to present several interesting and complex phenomena under random perturbations, which have been confirmed by simple mathematical models. The important questions are: how the random perturbations influence the dynamics of the discrete population models with multiple steady states or multiple species interactions? and is there any different effects for single species and multiple species models with random perturbation? To address those interesting questions, we have proposed the discrete single species model with two stable equilibria and the host-parasitoid model with Holling type functional response functions to address how the random perturbation affects the dynamics. The main results indicate that the random perturbation does not change the number of blurred orbits of the single species model with two stable steady states compared with results for the classical Ricker model with same random perturbation, but it can strength the stability. However, extensive numerical investigations depict that the random perturbation does not influence the complexities of the host-parasitoid models compared with the results for the models without perturbation, while it does increase the period of periodic orbits doubly. All those confirm that the random perturbation has a reverse effect on the dynamics of the discrete single and multiple population models, which could be applied in reality including pest control and resources management.

  12. BOX-COX transformation and random regression models for fecal egg count data

    Directory of Open Access Journals (Sweden)

    Marcos Vinicius Silva

    2012-01-01

    Full Text Available Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. In ruminants fecal egg count (FEC is commonly used to measure resistance to nematodes. FEC values are not normally distributed and logarithmic transformations have been used to achieve normality before analysis. However, the transformed data are often not normally distributed, especially when data are extremely skewed. A series of repeated FEC measurements may provide information about the population dynamics of a group or individual. A total of 6,375 FEC measures were obtained for 410 animals between 1992 and 2003 from the Beltsville Agricultural Research Center Angus herd. Original data were transformed using an extension of the Box-Cox transformation to approach normality and to estimate (covariance components. We also proposed using random regression models (RRM for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM and restricted maximum likelihood. Within the different orders of Legendre polynomials used, those with more parameters (order 4 adjusted FEC data best. Results indicated that the transformation of FEC data utilizing the Box-Cox transformation family was effective in reducing the skewness and kurtosis, and dramatically increased estimates of heritability, and measurements of FEC obtained in the period between 12 and 26 weeks in a 26-week experimental challenge period are genetically correlated.

  13. Box-Cox Transformation and Random Regression Models for Fecal egg Count Data.

    Science.gov (United States)

    da Silva, Marcos Vinícius Gualberto Barbosa; Van Tassell, Curtis P; Sonstegard, Tad S; Cobuci, Jaime Araujo; Gasbarre, Louis C

    2011-01-01

    Accurate genetic evaluation of livestock is based on appropriate modeling of phenotypic measurements. In ruminants, fecal egg count (FEC) is commonly used to measure resistance to nematodes. FEC values are not normally distributed and logarithmic transformations have been used in an effort to achieve normality before analysis. However, the transformed data are often still not normally distributed, especially when data are extremely skewed. A series of repeated FEC measurements may provide information about the population dynamics of a group or individual. A total of 6375 FEC measures were obtained for 410 animals between 1992 and 2003 from the Beltsville Agricultural Research Center Angus herd. Original data were transformed using an extension of the Box-Cox transformation to approach normality and to estimate (co)variance components. We also proposed using random regression models (RRM) for genetic and non-genetic studies of FEC. Phenotypes were analyzed using RRM and restricted maximum likelihood. Within the different orders of Legendre polynomials used, those with more parameters (order 4) adjusted FEC data best. Results indicated that the transformation of FEC data utilizing the Box-Cox transformation family was effective in reducing the skewness and kurtosis, and dramatically increased estimates of heritability, and measurements of FEC obtained in the period between 12 and 26 weeks in a 26-week experimental challenge period are genetically correlated.

  14. Utility based maintenance analysis using a Random Sign censoring model

    International Nuclear Information System (INIS)

    Andres Christen, J.; Ruggeri, Fabrizio; Villa, Enrique

    2011-01-01

    Industrial systems subject to failures are usually inspected when there are evident signs of an imminent failure. Maintenance is therefore performed at a random time, somehow dependent on the failure mechanism. A competing risk model, namely a Random Sign model, is considered to relate failure and maintenance times. We propose a novel Bayesian analysis of the model and apply it to actual data from a water pump in an oil refinery. The design of an optimal maintenance policy is then discussed under a formal decision theoretic approach, analyzing the goodness of the current maintenance policy and making decisions about the optimal maintenance time.

  15. A simplified method for random vibration analysis of structures with random parameters

    International Nuclear Information System (INIS)

    Ghienne, Martin; Blanzé, Claude

    2016-01-01

    Piezoelectric patches with adapted electrical circuits or viscoelastic dissipative materials are two solutions particularly adapted to reduce vibration of light structures. To accurately design these solutions, it is necessary to describe precisely the dynamical behaviour of the structure. It may quickly become computationally intensive to describe robustly this behaviour for a structure with nonlinear phenomena, such as contact or friction for bolted structures, and uncertain variations of its parameters. The aim of this work is to propose a non-intrusive reduced stochastic method to characterize robustly the vibrational response of a structure with random parameters. Our goal is to characterize the eigenspace of linear systems with dynamic properties considered as random variables. This method is based on a separation of random aspects from deterministic aspects and allows us to estimate the first central moments of each random eigenfrequency with a single deterministic finite elements computation. The method is applied to a frame with several Young's moduli modeled as random variables. This example could be expanded to a bolted structure including piezoelectric devices. The method needs to be enhanced when random eigenvalues are closely spaced. An indicator with no additional computational cost is proposed to characterize the ’’proximity” of two random eigenvalues. (paper)

  16. Estimating Heritability from Nuclear Family and Pedigree Data.

    Science.gov (United States)

    Bochud, Murielle

    2017-01-01

    Heritability is a measure of familial resemblance. Estimating the heritability of a trait could be one of the first steps in the gene mapping process. This chapter describes how to estimate heritability for quantitative traits from nuclear and pedigree data using the ASSOC program in the Statistical Analysis in Genetic Epidemiology (S.A.G.E.) software package. Estimating heritability rests on the assumption that the total phenotypic variance of a quantitative trait can be partitioned into independent genetic and environmental components. In turn, the genetic variance can be divided into an additive (polygenic) genetic variance, a dominance variance (nonlinear interaction effects between alleles at the same locus) and an epistatic variance (interaction effects between alleles at different loci). The last two are often assumed to be zero. The additive genetic variance represents the average effects of individual alleles on the phenotype and reflects transmissible resemblance between relatives. Heritability in the narrow sense (h 2 ) refers to the ratio of the additive genetic variance to the total phenotypic variance. Heritability is a dimensionless population-specific parameter. ASSOC estimates association parameters (regression coefficients) and variance components from family data. ASSOC uses a linear regression model in which the total residual variance is partitioned, after regressing on covariates, into the sum of random components such as an additive polygenic component, a random sibship component, random nuclear family components, a random marital component, and an individual-specific random component. Assortative mating, nonrandom ascertainment of families, and failure to account for key confounding factors may bias heritability estimates.

  17. Output variability caused by random seeds in a multi-agent transport simulation model

    DEFF Research Database (Denmark)

    Paulsen, Mads; Rasmussen, Thomas Kjær; Nielsen, Otto Anker

    2018-01-01

    Dynamic transport simulators are intended to support decision makers in transport-related issues, and as such it is valuable that the random variability of their outputs is as small as possible. In this study we analyse the output variability caused by random seeds of a multi-agent transport...... simulator (MATSim) when applied to a case study of Santiago de Chile. Results based on 100 different random seeds shows that the relative accuracies of estimated link loads tend to increase with link load, but that relative errors of up to 10 % do occur even for links with large volumes. Although...

  18. Consistency in Estimation and Model Selection of Dynamic Panel Data Models with Fixed Effects

    Directory of Open Access Journals (Sweden)

    Guangjie Li

    2015-07-01

    Full Text Available We examine the relationship between consistent parameter estimation and model selection for autoregressive panel data models with fixed effects. We find that the transformation of fixed effects proposed by Lancaster (2002 does not necessarily lead to consistent estimation of common parameters when some true exogenous regressors are excluded. We propose a data dependent way to specify the prior of the autoregressive coefficient and argue for comparing different model specifications before parameter estimation. Model selection properties of Bayes factors and Bayesian information criterion (BIC are investigated. When model uncertainty is substantial, we recommend the use of Bayesian Model Averaging to obtain point estimators with lower root mean squared errors (RMSE. We also study the implications of different levels of inclusion probabilities by simulations.

  19. Method of model reduction and multifidelity models for solute transport in random layered porous media

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Zhijie; Tartakovsky, Alexandre M.

    2017-09-01

    This work presents a hierarchical model for solute transport in bounded layered porous media with random permeability. The model generalizes the Taylor-Aris dispersion theory to stochastic transport in random layered porous media with a known velocity covariance function. In the hierarchical model, we represent (random) concentration in terms of its cross-sectional average and a variation function. We derive a one-dimensional stochastic advection-dispersion-type equation for the average concentration and a stochastic Poisson equation for the variation function, as well as expressions for the effective velocity and dispersion coefficient. We observe that velocity fluctuations enhance dispersion in a non-monotonic fashion: the dispersion initially increases with correlation length λ, reaches a maximum, and decreases to zero at infinity. Maximum enhancement can be obtained at the correlation length about 0.25 the size of the porous media perpendicular to flow.

  20. Property-Composition-Temperature Modeling of Waste Glass Melt Data Subject to a Randomization Restriction

    International Nuclear Information System (INIS)

    Piepel, Gregory F.; Heredia-Langner, Alejandro; Cooley, Scott K.

    2008-01-01

    Properties such as viscosity and electrical conductivity of glass melts are functions of melt temperature as well as glass composition. When measuring such a property for several glasses, the property is typically measured at several temperatures for one glass, then at several temperatures for the next glass, and so on. This data-collection process involves a restriction on randomization, which is referred to as split-plot experiment. The split-plot data structure must be accounted for in developing property-composition-temperature models and the corresponding uncertainty equations for model predictions. Instead of ordinary least squares (OLS) regression methods, generalized least squares (GLS) regression methods using restricted maximum likelihood (REML) estimation must be used. This article describes the methodology for developing property-composition-temperature models and corresponding prediction uncertainty equations using the GLS/REML regression approach. Viscosity data collected on 197 simulated nuclear waste glasses are used to illustrate the GLS/REML methods for developing a viscosity-composition-temperature model and corresponding equations for model prediction uncertainties. The correct results using GLS/REML regression are compared to the incorrect results obtained using OLS regression

  1. Weibull Parameters Estimation Based on Physics of Failure Model

    DEFF Research Database (Denmark)

    Kostandyan, Erik; Sørensen, John Dalsgaard

    2012-01-01

    Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... for degradation modeling and failure criteria determination. The time dependent accumulated damage is assumed linearly proportional to the time dependent degradation level. It is observed that the deterministic accumulated damage at the level of unity closely estimates the characteristic fatigue life of Weibull...

  2. Estimating varying coefficients for partial differential equation models.

    Science.gov (United States)

    Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J

    2017-09-01

    Partial differential equations (PDEs) are used to model complex dynamical systems in multiple dimensions, and their parameters often have important scientific interpretations. In some applications, PDE parameters are not constant but can change depending on the values of covariates, a feature that we call varying coefficients. We propose a parameter cascading method to estimate varying coefficients in PDE models from noisy data. Our estimates of the varying coefficients are shown to be consistent and asymptotically normally distributed. The performance of our method is evaluated by a simulation study and by an empirical study estimating three varying coefficients in a PDE model arising from LIDAR data. © 2017, The International Biometric Society.

  3. New constraints on modelling the random magnetic field of the MW

    Energy Technology Data Exchange (ETDEWEB)

    Beck, Marcus C.; Nielaba, Peter [Department of Physics, University of Konstanz, Universitätsstr. 10, D-78457 Konstanz (Germany); Beck, Alexander M.; Dolag, Klaus [University Observatory Munich, Scheinerstr. 1, D-81679 Munich (Germany); Beck, Rainer [Max Planck Institute for Radioastronomy, Auf dem Hügel 69, D-53121 Bonn (Germany); Strong, Andrew W., E-mail: marcus.beck@uni-konstanz.de, E-mail: abeck@usm.uni-muenchen.de, E-mail: rbeck@mpifr-bonn.mpg.de, E-mail: dolag@usm.uni-muenchen.de, E-mail: aws@mpe.mpg.de, E-mail: peter.nielaba@uni-konstanz.de [Max Planck Institute for Extraterrestrial Physics, Giessenbachstr. 1, D-85748 Garching (Germany)

    2016-05-01

    We extend the description of the isotropic and anisotropic random component of the small-scale magnetic field within the existing magnetic field model of the Milky Way from Jansson and Farrar, by including random realizations of the small-scale component. Using a magnetic-field power spectrum with Gaussian random fields, the NE2001 model for the thermal electrons and the Galactic cosmic-ray electron distribution from the current GALPROP model we derive full-sky maps for the total and polarized synchrotron intensity as well as the Faraday rotation-measure distribution. While previous work assumed that small-scale fluctuations average out along the line-of-sight or which only computed ensemble averages of random fields, we show that these fluctuations need to be carefully taken into account. Comparing with observational data we obtain not only good agreement with 408 MHz total and WMAP7 22 GHz polarized intensity emission maps, but also an improved agreement with Galactic foreground rotation-measure maps and power spectra, whose amplitude and shape strongly depend on the parameters of the random field. We demonstrate that a correlation length of 0≈22 pc (05 pc being a 5σ lower limit) is needed to match the slope of the observed power spectrum of Galactic foreground rotation-measure maps. Using multiple realizations allows us also to infer errors on individual observables. We find that previously-used amplitudes for random and anisotropic random magnetic field components need to be rescaled by factors of ≈0.3 and 0.6 to account for the new small-scale contributions. Our model predicts a rotation measure of −2.8±7.1 rad/m{sup 2} and 04.4±11. rad/m{sup 2} for the north and south Galactic poles respectively, in good agreement with observations. Applying our model to deflections of ultra-high-energy cosmic rays we infer a mean deflection of ≈3.5±1.1 degree for 60 EeV protons arriving from CenA.

  4. Estimating the decline in excess risk of cerebrovascular disease following quitting smoking--a systematic review based on the negative exponential model.

    Science.gov (United States)

    Lee, Peter N; Fry, John S; Thornton, Alison J

    2014-02-01

    We attempted to quantify the decline in stroke risk following quitting using the negative exponential model, with methodology previously employed for IHD. We identified 22 blocks of RRs (from 13 studies) comparing current smokers, former smokers (by time quit) and never smokers. Corresponding pseudo-numbers of cases and controls/at risk formed the data for model-fitting. We tried to estimate the half-life (H, time since quit when the excess risk becomes half that for a continuing smoker) for each block. The method failed to converge or produced very variable estimates of H in nine blocks with a current smoker RR <1.40. Rejecting these, and combining blocks by amount smoked in one study where problems arose in model-fitting, the final analyses used 11 blocks. Goodness-of-fit was adequate for each block, the combined estimate of H being 4.78(95%CI 2.17-10.50) years. However, considerable heterogeneity existed, unexplained by any factor studied, with the random-effects estimate 3.08(1.32-7.16). Sensitivity analyses allowing for reverse causation or differing assumed times for the final quitting period gave similar results. The estimates of H are similar for stroke and IHD, and the individual estimates similarly heterogeneous. Fitting the model is harder for stroke, due to its weaker association with smoking. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

  5. A random walk model to evaluate autism

    Science.gov (United States)

    Moura, T. R. S.; Fulco, U. L.; Albuquerque, E. L.

    2018-02-01

    A common test administered during neurological examination in children is the analysis of their social communication and interaction across multiple contexts, including repetitive patterns of behavior. Poor performance may be associated with neurological conditions characterized by impairments in executive function, such as the so-called pervasive developmental disorders (PDDs), a particular condition of the autism spectrum disorders (ASDs). Inspired in these diagnosis tools, mainly those related to repetitive movements and behaviors, we studied here how the diffusion regimes of two discrete-time random walkers, mimicking the lack of social interaction and restricted interests developed for children with PDDs, are affected. Our model, which is based on the so-called elephant random walk (ERW) approach, consider that one of the random walker can learn and imitate the microscopic behavior of the other with probability f (1 - f otherwise). The diffusion regimes, measured by the Hurst exponent (H), is then obtained, whose changes may indicate a different degree of autism.

  6. Connectivity ranking of heterogeneous random conductivity models

    Science.gov (United States)

    Rizzo, C. B.; de Barros, F.

    2017-12-01

    To overcome the challenges associated with hydrogeological data scarcity, the hydraulic conductivity (K) field is often represented by a spatial random process. The state-of-the-art provides several methods to generate 2D or 3D random K-fields, such as the classic multi-Gaussian fields or non-Gaussian fields, training image-based fields and object-based fields. We provide a systematic comparison of these models based on their connectivity. We use the minimum hydraulic resistance as a connectivity measure, which it has been found to be strictly correlated with early time arrival of dissolved contaminants. A computationally efficient graph-based algorithm is employed, allowing a stochastic treatment of the minimum hydraulic resistance through a Monte-Carlo approach and therefore enabling the computation of its uncertainty. The results show the impact of geostatistical parameters on the connectivity for each group of random fields, being able to rank the fields according to their minimum hydraulic resistance.

  7. Parameter Estimates in Differential Equation Models for Chemical Kinetics

    Science.gov (United States)

    Winkel, Brian

    2011-01-01

    We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…

  8. Applicability of models to estimate traffic noise for urban roads.

    Science.gov (United States)

    Melo, Ricardo A; Pimentel, Roberto L; Lacerda, Diego M; Silva, Wekisley M

    2015-01-01

    Traffic noise is a highly relevant environmental impact in cities. Models to estimate traffic noise, in turn, can be useful tools to guide mitigation measures. In this paper, the applicability of models to estimate noise levels produced by a continuous flow of vehicles on urban roads is investigated. The aim is to identify which models are more appropriate to estimate traffic noise in urban areas since several models available were conceived to estimate noise from highway traffic. First, measurements of traffic noise, vehicle count and speed were carried out in five arterial urban roads of a brazilian city. Together with geometric measurements of width of lanes and distance from noise meter to lanes, these data were input in several models to estimate traffic noise. The predicted noise levels were then compared to the respective measured counterparts for each road investigated. In addition, a chart showing mean differences in noise between estimations and measurements is presented, to evaluate the overall performance of the models. Measured Leq values varied from 69 to 79 dB(A) for traffic flows varying from 1618 to 5220 vehicles/h. Mean noise level differences between estimations and measurements for all urban roads investigated ranged from -3.5 to 5.5 dB(A). According to the results, deficiencies of some models are discussed while other models are identified as applicable to noise estimations on urban roads in a condition of continuous flow. Key issues to apply such models to urban roads are highlighted.

  9. A cellular automata model of traffic flow with variable probability of randomization

    International Nuclear Information System (INIS)

    Zheng Wei-Fan; Zhang Ji-Ye

    2015-01-01

    Research on the stochastic behavior of traffic flow is important to understand the intrinsic evolution rules of a traffic system. By introducing an interactional potential of vehicles into the randomization step, an improved cellular automata traffic flow model with variable probability of randomization is proposed in this paper. In the proposed model, the driver is affected by the interactional potential of vehicles before him, and his decision-making process is related to the interactional potential. Compared with the traditional cellular automata model, the modeling is more suitable for the driver’s random decision-making process based on the vehicle and traffic situations in front of him in actual traffic. From the improved model, the fundamental diagram (flow–density relationship) is obtained, and the detailed high-density traffic phenomenon is reproduced through numerical simulation. (paper)

  10. Temporal rainfall estimation using input data reduction and model inversion

    Science.gov (United States)

    Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.

    2016-12-01

    Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a

  11. MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION

    Directory of Open Access Journals (Sweden)

    M. Ahmadlou

    2016-06-01

    Full Text Available The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC and total operating characteristics (TOC, by overlaying the map of observed change and the modeled suitability map for land use change (error map. The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth.

  12. GLUE Based Uncertainty Estimation of Urban Drainage Modeling Using Weather Radar Precipitation Estimates

    DEFF Research Database (Denmark)

    Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.

    2011-01-01

    Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...

  13. A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model

    International Nuclear Information System (INIS)

    Wei, Zhongbao; Zhao, Jiyun; Ji, Dongxu; Tseng, King Jet

    2017-01-01

    Highlights: •SOC and capacity are dually estimated with online adapted battery model. •Model identification and state dual estimate are fully decoupled. •Multiple timescales are used to improve estimation accuracy and stability. •The proposed method is verified with lab-scale experiments. •The proposed method is applicable to different battery chemistries. -- Abstract: Reliable online estimation of state of charge (SOC) and capacity is critically important for the battery management system (BMS). This paper presents a multi-timescale method for dual estimation of SOC and capacity with an online identified battery model. The model parameter estimator and the dual estimator are fully decoupled and executed with different timescales to improve the model accuracy and stability. Specifically, the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them. Based on the online adapted battery model, the Kalman filter (KF)-based SOC estimator and RLS-based capacity estimator are formulated and integrated in the form of dual estimation. Experimental results suggest that the proposed method estimates the model parameters, SOC, and capacity in real time with fast convergence and high accuracy. Experiments on both lithium-ion battery and vanadium redox flow battery (VRB) verify the generality of the proposed method on multiple battery chemistries. The proposed method is also compared with other existing methods on the computational cost to reveal its superiority for practical application.

  14. Performances of some estimators of linear model with ...

    African Journals Online (AJOL)

    The estimators are compared by examing the finite properties of estimators namely; sum of biases, sum of absolute biases, sum of variances and sum of the mean squared error of the estimated parameter of the model. Results show that when the autocorrelation level is small (ρ=0.4), the MLGD estimator is best except when ...

  15. Estimation of unemployment rates using small area estimation model by combining time series and cross-sectional data

    Science.gov (United States)

    Muchlisoh, Siti; Kurnia, Anang; Notodiputro, Khairil Anwar; Mangku, I. Wayan

    2016-02-01

    Labor force surveys conducted over time by the rotating panel design have been carried out in many countries, including Indonesia. Labor force survey in Indonesia is regularly conducted by Statistics Indonesia (Badan Pusat Statistik-BPS) and has been known as the National Labor Force Survey (Sakernas). The main purpose of Sakernas is to obtain information about unemployment rates and its changes over time. Sakernas is a quarterly survey. The quarterly survey is designed only for estimating the parameters at the provincial level. The quarterly unemployment rate published by BPS (official statistics) is calculated based on only cross-sectional methods, despite the fact that the data is collected under rotating panel design. The study purpose to estimate a quarterly unemployment rate at the district level used small area estimation (SAE) model by combining time series and cross-sectional data. The study focused on the application and comparison between the Rao-Yu model and dynamic model in context estimating the unemployment rate based on a rotating panel survey. The goodness of fit of both models was almost similar. Both models produced an almost similar estimation and better than direct estimation, but the dynamic model was more capable than the Rao-Yu model to capture a heterogeneity across area, although it was reduced over time.

  16. The Dirichet-Multinomial model for multivariate randomized response data and small samples

    NARCIS (Netherlands)

    Avetisyan, Marianna; Fox, Gerardus J.A.

    2012-01-01

    In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The

  17. A stochastic model for stationary dynamics of prices in real estate markets. A case of random intensity for Poisson moments of prices changes

    Science.gov (United States)

    Rusakov, Oleg; Laskin, Michael

    2017-06-01

    We consider a stochastic model of changes of prices in real estate markets. We suppose that in a book of prices the changes happen in points of jumps of a Poisson process with a random intensity, i.e. moments of changes sequently follow to a random process of the Cox process type. We calculate cumulative mathematical expectations and variances for the random intensity of this point process. In the case that the process of random intensity is a martingale the cumulative variance has a linear grows. We statistically process a number of observations of real estate prices and accept hypotheses of a linear grows for estimations as well for cumulative average, as for cumulative variance both for input and output prises that are writing in the book of prises.

  18. Hydrological model uncertainty due to spatial evapotranspiration estimation methods

    Science.gov (United States)

    Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

    2016-05-01

    Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

  19. Flow, transport and diffusion in random geometries II: applications

    KAUST Repository

    Asinari, Pietro

    2015-01-07

    Multilevel Monte Carlo (MLMC) is an efficient and flexible solution for the propagation of uncertainties in complex models, where an explicit parametrization of the input randomness is not available or too expensive. We present several applications of our MLMC algorithm for flow, transport and diffusion in random heterogeneous materials. The absolute permeability and effective diffusivity (or formation factor) of micro-scale porous media samples are computed and the uncertainty related to the sampling procedures is studied. The algorithm is then extended to the transport problems and multiphase flows for the estimation of dispersion and relative permeability curves. The impact of water drops on random stuctured surfaces, with microfluidics applications to self-cleaning materials, is also studied and simulated. Finally the estimation of new drag correlation laws for poly-dispersed dilute and dense suspensions is presented.

  20. Flow, transport and diffusion in random geometries II: applications

    KAUST Repository

    Asinari, Pietro; Ceglia, Diego; Icardi, Matteo; Prudhomme, Serge; Tempone, Raul

    2015-01-01

    Multilevel Monte Carlo (MLMC) is an efficient and flexible solution for the propagation of uncertainties in complex models, where an explicit parametrization of the input randomness is not available or too expensive. We present several applications of our MLMC algorithm for flow, transport and diffusion in random heterogeneous materials. The absolute permeability and effective diffusivity (or formation factor) of micro-scale porous media samples are computed and the uncertainty related to the sampling procedures is studied. The algorithm is then extended to the transport problems and multiphase flows for the estimation of dispersion and relative permeability curves. The impact of water drops on random stuctured surfaces, with microfluidics applications to self-cleaning materials, is also studied and simulated. Finally the estimation of new drag correlation laws for poly-dispersed dilute and dense suspensions is presented.

  1. Small area estimation for semicontinuous data.

    Science.gov (United States)

    Chandra, Hukum; Chambers, Ray

    2016-03-01

    Survey data often contain measurements for variables that are semicontinuous in nature, i.e. they either take a single fixed value (we assume this is zero) or they have a continuous, often skewed, distribution on the positive real line. Standard methods for small area estimation (SAE) based on the use of linear mixed models can be inefficient for such variables. We discuss SAE techniques for semicontinuous variables under a two part random effects model that allows for the presence of excess zeros as well as the skewed nature of the nonzero values of the response variable. In particular, we first model the excess zeros via a generalized linear mixed model fitted to the probability of a nonzero, i.e. strictly positive, value being observed, and then model the response, given that it is strictly positive, using a linear mixed model fitted on the logarithmic scale. Empirical results suggest that the proposed method leads to efficient small area estimates for semicontinuous data of this type. We also propose a parametric bootstrap method to estimate the MSE of the proposed small area estimator. These bootstrap estimates of the MSE are compared to the true MSE in a simulation study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest

    Science.gov (United States)

    Sadler, J. M.; Goodall, J. L.; Morsy, M. M.; Spencer, K.

    2018-04-01

    Sea level rise has already caused more frequent and severe coastal flooding and this trend will likely continue. Flood prediction is an essential part of a coastal city's capacity to adapt to and mitigate this growing problem. Complex coastal urban hydrological systems however, do not always lend themselves easily to physically-based flood prediction approaches. This paper presents a method for using a data-driven approach to estimate flood severity in an urban coastal setting using crowd-sourced data, a non-traditional but growing data source, along with environmental observation data. Two data-driven models, Poisson regression and Random Forest regression, are trained to predict the number of flood reports per storm event as a proxy for flood severity, given extensive environmental data (i.e., rainfall, tide, groundwater table level, and wind conditions) as input. The method is demonstrated using data from Norfolk, Virginia USA from September 2010 to October 2016. Quality-controlled, crowd-sourced street flooding reports ranging from 1 to 159 per storm event for 45 storm events are used to train and evaluate the models. Random Forest performed better than Poisson regression at predicting the number of flood reports and had a lower false negative rate. From the Random Forest model, total cumulative rainfall was by far the most dominant input variable in predicting flood severity, followed by low tide and lower low tide. These methods serve as a first step toward using data-driven methods for spatially and temporally detailed coastal urban flood prediction.

  3. Estimation of rates-across-sites distributions in phylogenetic substitution models.

    Science.gov (United States)

    Susko, Edward; Field, Chris; Blouin, Christian; Roger, Andrew J

    2003-10-01

    Previous work has shown that it is often essential to account for the variation in rates at different sites in phylogenetic models in order to avoid phylogenetic artifacts such as long branch attraction. In most current models, the gamma distribution is used for the rates-across-sites distributions and is implemented as an equal-probability discrete gamma. In this article, we introduce discrete distribution estimates with large numbers of equally spaced rate categories allowing us to investigate the appropriateness of the gamma model. With large numbers of rate categories, these discrete estimates are flexible enough to approximate the shape of almost any distribution. Likelihood ratio statistical tests and a nonparametric bootstrap confidence-bound estimation procedure based on the discrete estimates are presented that can be used to test the fit of a parametric family. We applied the methodology to several different protein data sets, and found that although the gamma model often provides a good parametric model for this type of data, rate estimates from an equal-probability discrete gamma model with a small number of categories will tend to underestimate the largest rates. In cases when the gamma model assumption is in doubt, rate estimates coming from the discrete rate distribution estimate with a large number of rate categories provide a robust alternative to gamma estimates. An alternative implementation of the gamma distribution is proposed that, for equal numbers of rate categories, is computationally more efficient during optimization than the standard gamma implementation and can provide more accurate estimates of site rates.

  4. Estimation of Motion Vector Fields

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    1993-01-01

    This paper presents an approach to the estimation of 2-D motion vector fields from time varying image sequences. We use a piecewise smooth model based on coupled vector/binary Markov random fields. We find the maximum a posteriori solution by simulated annealing. The algorithm generate sample...... fields by means of stochastic relaxation implemented via the Gibbs sampler....

  5. New distributed fusion filtering algorithm based on covariances over sensor networks with random packet dropouts

    Science.gov (United States)

    Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

    2017-07-01

    This paper studies the distributed fusion estimation problem from multisensor measured outputs perturbed by correlated noises and uncertainties modelled by random parameter matrices. Each sensor transmits its outputs to a local processor over a packet-erasure channel and, consequently, random losses may occur during transmission. Different white sequences of Bernoulli variables are introduced to model the transmission losses. For the estimation, each lost output is replaced by its estimator based on the information received previously, and only the covariances of the processes involved are used, without requiring the signal evolution model. First, a recursive algorithm for the local least-squares filters is derived by using an innovation approach. Then, the cross-correlation matrices between any two local filters is obtained. Finally, the distributed fusion filter weighted by matrices is obtained from the local filters by applying the least-squares criterion. The performance of the estimators and the influence of both sensor uncertainties and transmission losses on the estimation accuracy are analysed in a numerical example.

  6. ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative-quantitative modeling.

    Science.gov (United States)

    Streif, Stefan; Savchenko, Anton; Rumschinski, Philipp; Borchers, Steffen; Findeisen, Rolf

    2012-05-01

    Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if-then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLab(TM)-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/

  7. Estimating genetic covariance functions assuming a parametric correlation structure for environmental effects

    Directory of Open Access Journals (Sweden)

    Meyer Karin

    2001-11-01

    Full Text Available Abstract A random regression model for the analysis of "repeated" records in animal breeding is described which combines a random regression approach for additive genetic and other random effects with the assumption of a parametric correlation structure for within animal covariances. Both stationary and non-stationary correlation models involving a small number of parameters are considered. Heterogeneity in within animal variances is modelled through polynomial variance functions. Estimation of parameters describing the dispersion structure of such model by restricted maximum likelihood via an "average information" algorithm is outlined. An application to mature weight records of beef cow is given, and results are contrasted to those from analyses fitting sets of random regression coefficients for permanent environmental effects.

  8. Joint modeling of ChIP-seq data via a Markov random field model

    NARCIS (Netherlands)

    Bao, Yanchun; Vinciotti, Veronica; Wit, Ernst; 't Hoen, Peter A C

    Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein-binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for

  9. Using satellite-based rainfall estimates for streamflow modelling: Bagmati Basin

    Science.gov (United States)

    Shrestha, M.S.; Artan, Guleid A.; Bajracharya, S.R.; Sharma, R. R.

    2008-01-01

    In this study, we have described a hydrologic modelling system that uses satellite-based rainfall estimates and weather forecast data for the Bagmati River Basin of Nepal. The hydrologic model described is the US Geological Survey (USGS) Geospatial Stream Flow Model (GeoSFM). The GeoSFM is a spatially semidistributed, physically based hydrologic model. We have used the GeoSFM to estimate the streamflow of the Bagmati Basin at Pandhera Dovan hydrometric station. To determine the hydrologic connectivity, we have used the USGS Hydro1k DEM dataset. The model was forced by daily estimates of rainfall and evapotranspiration derived from weather model data. The rainfall estimates used for the modelling are those produced by the National Oceanic and Atmospheric Administration Climate Prediction Centre and observed at ground rain gauge stations. The model parameters were estimated from globally available soil and land cover datasets – the Digital Soil Map of the World by FAO and the USGS Global Land Cover dataset. The model predicted the daily streamflow at Pandhera Dovan gauging station. The comparison of the simulated and observed flows at Pandhera Dovan showed that the GeoSFM model performed well in simulating the flows of the Bagmati Basin.

  10. Estimation and uncertainty of reversible Markov models.

    Science.gov (United States)

    Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank

    2015-11-07

    Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software--http://pyemma.org--as of version 2.0.

  11. Flow, transport and diffusion in random geometries I: a MLMC algorithm

    KAUST Repository

    Canuto, Claudio

    2015-01-07

    Multilevel Monte Carlo (MLMC) is an efficient and flexible solution for the propagation of uncertainties in complex models, where an explicit parametrization of the input randomness is not available or too expensive. We propose a general-purpose algorithm and computational code for the solution of Partial Differential Equations (PDEs) on random geoemtry and with random parameters. We make use of the key idea of MLMC, based on different discretization levels, extending it in a more general context, making use of a hierarchy of physical resolution scales, solvers, models and other numerical/geometrical discretization parameters. Modifications of the classical MLMC estimators are proposed to further reduce variance in cases where analytical convergence rates and asymptotic regimes are not available. Spheres, ellipsoids and general convex-shaped grains are placed randomly in the domain with different placing/packing algorithms and the effective properties of the heterogeneous medium are computed. These are, for example, effective diffusivities, conductivities, and reaction rates. The implementation of the Monte-Carlo estimators, the statistical samples and each single solver is done efficiently in parallel.

  12. The problem of multicollinearity in horizontal solar radiation estimation models and a new model for Turkey

    International Nuclear Information System (INIS)

    Demirhan, Haydar

    2014-01-01

    Highlights: • Impacts of multicollinearity on solar radiation estimation models are discussed. • Accuracy of existing empirical models for Turkey is evaluated. • A new non-linear model for the estimation of average daily horizontal global solar radiation is proposed. • Estimation and prediction performance of the proposed and existing models are compared. - Abstract: Due to the considerable decrease in energy resources and increasing energy demand, solar energy is an appealing field of investment and research. There are various modelling strategies and particular models for the estimation of the amount of solar radiation reaching at a particular point over the Earth. In this article, global solar radiation estimation models are taken into account. To emphasize severity of multicollinearity problem in solar radiation estimation models, some of the models developed for Turkey are revisited. It is observed that these models have been identified as accurate under certain multicollinearity structures, and when the multicollinearity is eliminated, the accuracy of these models is controversial. Thus, a reliable model that does not suffer from multicollinearity and gives precise estimates of global solar radiation for the whole region of Turkey is necessary. A new nonlinear model for the estimation of average daily horizontal solar radiation is proposed making use of the genetic programming technique. There is no multicollinearity problem in the new model, and its estimation accuracy is better than the revisited models in terms of numerous statistical performance measures. According to the proposed model, temperature, precipitation, altitude, longitude, and monthly average daily extraterrestrial horizontal solar radiation have significant effect on the average daily global horizontal solar radiation. Relative humidity and soil temperature are not included in the model due to their high correlation with precipitation and temperature, respectively. While altitude has

  13. Generalized Whittle-Matern random field as a model of correlated fluctuations

    International Nuclear Information System (INIS)

    Lim, S C; Teo, L P

    2009-01-01

    This paper considers a generalization of the Gaussian random field with covariance function of the Whittle-Matern family. Such a random field can be obtained as the solution to the fractional stochastic differential equation with two fractional orders. Asymptotic properties of the covariance functions belonging to this generalized Whittle-Matern family are studied, which are used to deduce the sample path properties of the random field. The Whittle-Matern field has been widely used in modeling geostatistical data such as sea beam data, wind speed, field temperature and soil data. In this paper we show that the generalized Whittle-Matern field provides a more flexible model for wind speed data

  14. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2009-01-01

    In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

  15. Efficient estimation of an additive quantile regression model

    NARCIS (Netherlands)

    Cheng, Y.; de Gooijer, J.G.; Zerom, D.

    2010-01-01

    In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

  16. Using Structured Additive Regression Models to Estimate Risk Factors of Malaria: Analysis of 2010 Malawi Malaria Indicator Survey Data

    Science.gov (United States)

    Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

    2014-01-01

    Background After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. Methods We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Results Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. Conclusions The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities. PMID:24991915

  17. Modeling of random geometric errors in superconducting magnets with applications to the CERN Large Hadron Collider

    Directory of Open Access Journals (Sweden)

    P. Ferracin

    2000-12-01

    Full Text Available Estimates of random field-shape errors induced by cable mispositioning in superconducting magnets are presented and specific applications to the Large Hadron Collider (LHC main dipoles and quadrupoles are extensively discussed. Numerical simulations obtained with Monte Carlo methods are compared to analytic estimates and are used to interpret the experimental data for the LHC dipole and quadrupole prototypes. The proposed approach can predict the effect of magnet tolerances on geometric components of random field-shape errors, and it is a useful tool to monitor the obtained tolerances during magnet production.

  18. Model to estimate fractal dimension for ion-bombarded materials

    Energy Technology Data Exchange (ETDEWEB)

    Hu, A., E-mail: hu77@purdue.edu; Hassanein, A.

    2014-03-15

    Comprehensive fractal Monte Carlo model ITMC-F (Hu and Hassanein, 2012 [1]) is developed based on the Monte Carlo ion bombardment simulation code, i.e., Ion Transport in Materials and Compounds (ITMC) code (Hassanein, 1985 [2]). The ITMC-F studies the impact of surface roughness on the angular dependence of sputtering yield. Instead of assuming material surfaces to be flat or composed of exact self-similar fractals in simulation, we developed a new method to describe the surface shapes. Random fractal surfaces which are generated by midpoint displacement algorithm and support vector machine algorithm are combined with ITMC. With this new fractal version of ITMC-F, we successfully simulated the angular dependence of sputtering yield for various ion-target combinations, with the input surface roughness exponent directly depicted from experimental data (Hu and Hassanein, 2012 [1]). The ITMC-F code showed good agreement with the experimental data. In advanced, we compare other experimental sputtering yield with the results from ITMC-F to estimate the surface roughness exponent for ion-bombarded material in this research.

  19. Pervasive randomness in physics: an introduction to its modelling and spectral characterisation

    Science.gov (United States)

    Howard, Roy

    2017-10-01

    An introduction to the modelling and spectral characterisation of random phenomena is detailed at a level consistent with a first exposure to the subject at an undergraduate level. A signal framework for defining a random process is provided and this underpins an introduction to common random processes including the Poisson point process, the random walk, the random telegraph signal, shot noise, information signalling random processes, jittered pulse trains, birth-death random processes and Markov chains. An introduction to the spectral characterisation of signals and random processes, via either an energy spectral density or a power spectral density, is detailed. The important case of defining a white noise random process concludes the paper.

  20. [Using log-binomial model for estimating the prevalence ratio].

    Science.gov (United States)

    Ye, Rong; Gao, Yan-hui; Yang, Yi; Chen, Yue

    2010-05-01

    To estimate the prevalence ratios, using a log-binomial model with or without continuous covariates. Prevalence ratios for individuals' attitude towards smoking-ban legislation associated with smoking status, estimated by using a log-binomial model were compared with odds ratios estimated by logistic regression model. In the log-binomial modeling, maximum likelihood method was used when there were no continuous covariates and COPY approach was used if the model did not converge, for example due to the existence of continuous covariates. We examined the association between individuals' attitude towards smoking-ban legislation and smoking status in men and women. Prevalence ratio and odds ratio estimation provided similar results for the association in women since smoking was not common. In men however, the odds ratio estimates were markedly larger than the prevalence ratios due to a higher prevalence of outcome. The log-binomial model did not converge when age was included as a continuous covariate and COPY method was used to deal with the situation. All analysis was performed by SAS. Prevalence ratio seemed to better measure the association than odds ratio when prevalence is high. SAS programs were provided to calculate the prevalence ratios with or without continuous covariates in the log-binomial regression analysis.