A Dynamical Model of Pitch Memory Provides an Improved Basis for Implied Harmony Estimation
Kim, Ji Chul
2017-01-01
Tonal melody can imply vertical harmony through a sequence of tones. Current methods for automatic chord estimation commonly use chroma-based features extracted from audio signals. However, the implied harmony of unaccompanied melodies can be difficult to estimate on the basis of chroma content in the presence of frequent nonchord tones. Here we present a novel approach to automatic chord estimation based on the human perception of pitch sequences. We use cohesion and inhibition between pitches in auditory short-term memory to differentiate chord tones and nonchord tones in tonal melodies. We model short-term pitch memory as a gradient frequency neural network, which is a biologically realistic model of auditory neural processing. The model is a dynamical system consisting of a network of tonotopically tuned nonlinear oscillators driven by audio signals. The oscillators interact with each other through nonlinear resonance and lateral inhibition, and the pattern of oscillatory traces emerging from the interactions is taken as a measure of pitch salience. We test the model with a collection of unaccompanied tonal melodies to evaluate it as a feature extractor for chord estimation. We show that chord tones are selectively enhanced in the response of the model, thereby increasing the accuracy of implied harmony estimation. We also find that, like other existing features for chord estimation, the performance of the model can be improved by using segmented input signals. We discuss possible ways to expand the present model into a full chord estimation system within the dynamical systems framework. PMID:28522983
Kao, S. C.; Shi, X.; Kumar, J.; Ricciuto, D. M.; Mao, J.; Thornton, P. E.
2017-12-01
With the concern of changing hydrologic regime, there is a crucial need to better understand how water availability may change and influence water management decisions in the projected future climate conditions. Despite that surface hydrology has long been simulated by land model within the Earth System modeling (ESM) framework, given the coarser horizontal resolution and lack of engineering-level calibration, raw runoff from ESM is generally discarded by water resource managers when conducting hydro-climate impact assessments. To identify a likely path to improve the credibility of ESM-simulated natural runoff, we conducted regional model simulation using the land component (ALM) of the Accelerated Climate Modeling for Energy (ACME) version 1 focusing on the conterminous United States (CONUS). Two very different forcing data sets, including (1) the conventional 0.5° CRUNCEP (v5, 1901-2013) and (2) the 1-km Daymet (v3, 1980-2013) aggregated to 0.5°, were used to conduct 20th century transient simulation with satellite phenology. Additional meteorologic and hydrologic observations, including PRISM precipitation and U.S. Geological Survey WaterWatch runoff, were used for model evaluation. For various CONUS hydrologic regions (such as Pacific Northwest), we found that Daymet can significantly improve the reasonableness of simulated ALM runoff even without intensive calibration. The large dry bias of CRUNCEP precipitation (evaluated by PRISM) in multiple CONUS hydrologic regions is believed to be the main reason causing runoff underestimation. The results suggest that when driving with skillful precipitation estimates, ESM has the ability to produce reasonable natural runoff estimates to support further water management studies. Nevertheless, model calibration will be required for regions (such as Upper Colorado) where ill performance is showed for multiple different forcings.
Estimated Nutritive Value of Low-Price Model Lunch Sets Provided to Garment Workers in Cambodia.
Makurat, Jan; Pillai, Aarati; Wieringa, Frank T; Chamnan, Chhoun; Krawinkel, Michael B
2017-07-21
The establishment of staff canteens is expected to improve the nutritional situation of Cambodian garment workers. The objective of this study is to assess the nutritive value of low-price model lunch sets provided at a garment factory in Phnom Penh, Cambodia. Exemplary lunch sets were served to female workers through a temporary canteen at a garment factory in Phnom Penh. Dish samples were collected repeatedly to examine mean serving sizes of individual ingredients. Food composition tables and NutriSurvey software were used to assess mean amounts and contributions to recommended dietary allowances (RDAs) or adequate intake of energy, macronutrients, dietary fiber, vitamin C (VitC), iron, vitamin A (VitA), folate and vitamin B12 (VitB12). On average, lunch sets provided roughly one third of RDA or adequate intake of energy, carbohydrates, fat and dietary fiber. Contribution to RDA of protein was high (46% RDA). The sets contained a high mean share of VitC (159% RDA), VitA (66% RDA), and folate (44% RDA), but were low in VitB12 (29% RDA) and iron (20% RDA). Overall, lunches satisfied recommendations of caloric content and macronutrient composition. Sets on average contained a beneficial amount of VitC, VitA and folate. Adjustments are needed for a higher iron content. Alternative iron-rich foods are expected to be better suited, compared to increasing portions of costly meat/fish components. Lunch provision at Cambodian garment factories holds the potential to improve food security of workers, approximately at costs of <1 USD/person/day at large scale. Data on quantitative total dietary intake as well as physical activity among workers are needed to further optimize the concept of staff canteens.
Estimated Nutritive Value of Low-Price Model Lunch Sets Provided to Garment Workers in Cambodia
Directory of Open Access Journals (Sweden)
Jan Makurat
2017-07-01
Full Text Available Background: The establishment of staff canteens is expected to improve the nutritional situation of Cambodian garment workers. The objective of this study is to assess the nutritive value of low-price model lunch sets provided at a garment factory in Phnom Penh, Cambodia. Methods: Exemplary lunch sets were served to female workers through a temporary canteen at a garment factory in Phnom Penh. Dish samples were collected repeatedly to examine mean serving sizes of individual ingredients. Food composition tables and NutriSurvey software were used to assess mean amounts and contributions to recommended dietary allowances (RDAs or adequate intake of energy, macronutrients, dietary fiber, vitamin C (VitC, iron, vitamin A (VitA, folate and vitamin B12 (VitB12. Results: On average, lunch sets provided roughly one third of RDA or adequate intake of energy, carbohydrates, fat and dietary fiber. Contribution to RDA of protein was high (46% RDA. The sets contained a high mean share of VitC (159% RDA, VitA (66% RDA, and folate (44% RDA, but were low in VitB12 (29% RDA and iron (20% RDA. Conclusions: Overall, lunches satisfied recommendations of caloric content and macronutrient composition. Sets on average contained a beneficial amount of VitC, VitA and folate. Adjustments are needed for a higher iron content. Alternative iron-rich foods are expected to be better suited, compared to increasing portions of costly meat/fish components. Lunch provision at Cambodian garment factories holds the potential to improve food security of workers, approximately at costs of <1 USD/person/day at large scale. Data on quantitative total dietary intake as well as physical activity among workers are needed to further optimize the concept of staff canteens.
Directory of Open Access Journals (Sweden)
A. I. Khader
2013-05-01
Full Text Available Groundwater contaminated with nitrate poses a serious health risk to infants when this contaminated water is used for culinary purposes. To avoid this health risk, people need to know whether their culinary water is contaminated or not. Therefore, there is a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management options. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision-maker and the expected outcomes from these alternatives. The alternatives include (i ignore the health risk of nitrate-contaminated water, (ii switch to alternative water sources such as bottled water, or (iii implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, contaminant transport processes, and climate (Khader, 2012. The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine, where methemoglobinemia (blue baby syndrome is the main health problem associated with the principal contaminant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not use aquifer water, and whether people get sick from drinking contaminated water
Khader, A. I.; Rosenberg, D. E.; McKee, M.
2013-05-01
Groundwater contaminated with nitrate poses a serious health risk to infants when this contaminated water is used for culinary purposes. To avoid this health risk, people need to know whether their culinary water is contaminated or not. Therefore, there is a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management options. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision-maker and the expected outcomes from these alternatives. The alternatives include (i) ignore the health risk of nitrate-contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, contaminant transport processes, and climate (Khader, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine, where methemoglobinemia (blue baby syndrome) is the main health problem associated with the principal contaminant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs
Khader, A.; Rosenberg, D.; McKee, M.
2012-12-01
Nitrate pollution poses a health risk for infants whose freshwater drinking source is groundwater. This risk creates a need to design an effective groundwater monitoring network, acquire information on groundwater conditions, and use acquired information to inform management. These actions require time, money, and effort. This paper presents a method to estimate the value of information (VOI) provided by a groundwater quality monitoring network located in an aquifer whose water poses a spatially heterogeneous and uncertain health risk. A decision tree model describes the structure of the decision alternatives facing the decision maker and the expected outcomes from these alternatives. The alternatives include: (i) ignore the health risk of nitrate contaminated water, (ii) switch to alternative water sources such as bottled water, or (iii) implement a previously designed groundwater quality monitoring network that takes into account uncertainties in aquifer properties, pollution transport processes, and climate (Khader and McKee, 2012). The VOI is estimated as the difference between the expected costs of implementing the monitoring network and the lowest-cost uninformed alternative. We illustrate the method for the Eocene Aquifer, West Bank, Palestine where methemoglobinemia is the main health problem associated with the principal pollutant nitrate. The expected cost of each alternative is estimated as the weighted sum of the costs and probabilities (likelihoods) associated with the uncertain outcomes resulting from the alternative. Uncertain outcomes include actual nitrate concentrations in the aquifer, concentrations reported by the monitoring system, whether people abide by manager recommendations to use/not-use aquifer water, and whether people get sick from drinking contaminated water. Outcome costs include healthcare for methemoglobinemia, purchase of bottled water, and installation and maintenance of the groundwater monitoring system. At current
Directory of Open Access Journals (Sweden)
Diane Palmer
2017-02-01
Full Text Available Plane-of-array (PoA irradiation data is a requirement to simulate the energetic performance of photovoltaic devices (PVs. Normally, solar data is only available as global horizontal irradiation, for a limited number of locations, and typically in hourly time resolution. One approach to handling this restricted data is to enhance it initially by interpolation to the location of interest; next, it must be translated to PoA data by separately considering the diffuse and the beam components. There are many methods of interpolation. This research selects ordinary kriging as the best performing technique by studying mathematical properties, experimentation and leave-one-out-cross validation. Likewise, a number of different translation models has been developed, most of them parameterised for specific measurement setups and locations. The work presented identifies the optimum approach for the UK on a national scale. The global horizontal irradiation will be split into its constituent parts. Divers separation models were tried. The results of each separation algorithm were checked against measured data distributed across the UK. It became apparent that while there is little difference between procedures (14 Wh/m2 mean bias error (MBE, 12 Wh/m2 root mean square error (RMSE, the Ridley, Boland, Lauret equation (a universal split algorithm consistently performed well. The combined interpolation/separation RMSE is 86 Wh/m2.
Ayyanat, Jayachandran A; Harbour, Catherine; Kumar, Sanjeev; Singh, Manjula
2018-01-05
Many interventions have attempted to increase vulnerable and remote populations' access to ORS and zinc to reduce child mortality from diarrhoea. However, the impact of these interventions is difficult to measure. From 2010 to 15, Micronutrient Initiative (MI), worked with the public sector in Bihar, India to enable community health workers to treat and report uncomplicated child diarrhoea with ORS and zinc. We describe how we estimated programme's impact on child mortality with Lives Saved Tool (LiST) modelling and data from MI's management information system (MIS). This study demonstrates that using LiST modelling and MIS data are viable options for evaluating programmes to reduce child mortality. We used MI's programme monitoring data to estimate coverage rates and LiST modelling software to estimate programme impact on child mortality. Four scenarios estimated the effects of different rates of programme scale-up and programme coverage on estimated child mortality by measuring children's lives saved. The programme saved an estimated 806-975 children under-5 who had diarrhoea during five-year project phase. Increasing ORS and zinc coverage rates to 19.8% & 18.3% respectively under public sector coverage with effective treatment would have increased the programme's impact on child mortality and could have achieved the project goal of saving 4200 children's lives during the five-year programme. Programme monitoring data can be used with LiST modelling software to estimate coverage rates and programme impact on child mortality. This modelling approach may cost less and yield estimates sooner than directly measuring programme impact with population-based surveys. However, users must be cautious about relying on modelled estimates of impact and ensure that the programme monitoring data used is complete and precise about the programme aspects that are modelled. Otherwise, LiST may mis-estimate impact on child mortality. Further, LiST software may require modifications
Methods of statistical model estimation
Hilbe, Joseph
2013-01-01
Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. Th
Cost Calculation Model for Logistics Service Providers
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Zoltán Bokor
2012-11-01
Full Text Available The exact calculation of logistics costs has become a real challenge in logistics and supply chain management. It is essential to gain reliable and accurate costing information to attain efficient resource allocation within the logistics service provider companies. Traditional costing approaches, however, may not be sufficient to reach this aim in case of complex and heterogeneous logistics service structures. So this paper intends to explore the ways of improving the cost calculation regimes of logistics service providers and show how to adopt the multi-level full cost allocation technique in logistics practice. After determining the methodological framework, a sample cost calculation scheme is developed and tested by using estimated input data. Based on the theoretical findings and the experiences of the pilot project it can be concluded that the improved costing model contributes to making logistics costing more accurate and transparent. Moreover, the relations between costs and performances also become more visible, which enhances the effectiveness of logistics planning and controlling significantly
Discrete Choice Models - Estimation of Passenger Traffic
DEFF Research Database (Denmark)
Sørensen, Majken Vildrik
2003-01-01
model, data and estimation are described, with a focus of possibilities/limitations of different techniques. Two special issues of modelling are addressed in further detail, namely data segmentation and estimation of Mixed Logit models. Both issues are concerned with whether individuals can be assumed...... for estimation of choice models). For application of the method an algorithm is provided with a case. Also for the second issue, estimation of Mixed Logit models, a method was proposed. The most commonly used approach to estimate Mixed Logit models, is to employ the Maximum Simulated Likelihood estimation (MSL...... distribution of coefficients were found. All the shapes of distributions found, complied with sound knowledge in terms of which should be uni-modal, sign specific and/or skewed distributions....
Asymptotic Optimality of Estimating Function Estimator for CHARN Model
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Tomoyuki Amano
2012-01-01
Full Text Available CHARN model is a famous and important model in the finance, which includes many financial time series models and can be assumed as the return processes of assets. One of the most fundamental estimators for financial time series models is the conditional least squares (CL estimator. However, recently, it was shown that the optimal estimating function estimator (G estimator is better than CL estimator for some time series models in the sense of efficiency. In this paper, we examine efficiencies of CL and G estimators for CHARN model and derive the condition that G estimator is asymptotically optimal.
Abran, Alain
2015-01-01
Software projects are often late and over-budget and this leads to major problems for software customers. Clearly, there is a serious issue in estimating a realistic, software project budget. Furthermore, generic estimation models cannot be trusted to provide credible estimates for projects as complex as software projects. This book presents a number of examples using data collected over the years from various organizations building software. It also presents an overview of the non-for-profit organization, which collects data on software projects, the International Software Benchmarking Stan
AMEM-ADL Polymer Migration Estimation Model User's Guide
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.
Model-Based Optimizing Control and Estimation Using Modelica Model
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L. Imsland
2010-07-01
Full Text Available This paper reports on experiences from case studies in using Modelica/Dymola models interfaced to control and optimization software, as process models in real time process control applications. Possible applications of the integrated models are in state- and parameter estimation and nonlinear model predictive control. It was found that this approach is clearly possible, providing many advantages over modeling in low-level programming languages. However, some effort is required in making the Modelica models accessible to NMPC software.
Gender differences in pension wealth: estimates using provider data.
Johnson, R W; Sambamoorthi, U; Crystal, S
1999-06-01
Information from pension providers was examined to investigate gender differences in pension wealth at midlife. For full-time wage and salary workers approaching retirement age who had pension coverage, median pension wealth on the current job was 76% greater for men than women. Differences in wages, years of job tenure, and industry between men and women accounted for most of the gender gap in pension wealth on the current job. Less than one third of the wealth difference could not be explained by gender differences in education, demographics, or job characteristics. The less-advantaged employment situation of working women currently in midlife carries over into worse retirement income prospects. However, the gender gap in pensions is likely to narrow in the future as married women's employment experiences increasingly resemble those of men.
Error estimation and adaptive chemical transport modeling
Directory of Open Access Journals (Sweden)
Malte Braack
2014-09-01
Full Text Available We present a numerical method to use several chemical transport models of increasing accuracy and complexity in an adaptive way. In largest parts of the domain, a simplified chemical model may be used, whereas in certain regions a more complex model is needed for accuracy reasons. A mathematically derived error estimator measures the modeling error and provides information where to use more accurate models. The error is measured in terms of output functionals. Therefore, one has to consider adjoint problems which carry sensitivity information. This concept is demonstrated by means of ozone formation and pollution emission.
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.
Model for traffic emissions estimation
Alexopoulos, A.; Assimacopoulos, D.; Mitsoulis, E.
A model is developed for the spatial and temporal evaluation of traffic emissions in metropolitan areas based on sparse measurements. All traffic data available are fully employed and the pollutant emissions are determined with the highest precision possible. The main roads are regarded as line sources of constant traffic parameters in the time interval considered. The method is flexible and allows for the estimation of distributed small traffic sources (non-line/area sources). The emissions from the latter are assumed to be proportional to the local population density as well as to the traffic density leading to local main arteries. The contribution of moving vehicles to air pollution in the Greater Athens Area for the period 1986-1988 is analyzed using the proposed model. Emissions and other related parameters are evaluated. Emissions from area sources were found to have a noticeable share of the overall air pollution.
Directory of Open Access Journals (Sweden)
Marion Hoehn
Full Text Available The effective population size (N(e is proportional to the loss of genetic diversity and the rate of inbreeding, and its accurate estimation is crucial for the monitoring of small populations. Here, we integrate temporal studies of the gecko Oedura reticulata, to compare genetic and demographic estimators of N(e. Because geckos have overlapping generations, our goal was to demographically estimate N(bI, the inbreeding effective number of breeders and to calculate the N(bI/N(a ratio (N(a =number of adults for four populations. Demographically estimated N(bI ranged from 1 to 65 individuals. The mean reduction in the effective number of breeders relative to census size (N(bI/N(a was 0.1 to 1.1. We identified the variance in reproductive success as the most important variable contributing to reduction of this ratio. We used four methods to estimate the genetic based inbreeding effective number of breeders N(bI(gen and the variance effective populations size N(eV(gen estimates from the genotype data. Two of these methods - a temporal moment-based (MBT and a likelihood-based approach (TM3 require at least two samples in time, while the other two were single-sample estimators - the linkage disequilibrium method with bias correction LDNe and the program ONeSAMP. The genetic based estimates were fairly similar across methods and also similar to the demographic estimates excluding those estimates, in which upper confidence interval boundaries were uninformative. For example, LDNe and ONeSAMP estimates ranged from 14-55 and 24-48 individuals, respectively. However, temporal methods suffered from a large variation in confidence intervals and concerns about the prior information. We conclude that the single-sample estimators are an acceptable short-cut to estimate N(bI for species such as geckos and will be of great importance for the monitoring of species in fragmented landscapes.
Robust estimation procedure in panel data model
Energy Technology Data Exchange (ETDEWEB)
Shariff, Nurul Sima Mohamad [Faculty of Science of Technology, Universiti Sains Islam Malaysia (USIM), 71800, Nilai, Negeri Sembilan (Malaysia); Hamzah, Nor Aishah [Institute of Mathematical Sciences, Universiti Malaya, 50630, Kuala Lumpur (Malaysia)
2014-06-19
The panel data modeling has received a great attention in econometric research recently. This is due to the availability of data sources and the interest to study cross sections of individuals observed over time. However, the problems may arise in modeling the panel in the presence of cross sectional dependence and outliers. Even though there are few methods that take into consideration the presence of cross sectional dependence in the panel, the methods may provide inconsistent parameter estimates and inferences when outliers occur in the panel. As such, an alternative method that is robust to outliers and cross sectional dependence is introduced in this paper. The properties and construction of the confidence interval for the parameter estimates are also considered in this paper. The robustness of the procedure is investigated and comparisons are made to the existing method via simulation studies. Our results have shown that robust approach is able to produce an accurate and reliable parameter estimates under the condition considered.
A Derivative Based Estimator for Semiparametric Index Models
Donkers, A.C.D.; Schafgans, M.
2003-01-01
This paper proposes a semiparametric estimator for single- and multiple index models.It provides an extension of the average derivative estimator to the multiple index model setting.The estimator uses the average of the outer product of derivatives and is shown to be root-N consistent and
International Nuclear Information System (INIS)
Lloyd, Colin R; Rebelo, Lisa-Maria; Max Finlayson, C
2013-01-01
The conversion of wetlands to agriculture through drainage and flooding, and the burning of wetland areas for agriculture have important implications for greenhouse gas (GHG) production and changing carbon stocks. However, the estimation of net GHG changes from mitigation practices in agricultural wetlands is complex compared to dryland crops. Agricultural wetlands have more complicated carbon and nitrogen cycles with both above- and below-ground processes and export of carbon via vertical and horizontal movement of water through the wetland. This letter reviews current research methodologies in estimating greenhouse gas production and provides guidance on the provision of robust estimates of carbon sequestration and greenhouse gas emissions in agricultural wetlands through the use of low cost reliable and sustainable measurement, modelling and remote sensing applications. The guidance is highly applicable to, and aimed at, wetlands such as those in the tropics and sub-tropics, where complex research infrastructure may not exist, or agricultural wetlands located in remote regions, where frequent visits by monitoring scientists prove difficult. In conclusion, the proposed measurement-modelling approach provides guidance on an affordable solution for mitigation and for investigating the consequences of wetland agricultural practice on GHG production, ecological resilience and possible changes to agricultural yields, variety choice and farming practice. (letter)
Efficient estimation of semiparametric copula models for bivariate survival data
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.
Nonparametric estimation in models for unobservable heterogeneity
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.
MCMC estimation of multidimensional IRT models
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
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.
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...
Modeling and estimating system availability
International Nuclear Information System (INIS)
Gaver, D.P.; Chu, B.B.
1976-11-01
Mathematical models to infer the availability of various types of more or less complicated systems are described. The analyses presented are probabilistic in nature and consist of three parts: a presentation of various analytic models for availability; a means of deriving approximate probability limits on system availability; and a means of statistical inference of system availability from sparse data, using a jackknife procedure. Various low-order redundant systems are used as examples, but extension to more complex systems is not difficult
Parameter estimation in fractional diffusion models
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...
Directory of Open Access Journals (Sweden)
Guiney Helena
2012-08-01
Full Text Available Abstract Background Valid estimation of dental treatment needed at population level is important for service planning. In many instances, planning is informed by survey data, which provide epidemiologically estimated need from the dental fieldworkers’ perspective. The aim of this paper is to determine the validity of this type of information for planning. A comparison of normative (epidemiologically estimated need for selected treatments, as measured on a randomly-selected representative sample, is compared with treatment actually provided in the population from which the sample was drawn. Methods This paper compares dental treatment need-estimates, from a national survey, with treatment provided within two choice-of-dentist schemes: Scheme 1, a co-payment scheme for employed adults, and Scheme 2, a ‘free’ service for less-well-off adults. Epidemiologically estimated need for extractions, restorations, advanced restorations and denture treatments was recorded for a nationally representative sample in 2000/02. Treatments provided to employed and less-well-off adults were retrieved from the claims databases for both schemes. We used the chi-square test to compare proportions, and the student’s t-test to compare means between the survey and claims databases. Results Among employed adults, the proportion of 35-44-year-olds whose teeth had restorations was greater than estimated as needed in the survey (55.7% vs. 36.7%;p Among less-well-off adults, the proportion of 16-24-year-olds who had teeth extracted was greater than estimated as needed in the survey (27.4% vs. 7.9%;p Conclusions Significant differences were found between epidemiologically estimated need and treatment provided for selected treatments, which may be accounted for by measurement differences. The gap between epidemiologically estimated need and treatment provided seems to be greatest for less-well-off adults.
Carbon footprint estimator, phase II : volume I - GASCAP model.
2014-03-01
The GASCAP model was developed to provide a software tool for analysis of the life-cycle GHG : emissions associated with the construction and maintenance of transportation projects. This phase : of development included techniques for estimating emiss...
TPmsm: Estimation of the Transition Probabilities in 3-State Models
Directory of Open Access Journals (Sweden)
Artur Araújo
2014-12-01
Full Text Available One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen 1978. However, two problems may arise from using this estimator: first, its standard error may be large in heavy censored scenarios; second, the estimator may be inconsistent if the process is non-Markovian. The development of the R package TPmsm has been motivated by several recent contributions that account for these estimation problems. Estimation and statistical inference for transition probabilities can be performed using TPmsm. The TPmsm package provides seven different approaches to three-state illness-death modeling. In two of these approaches the transition probabilities are estimated conditionally on current or past covariate measures. Two real data examples are included for illustration of software usage.
National Water Model: Providing the Nation with Actionable Water Intelligence
Aggett, G. R.; Bates, B.
2017-12-01
The National Water Model (NWM) provides national, street-level detail of water movement through time and space. Operating hourly, this flood of information offers enormous benefits in the form of water resource management, natural disaster preparedness, and the protection of life and property. The Geo-Intelligence Division at the NOAA National Water Center supplies forecasters and decision-makers with timely, actionable water intelligence through the processing of billions of NWM data points every hour. These datasets include current streamflow estimates, short and medium range streamflow forecasts, and many other ancillary datasets. The sheer amount of NWM data produced yields a dataset too large to allow for direct human comprehension. As such, it is necessary to undergo model data post-processing, filtering, and data ingestion by visualization web apps that make use of cartographic techniques to bring attention to the areas of highest urgency. This poster illustrates NWM output post-processing and cartographic visualization techniques being developed and employed by the Geo-Intelligence Division at the NOAA National Water Center to provide national actionable water intelligence.
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.
Temporal rainfall estimation using input data reduction and model inversion
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
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
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...
Estimation in autoregressive models with Markov regime
Ríos, Ricardo; Rodríguez, Luis
2005-01-01
In this paper we derive the consistency of the penalized likelihood method for the number state of the hidden Markov chain in autoregressive models with Markov regimen. Using a SAEM type algorithm to estimate the models parameters. We test the null hypothesis of hidden Markov Model against an autoregressive process with Markov regime.
Maneuver Estimation Model for Geostationary Orbit Determination
National Research Council Canada - National Science Library
Hirsch, Brian J
2006-01-01
.... The Clohessy-Wiltshire equations were used to model the relative motion of a geostationary satellite about its intended location, and a nonlinear least squares algorithm was developed to estimate the satellite trajectories.
Shafrin, Jason; Schwartz, Taylor T; Lakdawalla, Darius N; Forma, Felicia M
2016-11-01
Nonadherence to antipsychotic medication among patients with schizophrenia results in poor symptom management and increased health care and other costs. Despite its health impact, medication adherence remains difficult to accurately assess. New technologies offer the possibility of real-time patient monitoring data on adherence, which may in turn improve clinical decision making. However, the economic benefit of accurate patient drug adherence information (PDAI) has yet to be evaluated. To quantify how more accurate PDAI can generate value to payers by improving health care provider decision making in the treatment of patients with schizophrenia. A 3-step decision tree modeling framework was used to measure the effect of PDAI on annual costs (2016 U.S. dollars) for patients with schizophrenia who initiated therapy with an atypical antipsychotic. The first step classified patients using 3 attributes: adherence to antipsychotic medication, medication tolerance, and response to therapy conditional on medication adherence. The prevalence of each characteristic was determined from claims database analysis and literature reviews. The second step modeled the effect of PDAI on provider treatment decisions based on health care providers' survey responses to schizophrenia case vignettes. In the survey, providers were randomized to vignettes with access to PDAI and with no access. In the third step, the economic implications of alternative provider decisions were identified from published peer-reviewed studies. The simulation model calculated the total economic value of PDAI as the difference between expected annual patient total cost corresponding to provider decisions made with or without PDAI. In claims data, 75.3% of patients with schizophrenia were found to be nonadherent to their antipsychotic medications. Review of the literature revealed that 7% of patients cannot tolerate medication, and 72.9% would respond to antipsychotic medication if adherent. Survey responses by
Groundwater temperature estimation and modeling using hydrogeophysics.
Nguyen, F.; Lesparre, N.; Hermans, T.; Dassargues, A.; Klepikova, M.; Kemna, A.; Caers, J.
2017-12-01
Groundwater temperature may be of use as a state variable proxy for aquifer heat storage, highlighting preferential flow paths, or contaminant remediation monitoring. However, its estimation often relies on scarce temperature data collected in boreholes. Hydrogeophysical methods such as electrical resistivity tomography (ERT) and distributed temperature sensing (DTS) may provide more exhaustive spatial information of the bulk properties of interest than samples from boreholes. If a properly calibrated DTS reading provides direct measurements of the groundwater temperature in the well, ERT requires one to determine the fractional change per degree Celsius. One advantage of this petrophysical relationship is its relative simplicity: the fractional change is often found to be around 0.02 per degree Celcius, and represents mainly the variation of electrical resistivity due to the viscosity effect. However, in presence of chemical and kinetics effects, the variation may also depend on the duration of the test and may neglect reactions occurring between the pore water and the solid matrix. Such effects are not expected to be important for low temperature systems (<30 °C), at least for short experiments. In this contribution, we use different field experiments under natural and forced flow conditions to review developments for the joint use of DTS and ERT to map and monitor the temperature distribution within aquifers, to characterize aquifers in terms of heterogeneity and to better understand processes. We show how temperature time-series measurements might be used to constraint the ERT inverse problem in space and time and how combined ERT-derived and DTS estimation of temperature may be used together with hydrogeological modeling to provide predictions of the groundwater temperature field.
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
Guy, S Z Y; Li, L; Thomson, P C; Hermesch, S
2017-12-01
Environmental descriptors derived from mean performances of contemporary groups (CGs) are assumed to capture any known and unknown environmental challenges. The objective of this paper was to obtain a finer definition of the unknown challenges, by adjusting CG estimates for the known climatic effects of monthly maximum air temperature (MaxT), minimum air temperature (MinT) and monthly rainfall (Rain). As the unknown component could include infection challenges, these refined descriptors may help to better model varying responses of sire progeny to environmental infection challenges for the definition of disease resilience. Data were recorded from 1999 to 2013 at a piggery in south-east Queensland, Australia (n = 31,230). Firstly, CG estimates of average daily gain (ADG) and backfat (BF) were adjusted for MaxT, MinT and Rain, which were fitted as splines. In the models used to derive CG estimates for ADG, MaxT and MinT were significant variables. The models that contained these significant climatic variables had CG estimates with a lower variance compared to models without significant climatic variables. Variance component estimates were similar across all models, suggesting that these significant climatic variables accounted for some known environmental variation captured in CG estimates. No climatic variables were significant in the models used to derive the CG estimates for BF. These CG estimates were used to categorize environments. There was no observable sire by environment interaction (Sire×E) for ADG when using the environmental descriptors based on CG estimates on BF. For the environmental descriptors based on CG estimates of ADG, there was significant Sire×E only when MinT was included in the model (p = .01). Therefore, this new definition of the environment, preadjusted by MinT, increased the ability to detect Sire×E. While the unknown challenges captured in refined CG estimates need verification for infection challenges, this may provide a
Parameter Estimation of Partial Differential Equation Models
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.
Comprehensive Care For Joint Replacement Model - Provider Data
U.S. Department of Health & Human Services — Comprehensive Care for Joint Replacement Model - provider data. This data set includes provider data for two quality measures tracked during an episode of care:...
FUZZY MODELING BY SUCCESSIVE ESTIMATION OF RULES ...
African Journals Online (AJOL)
This paper presents an algorithm for automatically deriving fuzzy rules directly from a set of input-output data of a process for the purpose of modeling. The rules are extracted by a method termed successive estimation. This method is used to generate a model without truncating the number of fired rules, to within user ...
Modelling and parameter estimation of dynamic systems
Raol, JR; Singh, J
2004-01-01
Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system dynamics. The assumed model consists of a finite set of parameters, the values of which are calculated using estimation techniques. Most of the techniques that exist are based on least-square minimization of error between the model response and actual system response. However, with the proliferation of high speed digital computers, elegant and innovative techniques like filter error method, H-infinity and Artificial Neural Networks are finding more and mor
Benefit Estimation Model for Tourist Spaceflights
Goehlich, Robert A.
2003-01-01
It is believed that the only potential means for significant reduction of the recurrent launch cost, which results in a stimulation of human space colonization, is to make the launcher reusable, to increase its reliability, and to make it suitable for new markets such as mass space tourism. But such space projects, that have long range aspects are very difficult to finance, because even politicians would like to see a reasonable benefit during their term in office, because they want to be able to explain this investment to the taxpayer. This forces planners to use benefit models instead of intuitive judgement to convince sceptical decision-makers to support new investments in space. Benefit models provide insights into complex relationships and force a better definition of goals. A new approach is introduced in the paper that allows to estimate the benefits to be expected from a new space venture. The main objective why humans should explore space is determined in this study to ``improve the quality of life''. This main objective is broken down in sub objectives, which can be analysed with respect to different interest groups. Such interest groups are the operator of a space transportation system, the passenger, and the government. For example, the operator is strongly interested in profit, while the passenger is mainly interested in amusement, while the government is primarily interested in self-esteem and prestige. This leads to different individual satisfactory levels, which are usable for the optimisation process of reusable launch vehicles.
Lee, Way Seah; Poo, Muhammad Izzuddin; Nagaraj, Shyamala
2007-12-01
To estimate the cost of an episode of inpatient care and the economic burden of hospitalisation for childhood rotavirus gastroenteritis (GE) in Malaysia. A 12-month prospective, hospital-based study on children less than 14 years of age with rotavirus GE, admitted to University of Malaya Medical Centre, Kuala Lumpur, was conducted in 2002. Data on human resource expenditure, costs of investigations, treatment and consumables were collected. Published estimates on rotavirus disease incidence in Malaysia were searched. Economic burden of hospital care for rotavirus GE in Malaysia was estimated by multiplying the cost of each episode of hospital admission for rotavirus GE with national rotavirus incidence in Malaysia. In 2002, the per capita health expenditure by Malaysian Government was US$71.47. Rotavirus was positive in 85 (22%) of the 393 patients with acute GE admitted during the study period. The median cost of providing inpatient care for an episode of rotavirus GE was US$211.91 (range US$68.50-880.60). The estimated average cases of children hospitalised for rotavirus GE in Malaysia (1999-2000) was 8571 annually. The financial burden of providing inpatient care for rotavirus GE in Malaysian children was estimated to be US$1.8 million (range US$0.6 million-7.5 million) annually. The cost of providing inpatient care for childhood rotavirus GE in Malaysia was estimated to be US$1.8 million annually. The financial burden of rotavirus disease would be higher if cost of outpatient visits, non-medical and societal costs are included.
Statistical and RBF NN models : providing forecasts and risk assessment
Marček, Milan
2009-01-01
Forecast accuracy of economic and financial processes is a popular measure for quantifying the risk in decision making. In this paper, we develop forecasting models based on statistical (stochastic) methods, sometimes called hard computing, and on a soft method using granular computing. We consider the accuracy of forecasting models as a measure for risk evaluation. It is found that the risk estimation process based on soft methods is simplified and less critical to the question w...
Estimation and uncertainty of reversible Markov models.
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.
Estimating Dynamic Equilibrium Models using Macro and Financial Data
DEFF Research Database (Denmark)
Christensen, Bent Jesper; Posch, Olaf; van der Wel, Michel
We show that including financial market data at daily frequency, along with macro series at standard lower frequency, facilitates statistical inference on structural parameters in dynamic equilibrium models. Our continuous-time formulation conveniently accounts for the difference in observation...... frequency. We suggest two approaches for the estimation of structural parameters. The first is a simple regression-based procedure for estimation of the reduced-form parameters of the model, combined with a minimum-distance method for identifying the structural parameters. The second approach uses...... martingale estimating functions to estimate the structural parameters directly through a non-linear optimization scheme. We illustrate both approaches by estimating the stochastic AK model with mean-reverting spot interest rates. We also provide Monte Carlo evidence on the small sample behavior...
Information matrix estimation procedures for cognitive diagnostic models.
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.
Customer-Provider Strategic Alignment: A Maturity Model
Luftman, Jerry; Brown, Carol V.; Balaji, S.
This chapter presents a new model for assessing the maturity of a customer-provider relationship from a collaborative service delivery perspective: the Customer-Provider Strategic Alignment Maturity (CPSAM) Model. This model builds on recent research for effectively managing the customer-provider relationship in IT service outsourcing contexts and a validated model for assessing alignment across internal IT service units and their business customers within the same organization. After reviewing relevant literature by service science and information systems researchers, the six overarching components of the maturity model are presented: value measurements, governance, partnership, communications, human resources and skills, and scope and architecture. A key assumption of the model is that all of the components need be addressed to assess and improve customer-provider alignment. Examples of specific metrics for measuring the maturity level of each component over the five levels of maturity are also presented.
A risk assessment model for selecting cloud service providers
Cayirci, Erdal; Garaga, Alexandr; Santana de Oliveira, Anderson; Roudier, Yves
2016-01-01
The Cloud Adoption Risk Assessment Model is designed to help cloud customers in assessing the risks that they face by selecting a specific cloud service provider. It evaluates background information obtained from cloud customers and cloud service providers to analyze various risk scenarios. This facilitates decision making an selecting the cloud service provider with the most preferable risk profile based on aggregated risks to security, privacy, and service delivery. Based on this model we ...
Parameter and Uncertainty Estimation in Groundwater Modelling
DEFF Research Database (Denmark)
Jensen, Jacob Birk
The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... was applied.Capture zone modelling was conducted on a synthetic stationary 3-dimensional flow problem involving river, surface and groundwater flow. Simulated capture zones were illustrated as likelihood maps and compared with a deterministic capture zones derived from a reference model. The results showed...
Extreme gust wind estimation using mesoscale modeling
DEFF Research Database (Denmark)
Larsén, Xiaoli Guo; Kruger, Andries
2014-01-01
Currently, the existing estimation of the extreme gust wind, e.g. the 50-year winds of 3 s values, in the IEC standard, is based on a statistical model to convert the 1:50-year wind values from the 10 min resolution. This statistical model assumes a Gaussian process that satisfies the classical...... through turbulent eddies. This process is modeled using the mesoscale Weather Forecasting and Research (WRF) model. The gust at the surface is calculated as the largest winds over a layer where the averaged turbulence kinetic energy is greater than the averaged buoyancy force. The experiments have been...
A unified framework for benchmark dose estimation applied to mixed models and model averaging
DEFF Research Database (Denmark)
Ritz, Christian; Gerhard, Daniel; Hothorn, Ludwig A.
2013-01-01
This article develops a framework for benchmark dose estimation that allows intrinsically nonlinear dose-response models to be used for continuous data in much the same way as is already possible for quantal data. This means that the same dose-response model equations may be applied to both...... continuous and quantal data, facilitating benchmark dose estimation in general for a wide range of candidate models commonly used in toxicology. Moreover, the proposed framework provides a convenient means for extending benchmark dose concepts through the use of model averaging and random effects modeling...... provides slightly conservative, yet useful, estimates of benchmark dose lower limit under realistic scenarios....
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...... the two estimation methods without noise correction are studied. Second, a noise robust GMM estimator is constructed by approximating integrated volatility by a realized kernel instead of realized variance. The PBEFs are also recalculated in the noise setting, and the two estimation methods ability...
High-dimensional model estimation and model selection
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.
Estimating the development assistance for health provided to faith-based organizations, 1990-2013.
Directory of Open Access Journals (Sweden)
Annie Haakenstad
Full Text Available Faith-based organizations (FBOs have been active in the health sector for decades. Recently, the role of FBOs in global health has been of increased interest. However, little is known about the magnitude and trends in development assistance for health (DAH channeled through these organizations.Data were collected from the 21 most recent editions of the Report of Voluntary Agencies. These reports provide information on the revenue and expenditure of organizations. Project-level data were also collected and reviewed from the Bill & Melinda Gates Foundation and the Global Fund to Fight AIDS, Tuberculosis and Malaria. More than 1,900 non-governmental organizations received funds from at least one of these three organizations. Background information on these organizations was examined by two independent reviewers to identify the amount of funding channeled through FBOs.In 2013, total spending by the FBOs identified in the VolAg amounted to US$1.53 billion. In 1990, FB0s spent 34.1% of total DAH provided by private voluntary organizations reported in the VolAg. In 2013, FBOs expended 31.0%. Funds provided by the Global Fund to FBOs have grown since 2002, amounting to $80.9 million in 2011, or 16.7% of the Global Fund's contributions to NGOs. In 2011, the Gates Foundation's contributions to FBOs amounted to $7.1 million, or 1.1% of the total provided to NGOs.Development assistance partners exhibit a range of preferences with respect to the amount of funds provided to FBOs. Overall, estimates show that FBOS have maintained a substantial and consistent share over time, in line with overall spending in global health on NGOs. These estimates provide the foundation for further research on the spending trends and effectiveness of FBOs in global health.
Estimating the Development Assistance for Health Provided to Faith-Based Organizations, 1990–2013
Haakenstad, Annie; Johnson, Elizabeth; Graves, Casey; Olivier, Jill; Duff, Jean; Dieleman, Joseph L.
2015-01-01
Background Faith-based organizations (FBOs) have been active in the health sector for decades. Recently, the role of FBOs in global health has been of increased interest. However, little is known about the magnitude and trends in development assistance for health (DAH) channeled through these organizations. Material and Methods Data were collected from the 21 most recent editions of the Report of Voluntary Agencies. These reports provide information on the revenue and expenditure of organizations. Project-level data were also collected and reviewed from the Bill & Melinda Gates Foundation and the Global Fund to Fight AIDS, Tuberculosis and Malaria. More than 1,900 non-governmental organizations received funds from at least one of these three organizations. Background information on these organizations was examined by two independent reviewers to identify the amount of funding channeled through FBOs. Results In 2013, total spending by the FBOs identified in the VolAg amounted to US$1.53 billion. In 1990, FB0s spent 34.1% of total DAH provided by private voluntary organizations reported in the VolAg. In 2013, FBOs expended 31.0%. Funds provided by the Global Fund to FBOs have grown since 2002, amounting to $80.9 million in 2011, or 16.7% of the Global Fund’s contributions to NGOs. In 2011, the Gates Foundation’s contributions to FBOs amounted to $7.1 million, or 1.1% of the total provided to NGOs. Conclusion Development assistance partners exhibit a range of preferences with respect to the amount of funds provided to FBOs. Overall, estimates show that FBOS have maintained a substantial and consistent share over time, in line with overall spending in global health on NGOs. These estimates provide the foundation for further research on the spending trends and effectiveness of FBOs in global health. PMID:26042731
Estimating the development assistance for health provided to faith-based organizations, 1990-2013.
Haakenstad, Annie; Johnson, Elizabeth; Graves, Casey; Olivier, Jill; Duff, Jean; Dieleman, Joseph L
2015-01-01
Faith-based organizations (FBOs) have been active in the health sector for decades. Recently, the role of FBOs in global health has been of increased interest. However, little is known about the magnitude and trends in development assistance for health (DAH) channeled through these organizations. Data were collected from the 21 most recent editions of the Report of Voluntary Agencies. These reports provide information on the revenue and expenditure of organizations. Project-level data were also collected and reviewed from the Bill & Melinda Gates Foundation and the Global Fund to Fight AIDS, Tuberculosis and Malaria. More than 1,900 non-governmental organizations received funds from at least one of these three organizations. Background information on these organizations was examined by two independent reviewers to identify the amount of funding channeled through FBOs. In 2013, total spending by the FBOs identified in the VolAg amounted to US$1.53 billion. In 1990, FB0s spent 34.1% of total DAH provided by private voluntary organizations reported in the VolAg. In 2013, FBOs expended 31.0%. Funds provided by the Global Fund to FBOs have grown since 2002, amounting to $80.9 million in 2011, or 16.7% of the Global Fund's contributions to NGOs. In 2011, the Gates Foundation's contributions to FBOs amounted to $7.1 million, or 1.1% of the total provided to NGOs. Development assistance partners exhibit a range of preferences with respect to the amount of funds provided to FBOs. Overall, estimates show that FBOS have maintained a substantial and consistent share over time, in line with overall spending in global health on NGOs. These estimates provide the foundation for further research on the spending trends and effectiveness of FBOs in global health.
Extreme Earthquake Risk Estimation by Hybrid Modeling
Chavez, M.; Cabrera, E.; Ashworth, M.; Garcia, S.; Emerson, D.; Perea, N.; Salazar, A.; Moulinec, C.
2012-12-01
The estimation of the hazard and the economical consequences i.e. the risk associated to the occurrence of extreme magnitude earthquakes in the neighborhood of urban or lifeline infrastructure, such as the 11 March 2011 Mw 9, Tohoku, Japan, represents a complex challenge as it involves the propagation of seismic waves in large volumes of the earth crust, from unusually large seismic source ruptures up to the infrastructure location. The large number of casualties and huge economic losses observed for those earthquakes, some of which have a frequency of occurrence of hundreds or thousands of years, calls for the development of new paradigms and methodologies in order to generate better estimates, both of the seismic hazard, as well as of its consequences, and if possible, to estimate the probability distributions of their ground intensities and of their economical impacts (direct and indirect losses), this in order to implement technological and economical policies to mitigate and reduce, as much as possible, the mentioned consequences. Herewith, we propose a hybrid modeling which uses 3D seismic wave propagation (3DWP) and neural network (NN) modeling in order to estimate the seismic risk of extreme earthquakes. The 3DWP modeling is achieved by using a 3D finite difference code run in the ~100 thousands cores Blue Gene Q supercomputer of the STFC Daresbury Laboratory of UK, combined with empirical Green function (EGF) techniques and NN algorithms. In particular the 3DWP is used to generate broadband samples of the 3D wave propagation of extreme earthquakes (plausible) scenarios corresponding to synthetic seismic sources and to enlarge those samples by using feed-forward NN. We present the results of the validation of the proposed hybrid modeling for Mw 8 subduction events, and show examples of its application for the estimation of the hazard and the economical consequences, for extreme Mw 8.5 subduction earthquake scenarios with seismic sources in the Mexican
[Using log-binomial model for estimating the prevalence ratio].
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.
Decimative Spectral Estimation with Unconstrained Model Order
Directory of Open Access Journals (Sweden)
Stavroula-Evita Fotinea
2012-01-01
Full Text Available This paper presents a new state-space method for spectral estimation that performs decimation by any factor, it makes use of the full set of data and brings further apart the poles under consideration, while imposing almost no constraints to the size of the Hankel matrix (model order, as decimation increases. It is compared against two previously proposed techniques for spectral estimation (along with derived decimative versions, that lie among the most promising methods in the field of spectroscopy, where accuracy of parameter estimation is of utmost importance. Moreover, it is compared against a state-of-the-art purely decimative method proposed in literature. Experiments performed on simulated NMR signals prove the new method to be more robust, especially for low signal-to-noise ratio.
Estimating Coastal Digital Elevation Model (DEM) Uncertainty
Amante, C.; Mesick, S.
2017-12-01
Integrated bathymetric-topographic digital elevation models (DEMs) are representations of the Earth's solid surface and are fundamental to the modeling of coastal processes, including tsunami, storm surge, and sea-level rise inundation. Deviations in elevation values from the actual seabed or land surface constitute errors in DEMs, which originate from numerous sources, including: (i) the source elevation measurements (e.g., multibeam sonar, lidar), (ii) the interpolative gridding technique (e.g., spline, kriging) used to estimate elevations in areas unconstrained by source measurements, and (iii) the datum transformation used to convert bathymetric and topographic data to common vertical reference systems. The magnitude and spatial distribution of the errors from these sources are typically unknown, and the lack of knowledge regarding these errors represents the vertical uncertainty in the DEM. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) has developed DEMs for more than 200 coastal communities. This study presents a methodology developed at NOAA NCEI to derive accompanying uncertainty surfaces that estimate DEM errors at the individual cell-level. The development of high-resolution (1/9th arc-second), integrated bathymetric-topographic DEMs along the southwest coast of Florida serves as the case study for deriving uncertainty surfaces. The estimated uncertainty can then be propagated into the modeling of coastal processes that utilize DEMs. Incorporating the uncertainty produces more reliable modeling results, and in turn, better-informed coastal management decisions.
Consistent Estimation of Partition Markov Models
Directory of Open Access Journals (Sweden)
Jesús E. García
2017-04-01
Full Text Available The Partition Markov Model characterizes the process by a partition L of the state space, where the elements in each part of L share the same transition probability to an arbitrary element in the alphabet. This model aims to answer the following questions: what is the minimal number of parameters needed to specify a Markov chain and how to estimate these parameters. In order to answer these questions, we build a consistent strategy for model selection which consist of: giving a size n realization of the process, finding a model within the Partition Markov class, with a minimal number of parts to represent the process law. From the strategy, we derive a measure that establishes a metric in the state space. In addition, we show that if the law of the process is Markovian, then, eventually, when n goes to infinity, L will be retrieved. We show an application to model internet navigation patterns.
Los Alamos Waste Management Cost Estimation Model
International Nuclear Information System (INIS)
Matysiak, L.M.; Burns, M.L.
1994-03-01
This final report completes the Los Alamos Waste Management Cost Estimation Project, and includes the documentation of the waste management processes at Los Alamos National Laboratory (LANL) for hazardous, mixed, low-level radioactive solid and transuranic waste, development of the cost estimation model and a user reference manual. The ultimate goal of this effort was to develop an estimate of the life cycle costs for the aforementioned waste types. The Cost Estimation Model is a tool that can be used to calculate the costs of waste management at LANL for the aforementioned waste types, under several different scenarios. Each waste category at LANL is managed in a separate fashion, according to Department of Energy requirements and state and federal regulations. The cost of the waste management process for each waste category has not previously been well documented. In particular, the costs associated with the handling, treatment and storage of the waste have not been well understood. It is anticipated that greater knowledge of these costs will encourage waste generators at the Laboratory to apply waste minimization techniques to current operations. Expected benefits of waste minimization are a reduction in waste volume, decrease in liability and lower waste management costs
Bring Your Own Device - Providing Reliable Model of Data Access
Directory of Open Access Journals (Sweden)
Stąpór Paweł
2016-10-01
Full Text Available The article presents a model of Bring Your Own Device (BYOD as a model network, which provides the user reliable access to network resources. BYOD is a model dynamically developing, which can be applied in many areas. Research network has been launched in order to carry out the test, in which as a service of BYOD model Work Folders service was used. This service allows the user to synchronize files between the device and the server. An access to the network is completed through the wireless communication by the 802.11n standard. Obtained results are shown and analyzed in this article.
Model Year 2012 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2011-11-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles.
Model Year 2011 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2010-11-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles.
Model Year 2013 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2012-12-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles.
Model Year 2017 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2016-11-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles.
Model Year 2018 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2017-12-07
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles.
Conditional shape models for cardiac motion estimation
DEFF Research Database (Denmark)
Metz, Coert; Baka, Nora; Kirisli, Hortense
2010-01-01
We propose a conditional statistical shape model to predict patient specific cardiac motion from the 3D end-diastolic CTA scan. The model is built from 4D CTA sequences by combining atlas based segmentation and 4D registration. Cardiac motion estimation is, for example, relevant in the dynamic...... alignment of pre-operative CTA data with intra-operative X-ray imaging. Due to a trend towards prospective electrocardiogram gating techniques, 4D imaging data, from which motion information could be extracted, is not commonly available. The prediction of motion from shape information is thus relevant...
Software Cost Estimating Models: A Comparative Study of What the Models Estimate
1993-09-01
generate good cost estimates. One model developer best summed up this sentiment by stating: Estimation is not a mechanical process. Art, skill, and...Allocation Perc.uinta~es for SASEY Development Phases Sysieni Conce~pt 7.5% yseS/W Requ~irements Anlysis _________%__ S/W Raq;iirements Analysis 9.0
Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2010-01-01
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing both discrete and continuous variables). On the other hand, estimating an MTE from data has turned out to be a difficult...
Estimating and Forecasting Generalized Fractional Long Memory Stochastic Volatility Models
Directory of Open Access Journals (Sweden)
Shelton Peiris
2017-12-01
Full Text Available This paper considers a flexible class of time series models generated by Gegenbauer polynomials incorporating the long memory in stochastic volatility (SV components in order to develop the General Long Memory SV (GLMSV model. We examine the corresponding statistical properties of this model, discuss the spectral likelihood estimation and investigate the finite sample properties via Monte Carlo experiments. We provide empirical evidence by applying the GLMSV model to three exchange rate return series and conjecture that the results of out-of-sample forecasts adequately confirm the use of GLMSV model in certain financial applications.
Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.
2017-01-01
Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of
Zagatto, A M; Gobatto, C A
2012-08-01
The aim of this study was to verify the validity of the curvature constant parameter (W'), calculated from 2-parameter mathematical equations of critical power model, in estimating the anaerobic capacity and anaerobic work capacity from a table tennis-specific test. Specifically, we aimed to i) compare constants estimated from three critical intensity models in a table tennis-specific test (Cf); ii) correlate each estimated W' with the maximal accumulated oxygen deficit (MAOD); iii) correlate each W' with the total amount of anaerobic work (W ANAER) performed in each exercise bout performed during the Cf test. Nine national-standard male table tennis players participated in the study. MAOD was 63.0(10.8) mL · kg - 1 and W' values were 32.8(6.6) balls for the linear-frequency model, 38.3(6.9) balls for linear-total balls model, 48.7(8.9) balls for Nonlinear-2 parameter model. Estimated W' from the Nonlinear 2-parameter model was significantly different from W' from the other 2 models (P0.13). Thus, W' estimated from the 2-parameter mathematical equations did not correlate with MAOD or W ANAER in table tennis-specific tests, indicating that W' may not provide a strong and valid estimation of anaerobic capacity and anaerobic capacity work. © Georg Thieme Verlag KG Stuttgart · New York.
Estimation of pump operational state with model-based methods
International Nuclear Information System (INIS)
Ahonen, Tero; Tamminen, Jussi; Ahola, Jero; Viholainen, Juha; Aranto, Niina; Kestilae, Juha
2010-01-01
Pumps are widely used in industry, and they account for 20% of the industrial electricity consumption. Since the speed variation is often the most energy-efficient method to control the head and flow rate of a centrifugal pump, frequency converters are used with induction motor-driven pumps. Although a frequency converter can estimate the operational state of an induction motor without external measurements, the state of a centrifugal pump or other load machine is not typically considered. The pump is, however, usually controlled on the basis of the required flow rate or output pressure. As the pump operational state can be estimated with a general model having adjustable parameters, external flow rate or pressure measurements are not necessary to determine the pump flow rate or output pressure. Hence, external measurements could be replaced with an adjustable model for the pump that uses estimates of the motor operational state. Besides control purposes, modelling the pump operation can provide useful information for energy auditing and optimization purposes. In this paper, two model-based methods for pump operation estimation are presented. Factors affecting the accuracy of the estimation methods are analyzed. The applicability of the methods is verified by laboratory measurements and tests in two pilot installations. Test results indicate that the estimation methods can be applied to the analysis and control of pump operation. The accuracy of the methods is sufficient for auditing purposes, and the methods can inform the user if the pump is driven inefficiently.
Nonparametric model assisted model calibrated estimation in two ...
African Journals Online (AJOL)
Nonparametric model assisted model calibrated estimation in two stage survey sampling. RO Otieno, PN Mwita, PN Kihara. Abstract. No Abstract > East African Journal of Statistics Vol. 1 (3) 2007: pp.261-281. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT.
Levels of Interaction Provided by Online Distance Education Models
Alhih, Mohammed; Ossiannilsson, Ebba; Berigel, Muhammet
2017-01-01
Interaction plays a significant role to foster usability and quality in online education. It is one of the quality standard to reveal the evidence of practice in online distance education models. This research study aims to evaluate levels of interaction in the practices of distance education centres. It is aimed to provide online distance…
Estimating a DIF decomposition model using a random-weights linear logistic test model approach.
Paek, Insu; Fukuhara, Hirotaka
2015-09-01
A differential item functioning (DIF) decomposition model separates a testlet item DIF into two sources: item-specific differential functioning and testlet-specific differential functioning. This article provides an alternative model-building framework and estimation approach for a DIF decomposition model that was proposed by Beretvas and Walker (2012). Although their model is formulated under multilevel modeling with the restricted pseudolikelihood estimation method, our approach illustrates DIF decomposition modeling that is directly built upon the random-weights linear logistic test model framework with the marginal maximum likelihood estimation method. In addition to demonstrating our approach's performance, we provide detailed information on how to implement this new DIF decomposition model using an item response theory software program; using DIF decomposition may be challenging for practitioners, yet practical information on how to implement it has previously been unavailable in the measurement literature.
Models and estimation methods for clinical HIV-1 data
Verotta, Davide
2005-12-01
Clinical HIV-1 data include many individual factors, such as compliance to treatment, pharmacokinetics, variability in respect to viral dynamics, race, sex, income, etc., which might directly influence or be associated with clinical outcome. These factors need to be taken into account to achieve a better understanding of clinical outcome and mathematical models can provide a unifying framework to do so. The first objective of this paper is to demonstrate the development of comprehensive HIV-1 dynamics models that describe viral dynamics and also incorporate different factors influencing such dynamics. The second objective of this paper is to describe alternative estimation methods that can be applied to the analysis of data with such models. In particular, we consider: (i) simple but effective two-stage estimation methods, in which data from each patient are analyzed separately and summary statistics derived from the results, (ii) more complex nonlinear mixed effect models, used to pool all the patient data in a single analysis. Bayesian estimation methods are also considered, in particular: (iii) maximum posterior approximations, MAP, and (iv) Markov chain Monte Carlo, MCMC. Bayesian methods incorporate prior knowledge into the models, thus avoiding some of the model simplifications introduced when the data are analyzed using two-stage methods, or a nonlinear mixed effect framework. We demonstrate the development of the models and the different estimation methods using real AIDS clinical trial data involving patients receiving multiple drugs regimens.
Modeling, estimation and optimal filtration in signal processing
Najim, Mohamed
2010-01-01
The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the
Linear Regression Models for Estimating True Subsurface ...
Indian Academy of Sciences (India)
47
The objective is to minimize the processing time and computer memory required .... Survey. 65 time to acquire extra GPR or seismic data for large sites and picking the first arrival time. 66 to provide the needed datasets for the joint inversion are also .... The data utilized for the regression modelling was acquired from ground.
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Energy Technology Data Exchange (ETDEWEB)
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
Model of Providing Assistive Technologies in Special Education Schools.
Lersilp, Suchitporn; Putthinoi, Supawadee; Chakpitak, Nopasit
2015-05-14
Most students diagnosed with disabilities in Thai special education schools received assistive technologies, but this did not guarantee the greatest benefits. The purpose of this study was to survey the provision, use and needs of assistive technologies, as well as the perspectives of key informants regarding a model of providing them in special education schools. The participants were selected by the purposive sampling method, and they comprised 120 students with visual, physical, hearing or intellectual disabilities from four special education schools in Chiang Mai, Thailand; and 24 key informants such as parents or caregivers, teachers, school principals and school therapists. The instruments consisted of an assistive technology checklist and a semi-structured interview. Results showed that a category of assistive technologies was provided for students with disabilities, with the highest being "services", followed by "media" and then "facilities". Furthermore, mostly students with physical disabilities were provided with assistive technologies, but those with visual disabilities needed it more. Finally, the model of providing assistive technologies was composed of 5 components: Collaboration; Holistic perspective; Independent management of schools; Learning systems and a production manual for users; and Development of an assistive technology center, driven by 3 major sources such as Government and Private organizations, and Schools.
A single model procedure for estimating tank calibration equations
International Nuclear Information System (INIS)
Liebetrau, A.M.
1997-10-01
A fundamental component of any accountability system for nuclear materials is a tank calibration equation that relates the height of liquid in a tank to its volume. Tank volume calibration equations are typically determined from pairs of height and volume measurements taken in a series of calibration runs. After raw calibration data are standardized to a fixed set of reference conditions, the calibration equation is typically fit by dividing the data into several segments--corresponding to regions in the tank--and independently fitting the data for each segment. The estimates obtained for individual segments must then be combined to obtain an estimate of the entire calibration function. This process is tedious and time-consuming. Moreover, uncertainty estimates may be misleading because it is difficult to properly model run-to-run variability and between-segment correlation. In this paper, the authors describe a model whose parameters can be estimated simultaneously for all segments of the calibration data, thereby eliminating the need for segment-by-segment estimation. The essence of the proposed model is to define a suitable polynomial to fit to each segment and then extend its definition to the domain of the entire calibration function, so that it (the entire calibration function) can be expressed as the sum of these extended polynomials. The model provides defensible estimates of between-run variability and yields a proper treatment of between-segment correlations. A portable software package, called TANCS, has been developed to facilitate the acquisition, standardization, and analysis of tank calibration data. The TANCS package was used for the calculations in an example presented to illustrate the unified modeling approach described in this paper. With TANCS, a trial calibration function can be estimated and evaluated in a matter of minutes
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.
A software for parameter estimation in dynamic models
Directory of Open Access Journals (Sweden)
M. Yuceer
2008-12-01
Full Text Available A common problem in dynamic systems is to determine parameters in an equation used to represent experimental data. The goal is to determine the values of model parameters that provide the best fit to measured data, generally based on some type of least squares or maximum likelihood criterion. In the most general case, this requires the solution of a nonlinear and frequently non-convex optimization problem. Some of the available software lack in generality, while others do not provide ease of use. A user-interactive parameter estimation software was needed for identifying kinetic parameters. In this work we developed an integration based optimization approach to provide a solution to such problems. For easy implementation of the technique, a parameter estimation software (PARES has been developed in MATLAB environment. When tested with extensive example problems from literature, the suggested approach is proven to provide good agreement between predicted and observed data within relatively less computing time and iterations.
Multilevel Autoregressive Mediation Models: Specification, Estimation, and Applications.
Zhang, Qian; Wang, Lijuan; Bergeman, C S
2017-11-27
In the current study, extending from the cross-lagged panel models (CLPMs) in Cole and Maxwell (2003), we proposed the multilevel autoregressive mediation models (MAMMs) by allowing the coefficients to differ across individuals. In addition, Level-2 covariates can be included to explain the interindividual differences of mediation effects. Given the complexity of the proposed models, Bayesian estimation was used. Both a CLPM and an unconditional MAMM were fitted to daily diary data. The 2 models yielded different statistical conclusions regarding the average mediation effect. A simulation study was conducted to examine the estimation accuracy of Bayesian estimation for MAMMs and consequences of model mis-specifications. Factors considered included the sample size (N), number of time points (T), fixed indirect and direct effect sizes, and Level-2 variances and covariances. Results indicated that the fixed effect estimates for the indirect effect components (a and b) and the fixed effects of Level-2 covariates were accurate when N ≥ 50 and T ≥ 5. For estimating Level-2 variances and covariances, they were accurate provided a sufficiently large N and T (e.g., N ≥ 500 and T ≥ 50). Estimates of the average mediation effect were generally accurate when N ≥ 100 and T ≥ 10, or N ≥ 50 and T ≥ 20. Furthermore, we found that when Level-2 variances were zero, MAMMs yielded valid inferences about the fixed effects, whereas when random effects existed, CLPMs had low coverage rates for fixed effects. DIC can be used for model selection. Limitations and future directions were discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Estimators for longitudinal latent exposure models: examining measurement model assumptions.
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.
Robust estimation of errors-in-variables models using M-estimators
Guo, Cuiping; Peng, Junhuan
2017-07-01
The traditional Errors-in-variables (EIV) models are widely adopted in applied sciences. The EIV model estimators, however, can be highly biased by gross error. This paper focuses on robust estimation in EIV models. A new class of robust estimators, called robust weighted total least squared estimators (RWTLS), is introduced. Robust estimators of the parameters of the EIV models are derived from M-estimators and Lagrange multiplier method. A simulated example is carried out to demonstrate the performance of the presented RWTLS. The result shows that the RWTLS algorithm can indeed resist gross error to achieve a reliable solution.
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
A logical model provides insights into T cell receptor signaling.
Directory of Open Access Journals (Sweden)
Julio Saez-Rodriguez
2007-08-01
Full Text Available Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.
Estimating Predictive Variance for Statistical Gas Distribution Modelling
International Nuclear Information System (INIS)
Lilienthal, Achim J.; Asadi, Sahar; Reggente, Matteo
2009-01-01
Recent publications in statistical gas distribution modelling have proposed algorithms that model mean and variance of a distribution. This paper argues that estimating the predictive concentration variance entails not only a gradual improvement but is rather a significant step to advance the field. This is, first, since the models much better fit the particular structure of gas distributions, which exhibit strong fluctuations with considerable spatial variations as a result of the intermittent character of gas dispersal. Second, because estimating the predictive variance allows to evaluate the model quality in terms of the data likelihood. This offers a solution to the problem of ground truth evaluation, which has always been a critical issue for gas distribution modelling. It also enables solid comparisons of different modelling approaches, and provides the means to learn meta parameters of the model, to determine when the model should be updated or re-initialised, or to suggest new measurement locations based on the current model. We also point out directions of related ongoing or potential future research work.
Governance, Government, and the Search for New Provider Models
Directory of Open Access Journals (Sweden)
Richard B. Saltman
2016-01-01
Full Text Available A central problem in designing effective models of provider governance in health systems has been to ensure an appropriate balance between the concerns of public sector and/or government decision-makers, on the one hand, and of non-governmental health services actors in civil society and private life, on the other. In tax-funded European health systems up to the 1980s, the state and other public sector decision-makers played a dominant role over health service provision, typically operating hospitals through national or regional governments on a command-and-control basis. In a number of countries, however, this state role has started to change, with governments first stepping out of direct service provision and now de facto pushed to focus more on steering provider organizations rather than on direct public management. In this new approach to provider governance, the state has pulled back into a regulatory role that introduces market-like incentives and management structures, which then apply to both public and private sector providers alike. This article examines some of the main operational complexities in implementing this new governance reality/strategy, specifically from a service provision (as opposed to mostly a financing or even regulatory perspective. After briefly reviewing some of the key theoretical dilemmas, the paper presents two case studies where this new approach was put into practice: primary care in Sweden and hospitals in Spain. The article concludes that good governance today needs to reflect practical operational realities if it is to have the desired effect on health sector reform outcome.
Proposition of a multicriteria model to select logistics services providers
Directory of Open Access Journals (Sweden)
Miriam Catarina Soares Aharonovitz
2014-06-01
Full Text Available This study aims to propose a multicriteria model to select logistics service providers by the development of a decision tree. The methodology consists of a survey, which resulted in a sample of 181 responses. The sample was analyzed using statistic methods, descriptive statistics among them, multivariate analysis, variance analysis, and parametric tests to compare means. Based on these results, it was possible to obtain the decision tree and information to support the multicriteria analysis. The AHP (Analytic Hierarchy Process was applied to determine the data influence and thus ensure better consistency in the analysis. The decision tree categorizes the criteria according to the decision levels (strategic, tactical and operational. Furthermore, it allows to generically evaluate the importance of each criterion in the supplier selection process from the point of view of logistics services contractors.
Model Year 2014 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2013-12-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Model Year 2010 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2009-10-14
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Model Year 2016 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2015-11-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Model Year 2015 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2014-12-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Model Year 2005 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2004-11-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Model Year 2006 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2005-11-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Model Year 2009 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2008-10-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Model Year 2008 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2007-10-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Model Year 2007 Fuel Economy Guide: EPA Fuel Economy Estimates
Energy Technology Data Exchange (ETDEWEB)
None
2007-10-01
The Fuel Economy Guide is published by the U.S. Department of Energy as an aid to consumers considering the purchase of a new vehicle. The Guide lists estimates of miles per gallon (mpg) for each vehicle available for the new model year. These estimates are provided by the U.S. Environmental Protection Agency in compliance with Federal Law. By using this Guide, consumers can estimate the average yearly fuel cost for any vehicle. The Guide is intended to help consumers compare the fuel economy of similarly sized cars, light duty trucks and special purpose vehicles. The vehicles listed have been divided into three classes of cars, three classes of light duty trucks, and three classes of special purpose vehicles.
Negative binomial models for abundance estimation of multiple closed populations
Boyce, Mark S.; MacKenzie, Darry I.; Manly, Bryan F.J.; Haroldson, Mark A.; Moody, David W.
2001-01-01
Counts of uniquely identified individuals in a population offer opportunities to estimate abundance. However, for various reasons such counts may be burdened by heterogeneity in the probability of being detected. Theoretical arguments and empirical evidence demonstrate that the negative binomial distribution (NBD) is a useful characterization for counts from biological populations with heterogeneity. We propose a method that focuses on estimating multiple populations by simultaneously using a suite of models derived from the NBD. We used this approach to estimate the number of female grizzly bears (Ursus arctos) with cubs-of-the-year in the Yellowstone ecosystem, for each year, 1986-1998. Akaike's Information Criteria (AIC) indicated that a negative binomial model with a constant level of heterogeneity across all years was best for characterizing the sighting frequencies of female grizzly bears. A lack-of-fit test indicated the model adequately described the collected data. Bootstrap techniques were used to estimate standard errors and 95% confidence intervals. We provide a Monte Carlo technique, which confirms that the Yellowstone ecosystem grizzly bear population increased during the period 1986-1998.
In-phase and quadrature imbalance modeling, estimation, and compensation
Li, Yabo
2013-01-01
This book provides a unified IQ imbalance model and systematically reviews the existing estimation and compensation schemes. It covers the different assumptions and approaches that lead to many models of IQ imbalance. In wireless communication systems, the In-phase and Quadrature (IQ) modulator and demodulator are usually used as transmitter (TX) and receiver (RX), respectively. For Digital-to-Analog Converter (DAC) and Analog-to-Digital Converter (ADC) limited systems, such as multi-giga-hertz bandwidth millimeter-wave systems, using analog modulator and demodulator is still a low power and l
Do wavelet filters provide more accurate estimates of reverberation times at low frequencies
DEFF Research Database (Denmark)
Sobreira Seoane, Manuel A.; Pérez Cabo, David; Agerkvist, Finn T.
2016-01-01
the continuous wavelet transform (CTW) has been implemented using a Morlet mother function. Although in general, the wavelet filter bank performs better than the usual filters, the influence of decaying modes outside the filter bandwidth on the measurements has been detected, leading to a biased estimation...
Dynamic Diffusion Estimation in Exponential Family Models
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2013-01-01
Roč. 20, č. 11 (2013), s. 1114-1117 ISSN 1070-9908 R&D Projects: GA MŠk 7D12004; GA ČR GA13-13502S Keywords : diffusion estimation * distributed estimation * paremeter estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.639, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0396518.pdf
UAV State Estimation Modeling Techniques in AHRS
Razali, Shikin; Zhahir, Amzari
2017-11-01
Autonomous unmanned aerial vehicle (UAV) system is depending on state estimation feedback to control flight operation. Estimation on the correct state improves navigation accuracy and achieves flight mission safely. One of the sensors configuration used in UAV state is Attitude Heading and Reference System (AHRS) with application of Extended Kalman Filter (EKF) or feedback controller. The results of these two different techniques in estimating UAV states in AHRS configuration are displayed through position and attitude graphs.
Propagation channel characterization, parameter estimation, and modeling for wireless communications
Yin, Xuefeng
2016-01-01
Thoroughly covering channel characteristics and parameters, this book provides the knowledge needed to design various wireless systems, such as cellular communication systems, RFID and ad hoc wireless communication systems. It gives a detailed introduction to aspects of channels before presenting the novel estimation and modelling techniques which can be used to achieve accurate models. To systematically guide readers through the topic, the book is organised in three distinct parts. The first part covers the fundamentals of the characterization of propagation channels, including the conventional single-input single-output (SISO) propagation channel characterization as well as its extension to multiple-input multiple-output (MIMO) cases. Part two focuses on channel measurements and channel data post-processing. Wideband channel measurements are introduced, including the equipment, technology and advantages and disadvantages of different data acquisition schemes. The channel parameter estimation methods are ...
Evaluation of black carbon estimations in global aerosol models
Directory of Open Access Journals (Sweden)
Y. Zhao
2009-11-01
Full Text Available We evaluate black carbon (BC model predictions from the AeroCom model intercomparison project by considering the diversity among year 2000 model simulations and comparing model predictions with available measurements. These model-measurement intercomparisons include BC surface and aircraft concentrations, aerosol absorption optical depth (AAOD retrievals from AERONET and Ozone Monitoring Instrument (OMI and BC column estimations based on AERONET. In regions other than Asia, most models are biased high compared to surface concentration measurements. However compared with (column AAOD or BC burden retreivals, the models are generally biased low. The average ratio of model to retrieved AAOD is less than 0.7 in South American and 0.6 in African biomass burning regions; both of these regions lack surface concentration measurements. In Asia the average model to observed ratio is 0.7 for AAOD and 0.5 for BC surface concentrations. Compared with aircraft measurements over the Americas at latitudes between 0 and 50N, the average model is a factor of 8 larger than observed, and most models exceed the measured BC standard deviation in the mid to upper troposphere. At higher latitudes the average model to aircraft BC ratio is 0.4 and models underestimate the observed BC loading in the lower and middle troposphere associated with springtime Arctic haze. Low model bias for AAOD but overestimation of surface and upper atmospheric BC concentrations at lower latitudes suggests that most models are underestimating BC absorption and should improve estimates for refractive index, particle size, and optical effects of BC coating. Retrieval uncertainties and/or differences with model diagnostic treatment may also contribute to the model-measurement disparity. Largest AeroCom model diversity occurred in northern Eurasia and the remote Arctic, regions influenced by anthropogenic sources. Changing emissions, aging, removal, or optical properties within a single model
Efficient estimation of an additive quantile regression model
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
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 ...
Yackel Adams, A.A.; Skagen, S.K.; Savidge, J.A.
2007-01-01
Many North American prairie bird populations have recently declined, and the causes of these declines remain largely unknown. To determine whether population limitation occurs during breeding, we evaluated the stability of a population of prairie birds using population-specific values for fecundity and postfledging survival. During 2001-2003, we radiomarked 67 female Lark Buntings (Calamospiza melanocorys) to determine annual fecundity and evaluate contributing factors such as nest survival and breeding response (number of breeding attempts and dispersal). Collectively, 67 females built 112 nests (1.67 ± 0.07 nests female−1 season−1; range: 1–3); 34 were second nests and 11 were third nests. Daily nest survival estimates were similar for initial and later nests with overall nest survival (DSR19) of 30.7% and 31.7%, respectively. Nest predation was the most common cause of failure (92%). Capture and radiomarking of females did not affect nest survival. Lark Bunting dispersal probabilities increased among females that fledged young from initial nests and females that lost their original nests late in the season. Conservative and liberal estimates of mean annual fecundity were 0.96 ±0.11 and 1.24 ± 0.09 female offspring per female, respectively. Given the fecundity and juvenile-survival estimates for this population, annual adult survival values of 71–77% are necessary to achieve a stable population. Because adult survival of prairie passerines ranges between 55% and 65%, this study area may not be capable of sustaining a stable population in the absence of immigration. We contrast our population assessment with one that assumes indirect values of fecundity and juvenile survival. To elucidate limiting factors, estimation of population-specific demographic parameters is desirable. We present an approach for selecting species and areas for evaluation of population stability.
On population size estimators in the Poisson mixture model.
Mao, Chang Xuan; Yang, Nan; Zhong, Jinhua
2013-09-01
Estimating population sizes via capture-recapture experiments has enormous applications. The Poisson mixture model can be adopted for those applications with a single list in which individuals appear one or more times. We compare several nonparametric estimators, including the Chao estimator, the Zelterman estimator, two jackknife estimators and the bootstrap estimator. The target parameter of the Chao estimator is a lower bound of the population size. Those of the other four estimators are not lower bounds, and they may produce lower confidence limits for the population size with poor coverage probabilities. A simulation study is reported and two examples are investigated. © 2013, The International Biometric Society.
Remaining lifetime modeling using State-of-Health estimation
Beganovic, Nejra; Söffker, Dirk
2017-08-01
Technical systems and system's components undergo gradual degradation over time. Continuous degradation occurred in system is reflected in decreased system's reliability and unavoidably lead to a system failure. Therefore, continuous evaluation of State-of-Health (SoH) is inevitable to provide at least predefined lifetime of the system defined by manufacturer, or even better, to extend the lifetime given by manufacturer. However, precondition for lifetime extension is accurate estimation of SoH as well as the estimation and prediction of Remaining Useful Lifetime (RUL). For this purpose, lifetime models describing the relation between system/component degradation and consumed lifetime have to be established. In this contribution modeling and selection of suitable lifetime models from database based on current SoH conditions are discussed. Main contribution of this paper is the development of new modeling strategies capable to describe complex relations between measurable system variables, related system degradation, and RUL. Two approaches with accompanying advantages and disadvantages are introduced and compared. Both approaches are capable to model stochastic aging processes of a system by simultaneous adaption of RUL models to current SoH. The first approach requires a priori knowledge about aging processes in the system and accurate estimation of SoH. An estimation of SoH here is conditioned by tracking actual accumulated damage into the system, so that particular model parameters are defined according to a priori known assumptions about system's aging. Prediction accuracy in this case is highly dependent on accurate estimation of SoH but includes high number of degrees of freedom. The second approach in this contribution does not require a priori knowledge about system's aging as particular model parameters are defined in accordance to multi-objective optimization procedure. Prediction accuracy of this model does not highly depend on estimated SoH. This model
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...
Radiation risk estimation based on measurement error models
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.
Model for Estimation of Fuel Consumption of Cruise Ships
Directory of Open Access Journals (Sweden)
Morten Simonsen
2018-04-01
Full Text Available This article presents a model to estimate the energy use and fuel consumption of cruise ships that sail Norwegian waters. Automatic identification system (AIS data and technical information about cruise ships provided input to the model, including service speed, total power, and number of engines. The model was tested against real-world data obtained from a small cruise vessel and both a medium and large cruise ship. It is sensitive to speed and the corresponding engine load profile of the ship. A crucial determinate for total fuel consumption is also associated with hotel functions, which can make a large contribution to the overall energy use of cruise ships. Real-world data fits the model best when ship speed is 70–75% of service speed. With decreased or increased speed, the model tends to diverge from real-world observations. The model gives a proxy for calculation of fuel consumption associated with cruise ships that sail to Norwegian waters and can be used to estimate greenhouse gas emissions and to evaluate energy reduction strategies for cruise ships.
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.
Viviano, Gaetano; Salerno, Franco; Manfredi, Emanuela Chiara; Polesello, Stefano; Valsecchi, Sara; Tartari, Gianni
2014-11-01
Until robust in situ sensors for total phosphorus (TP) are developed, continuous water quality measurements have the potential to be used as surrogates for generating high frequency estimates. Their use has widespread implications for water quality monitoring programmes considering that TP, in particular, is generally recognised as the limiting factor in the process of eutrophication. Surrogate measures for TP concentration, such as turbidity, have proved useful within natural and agricultural contexts, but their predictive capability for urban watersheds is considered more difficult, due to the different sources of TP, though a strict relationship with turbidity/suspended matter has been clearly described even for these environments. In this context, we investigated this still unresolved problem for high frequency estimation of TP concentration in urban environments by monitoring a medium-sized (71 km(2)) urban watershed (Lambro River watershed, north Italy) in which we detected 60 active combined sewer overflows, and an its natural sub-basin for comparison. We found two different relationships between turbidity and TP concentration in the investigated urban watershed that differently describe the prevalence of TP from point sources (domestic wastewaters) or diffuse origin (surface runoff). In this regard, we first characterise the prevailing sources of TP by using a marker for detecting domestic wastewater contamination (caffeine), then we describe the mutual relationships amongst the continuously monitored variables (in our case the occurrence of the First Flush and the clockwise turbidity/discharge hysteresis). Afterwards we discriminate, by observing variables that are continuously monitored (in our case, the discharge and the turbidity), amongst the continuous surrogate records according to their sources. In conclusion, we are able to apply the relevant turbidity/TP regression equations to each turbidity record and, thus, estimate the respective TP
A method for model identification and parameter estimation
International Nuclear Information System (INIS)
Bambach, M; Heinkenschloss, M; Herty, M
2013-01-01
We propose and analyze a new method for the identification of a parameter-dependent model that best describes a given system. This problem arises, for example, in the mathematical modeling of material behavior where several competing constitutive equations are available to describe a given material. In this case, the models are differential equations that arise from the different constitutive equations, and the unknown parameters are coefficients in the constitutive equations. One has to determine the best-suited constitutive equations for a given material and application from experiments. We assume that the true model is one of the N possible parameter-dependent models. To identify the correct model and the corresponding parameters, we can perform experiments, where for each experiment we prescribe an input to the system and observe a part of the system state. Our approach consists of two stages. In the first stage, for each pair of models we determine the experiment, i.e. system input and observation, that best differentiates between the two models, and measure the distance between the two models. Then we conduct N(N − 1) or, depending on the approach taken, N(N − 1)/2 experiments and use the result of the experiments as well as the previously computed model distances to determine the true model. We provide sufficient conditions on the model distances and measurement errors which guarantee that our approach identifies the correct model. Given the model, we identify the corresponding model parameters in the second stage. The problem in the second stage is a standard parameter estimation problem and we use a method suitable for the given application. We illustrate our approach on three examples, including one where the models are elliptic partial differential equations with different parameterized right-hand sides and an example where we identify the constitutive equation in a problem from computational viscoplasticity. (paper)
Estimation of distribution overlap of urn models.
Hampton, Jerrad; Lladser, Manuel E
2012-01-01
A classical problem in statistics is estimating the expected coverage of a sample, which has had applications in gene expression, microbial ecology, optimization, and even numismatics. Here we consider a related extension of this problem to random samples of two discrete distributions. Specifically, we estimate what we call the dissimilarity probability of a sample, i.e., the probability of a draw from one distribution not being observed in [Formula: see text] draws from another distribution. We show our estimator of dissimilarity to be a [Formula: see text]-statistic and a uniformly minimum variance unbiased estimator of dissimilarity over the largest appropriate range of [Formula: see text]. Furthermore, despite the non-Markovian nature of our estimator when applied sequentially over [Formula: see text], we show it converges uniformly in probability to the dissimilarity parameter, and we present criteria when it is approximately normally distributed and admits a consistent jackknife estimator of its variance. As proof of concept, we analyze V35 16S rRNA data to discern between various microbial environments. Other potential applications concern any situation where dissimilarity of two discrete distributions may be of interest. For instance, in SELEX experiments, each urn could represent a random RNA pool and each draw a possible solution to a particular binding site problem over that pool. The dissimilarity of these pools is then related to the probability of finding binding site solutions in one pool that are absent in the other.
Semiparametric Efficient Adaptive Estimation of the PTTGARCH model
Ciccarelli, Nicola
2016-01-01
Financial data sets exhibit conditional heteroskedasticity and asymmetric volatility. In this paper we derive a semiparametric efficient adaptive estimator of a conditional heteroskedasticity and asymmetric volatility GARCH-type model (i.e., the PTTGARCH(1,1) model). Via kernel density estimation of the unknown density function of the innovation and via the Newton-Raphson technique applied on the root-n-consistent quasi-maximum likelihood estimator, we construct a more efficient estimator tha...
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation of struct......This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines and other models applied to fast evaluation...... response during excitation and the geometrical damping related to free vibrations of a hexagonal footing. The optimal order of a lumped-parameter model is determined for each degree of freedom, i.e. horizontal and vertical translation as well as torsion and rocking. In particular, the necessity of coupling...
Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane
2017-01-01
Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...
Small Area Model-Based Estimators Using Big Data Sources
Directory of Open Access Journals (Sweden)
Marchetti Stefano
2015-06-01
Full Text Available The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.
Sathyachandran, S. K.; Roy, D. P.; Boschetti, L.
2014-12-01
The Fire Radiative Power (FRP) [MW] is a measure of the rate of biomass combustion and can be retrieved from ground based and satellite observations using middle infra-red measurements. The temporal integral of FRP is the Fire Radiative Energy (FRE) [MJ] and is related linearly to the total biomass consumption and so pyrogenic emissions. Satellite derived biomass consumption and emissions estimates have been derived conventionally by computing the summed total FRP, or the average FRP (arithmetic average of FRP retrievals), over spatial geographic grids for fixed time periods. These two methods are prone to estimation bias, especially under irregular sampling conditions such as provided by polar-orbiting satellites, because the FRP can vary rapidly in space and time as a function of the fire behavior. Linear temporal integration of FRP taking into account when the FRP values were observed and using the trapezoidal rule for numerical integration has been suggested as an alternate FRE estimation method. In this study FRP data measured rapidly with a dual-band radiometer over eight prescribed fires are used to compute eight FRE values using the sum, mean and trapezoidal estimation approaches under a variety of simulated irregular sampling conditions. The estimated values are compared to biomass consumed measurements for each of the eight fires to provide insights into which method provides more accurate and precise biomass consumption estimates. The three methods are also applied to continental MODIS FRP data to study their differences using polar orbiting satellite data. The research findings indicate that trapezoidal FRP numerical integration provides the most reliable estimator.
Do Lumped-Parameter Models Provide the Correct Geometrical Damping?
DEFF Research Database (Denmark)
Andersen, Lars
2007-01-01
This paper concerns the formulation of lumped-parameter models for rigid footings on homogenous or stratified soil with focus on the horizontal sliding and rocking. Such models only contain a few degrees of freedom, which makes them ideal for inclusion in aero-elastic codes for wind turbines......-parameter models with respect to the prediction of the maximum response during excitation and the geometrical damping related to free vibrations of a footing....
Volatility estimation using a rational GARCH model
Directory of Open Access Journals (Sweden)
Tetsuya Takaishi
2018-03-01
Full Text Available The rational GARCH (RGARCH model has been proposed as an alternative GARCHmodel that captures the asymmetric property of volatility. In addition to the previously proposedRGARCH model, we propose an alternative RGARCH model called the RGARCH-Exp model thatis more stable when dealing with outliers. We measure the performance of the volatility estimationby a loss function calculated using realized volatility as a proxy for true volatility and compare theRGARCH-type models with other asymmetric type models such as the EGARCH and GJR models.We conduct empirical studies of six stocks on the Tokyo Stock Exchange and find that a volatilityestimation using the RGARCH-type models outperforms the GARCH model and is comparable toother asymmetric GARCH models.
Model parameters estimation and sensitivity by genetic algorithms
International Nuclear Information System (INIS)
Marseguerra, Marzio; Zio, Enrico; Podofillini, Luca
2003-01-01
In this paper we illustrate the possibility of extracting qualitative information on the importance of the parameters of a model in the course of a Genetic Algorithms (GAs) optimization procedure for the estimation of such parameters. The Genetic Algorithms' search of the optimal solution is performed according to procedures that resemble those of natural selection and genetics: an initial population of alternative solutions evolves within the search space through the four fundamental operations of parent selection, crossover, replacement, and mutation. During the search, the algorithm examines a large amount of solution points which possibly carries relevant information on the underlying model characteristics. A possible utilization of this information amounts to create and update an archive with the set of best solutions found at each generation and then to analyze the evolution of the statistics of the archive along the successive generations. From this analysis one can retrieve information regarding the speed of convergence and stabilization of the different control (decision) variables of the optimization problem. In this work we analyze the evolution strategy followed by a GA in its search for the optimal solution with the aim of extracting information on the importance of the control (decision) variables of the optimization with respect to the sensitivity of the objective function. The study refers to a GA search for optimal estimates of the effective parameters in a lumped nuclear reactor model of literature. The supporting observation is that, as most optimization procedures do, the GA search evolves towards convergence in such a way to stabilize first the most important parameters of the model and later those which influence little the model outputs. In this sense, besides estimating efficiently the parameters values, the optimization approach also allows us to provide a qualitative ranking of their importance in contributing to the model output. The
Comparison of two intelligent models to estimate the instantaneous ...
Indian Academy of Sciences (India)
Mostafa Zamani Mohiabadi
2017-07-25
Jul 25, 2017 ... 2014) has combined empirical models and a Bayesian neural network (BNN) model to estimate daily global solar radiation on a horizon- tal surface in Ghardaıa, Algeria. In their model, the maximum and minimum air temperatures of the year 2006 have been used to estimate the coefficients of the empirical ...
A Contingent Trip Model for Estimating Rail-trail Demand
Carter J. Betz; John C. Bergstrom; J. Michael Bowker
2003-01-01
The authors develop a contingent trip model to estimate the recreation demand for and value of a potential rail-trail site in north-east Georgia. The contingent trip model is an alternative to travel cost modelling useful for ex ante evaluation of proposed recreation resources or management alternatives. The authors estimate the empirical demand for trips using a...
Using satellites to provide reliable daily water use estimates at field scales
The ability to accurately map daily crop water use over agricultural landscapes at scales resolving individual farm fields is a significant challenge, but it is increasingly relevant in a future scenario of reduced water availability and increased atmospheric demand. Remote sensing provides a robust...
truncSP: An R Package for Estimation of Semi-Parametric Truncated Linear Regression Models
Directory of Open Access Journals (Sweden)
Maria Karlsson
2014-05-01
Full Text Available Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and ?nite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.
Use of econometric models to estimate expenditure shares.
Trogdon, Justin G; Finkelstein, Eric A; Hoerger, Thomas J
2008-08-01
To investigate the use of regression models to calculate disease-specific shares of medical expenditures. Medical Expenditure Panel Survey (MEPS), 2000-2003. Theoretical investigation and secondary data analysis. Condition files used to define the presence of 10 medical conditions. Incremental effects of conditions on expenditures, expressed as a fraction of total expenditures, cannot generally be interpreted as shares. When the presence of one condition increases treatment costs for another condition, summing condition-specific shares leads to double-counting of expenditures. Condition-specific shares generated from multiplicative models should not be summed. We provide an algorithm that allows estimates based on these models to be interpreted as shares and summed across conditions.
Dynamic systems models new methods of parameter and state estimation
2016-01-01
This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraft in flight, by the function of a hidden Markov model used in bioinformatics or speech recognition or when analyzing the dynamics of asset pricing provided by the nonlinear models of financial mathematics. Dynamic Systems Models demonstrates the use of algorithms based on polynomial approximation which have weaker requirements than already-popular iterative methods. Specifically, they do not require a first approximation of a root vector and they allow non-differentiable elements in the vector functions being approximated. The text covers all the points necessary for the understanding and use of polynomial approximation from the mathematical fundamentals, through algorithm development to the application of the method in, for instance, aeroplane flight dynamic...
NEW MODEL FOR SOLAR RADIATION ESTIMATION FROM ...
African Journals Online (AJOL)
Air temperature of monthly mean minimum temperature, maximum temperature and relative humidity obtained from Nigerian Meteorological Agency (NIMET) were used as inputs to the ANFIS model and monthly mean global solar radiation was used as out of the model. Statistical evaluation of the model was done based on ...
Lag space estimation in time series modelling
DEFF Research Database (Denmark)
Goutte, Cyril
1997-01-01
The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...
Model organoids provide new research opportunities for ductal pancreatic cancer
Boj, Sylvia F|info:eu-repo/dai/nl/304074799; Hwang, Chang-Il; Baker, Lindsey A; Engle, Dannielle D; Tuveson, David A; Clevers, Hans|info:eu-repo/dai/nl/07164282X
We recently established organoid models from normal and neoplastic murine and human pancreas tissues. These organoids exhibit ductal- and disease stage-specific characteristics and, after orthotopic transplantation, recapitulate the full spectrum of tumor progression. Pancreatic organoid technology
Comment on Hoffman and Rovine (2007): SPSS MIXED can estimate models with heterogeneous variances.
Weaver, Bruce; Black, Ryan A
2015-06-01
Hoffman and Rovine (Behavior Research Methods, 39:101-117, 2007) have provided a very nice overview of how multilevel models can be useful to experimental psychologists. They included two illustrative examples and provided both SAS and SPSS commands for estimating the models they reported. However, upon examining the SPSS syntax for the models reported in their Table 3, we found no syntax for models 2B and 3B, both of which have heterogeneous error variances. Instead, there is syntax that estimates similar models with homogeneous error variances and a comment stating that SPSS does not allow heterogeneous errors. But that is not correct. We provide SPSS MIXED commands to estimate models 2B and 3B with heterogeneous error variances and obtain results nearly identical to those reported by Hoffman and Rovine in their Table 3. Therefore, contrary to the comment in Hoffman and Rovine's syntax file, SPSS MIXED can estimate models with heterogeneous error variances.
Efficient estimation of an additive quantile regression model
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
Efficient estimation of an additive quantile regression model
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
Probability density estimation in stochastic environmental models using reverse representations
Van den Berg, E.; Heemink, A.W.; Lin, H.X.; Schoenmakers, J.G.M.
2003-01-01
The estimation of probability densities of variables described by systems of stochastic dierential equations has long been done using forward time estimators, which rely on the generation of realizations of the model, forward in time. Recently, an estimator based on the combination of forward and
Performances Of Estimators Of Linear Models With Autocorrelated ...
African Journals Online (AJOL)
The performances of five estimators of linear models with Autocorrelated error terms are compared when the independent variable is autoregressive. The results reveal that the properties of the estimators when the sample size is finite is quite similar to the properties of the estimators when the sample size is infinite although ...
Estimation in the positive stable shared frailty Cox proportional hazards model
DEFF Research Database (Denmark)
Martinussen, Torben; Pipper, Christian Bressen
2005-01-01
model in situations where the correlated survival data show a decreasing association with time. In this paper, we devise a likelihood based estimation procedure for the positive stable shared frailty Cox model, which is expected to obtain high efficiency. The proposed estimator is provided with large...
Mortensen, Stig B; Klim, Søren; Dammann, Bernd; Kristensen, Niels R; Madsen, Henrik; Overgaard, Rune V
2007-10-01
The non-linear mixed-effects model based on stochastic differential equations (SDEs) provides an attractive residual error model, that is able to handle serially correlated residuals typically arising from structural mis-specification of the true underlying model. The use of SDEs also opens up for new tools for model development and easily allows for tracking of unknown inputs and parameters over time. An algorithm for maximum likelihood estimation of the model has earlier been proposed, and the present paper presents the first general implementation of this algorithm. The implementation is done in Matlab and also demonstrates the use of parallel computing for improved estimation times. The use of the implementation is illustrated by two examples of application which focus on the ability of the model to estimate unknown inputs facilitated by the extension to SDEs. The first application is a deconvolution-type estimation of the insulin secretion rate based on a linear two-compartment model for C-peptide measurements. In the second application the model is extended to also give an estimate of the time varying liver extraction based on both C-peptide and insulin measurements.
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.
Estimation of group means when adjusting for covariates in generalized linear models.
Qu, Yongming; Luo, Junxiang
2015-01-01
Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes. Copyright © 2014 John Wiley & Sons, Ltd.
A nonparametric mixture model for cure rate estimation.
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.
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...... 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...
A simulation of water pollution model parameter estimation
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.
Conceptual Models of the Individual Public Service Provider
DEFF Research Database (Denmark)
Andersen, Lotte Bøgh; Bhatti, Yosef; Petersen, Ole Helby
Individual public service providers’ motivation can be conceptualized as either extrinsic, autonomous or prosocial, and the question is how we can best theoretically understand this complexity without losing too much coherence and parsimony. Drawing on Allison’s approach (1969), three perspectives...... are used to gain insight on the motivation of public service providers; namely principal-agent theory, self-determination theory and public service motivation theory. We situate the theoretical discussions in the context of public service providers being transferred to private organizations...... as a consequence of outsourcing by the public sector. Although this empirical setting is interesting in itself, here it serves primarily as grist for a wider discussion on strategies for applying multiple theoretical approaches and crafting a theoretical synthesis. The key contribution of the paper is thus...
Optimal covariance selection for estimation using graphical models
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...
Estimating a Noncompensatory IRT Model Using Metropolis within Gibbs Sampling
Babcock, Ben
2011-01-01
Relatively little research has been conducted with the noncompensatory class of multidimensional item response theory (MIRT) models. A Monte Carlo simulation study was conducted exploring the estimation of a two-parameter noncompensatory item response theory (IRT) model. The estimation method used was a Metropolis-Hastings within Gibbs algorithm…
Estimated Frequency Domain Model Uncertainties used in Robust Controller Design
DEFF Research Database (Denmark)
Tøffner-Clausen, S.; Andersen, Palle; Stoustrup, Jakob
1994-01-01
This paper deals with the combination of system identification and robust controller design. Recent results on estimation of frequency domain model uncertainty are......This paper deals with the combination of system identification and robust controller design. Recent results on estimation of frequency domain model uncertainty are...
Estimating Lead (Pb) Bioavailability In A Mouse Model
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...
ESTIMATION DU MODELE LINEAIRE GENERALISE ET APPLICATION
Directory of Open Access Journals (Sweden)
Malika CHIKHI
2012-06-01
Full Text Available Cet article présente le modèle linéaire généralisé englobant les techniques de modélisation telles que la régression linéaire, la régression logistique, la régression log linéaire et la régression de Poisson . On Commence par la présentation des modèles des lois exponentielles pour ensuite estimer les paramètres du modèle par la méthode du maximum de vraisemblance. Par la suite on teste les coefficients du modèle pour voir leurs significations et leurs intervalles de confiances, en utilisant le test de Wald qui porte sur la signification de la vraie valeur du paramètre basé sur l'estimation de l'échantillon.
FASTSim: A Model to Estimate Vehicle Efficiency, Cost and Performance
Energy Technology Data Exchange (ETDEWEB)
Brooker, A.; Gonder, J.; Wang, L.; Wood, E.; Lopp, S.; Ramroth, L.
2015-05-04
The Future Automotive Systems Technology Simulator (FASTSim) is a high-level advanced vehicle powertrain systems analysis tool supported by the U.S. Department of Energy’s Vehicle Technologies Office. FASTSim provides a quick and simple approach to compare powertrains and estimate the impact of technology improvements on light- and heavy-duty vehicle efficiency, performance, cost, and battery batches of real-world drive cycles. FASTSim’s calculation framework and balance among detail, accuracy, and speed enable it to simulate thousands of driven miles in minutes. The key components and vehicle outputs have been validated by comparing the model outputs to test data for many different vehicles to provide confidence in the results. A graphical user interface makes FASTSim easy and efficient to use. FASTSim is freely available for download from the National Renewable Energy Laboratory’s website (see www.nrel.gov/fastsim).
Bayesian analysis of inflation: Parameter estimation for single field models
International Nuclear Information System (INIS)
Mortonson, Michael J.; Peiris, Hiranya V.; Easther, Richard
2011-01-01
Future astrophysical data sets promise to strengthen constraints on models of inflation, and extracting these constraints requires methods and tools commensurate with the quality of the data. In this paper we describe ModeCode, a new, publicly available code that computes the primordial scalar and tensor power spectra for single-field inflationary models. ModeCode solves the inflationary mode equations numerically, avoiding the slow roll approximation. It is interfaced with CAMB and CosmoMC to compute cosmic microwave background angular power spectra and perform likelihood analysis and parameter estimation. ModeCode is easily extendable to additional models of inflation, and future updates will include Bayesian model comparison. Errors from ModeCode contribute negligibly to the error budget for analyses of data from Planck or other next generation experiments. We constrain representative single-field models (φ n with n=2/3, 1, 2, and 4, natural inflation, and 'hilltop' inflation) using current data, and provide forecasts for Planck. From current data, we obtain weak but nontrivial limits on the post-inflationary physics, which is a significant source of uncertainty in the predictions of inflationary models, while we find that Planck will dramatically improve these constraints. In particular, Planck will link the inflationary dynamics with the post-inflationary growth of the horizon, and thus begin to probe the ''primordial dark ages'' between TeV and grand unified theory scale energies.
Seo, Seongwon; Hwang, Yongwoo
1999-08-01
Construction and demolition (C&D) debris is generated at the site of various construction activities. However, the amount of the debris is usually so large that it is necessary to estimate the amount of C&D debris as accurately as possible for effective waste management and control in urban areas. In this paper, an effective estimation method using a statistical model was proposed. The estimation process was composed of five steps: estimation of the life span of buildings; estimation of the floor area of buildings to be constructed and demolished; calculation of individual intensity units of C&D debris; and estimation of the future C&D debris production. This method was also applied in the city of Seoul as an actual case, and the estimated amount of C&D debris in Seoul in 2021 was approximately 24 million tons. Of this total amount, 98% was generated by demolition, and the main components of debris were concrete and brick.
Experimental studies on power transformer model winding provided with MOVs
Directory of Open Access Journals (Sweden)
G.H. Kusumadevi
2017-05-01
Full Text Available Surge voltage distribution across a HV transformer winding due to appearance of very fast rise time (rise time of order 1 μs transient voltages is highly non-uniform along the length of the winding for initial time instant of occurrence of surge. In order to achieve nearly uniform initial time instant voltage distribution along the length of the HV winding, investigations have been carried out on transformer model winding. By connecting similar type of metal oxide varistors across sections of HV transformer model winding, it is possible to improve initial time instant surge voltage distribution across length of the HV transformer winding. Transformer windings with α values 5.3, 9.5 and 19 have been analyzed. The experimental studies have been carried out using high speed oscilloscope of good accuracy. The initial time instant voltage distribution across sections of winding with MOV remains nearly uniform along length of the winding. Also results of fault diagnostics carried out with and without connection of MOVs across sections of winding are reported.
Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang
2010-07-01
We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.
Incremental parameter estimation of kinetic metabolic network models
Directory of Open Access Journals (Sweden)
Jia Gengjie
2012-11-01
Full Text Available Abstract Background An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE. Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified. Results In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates exceeds that of metabolites (chemical species. Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented. Conclusions The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.
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
Ballistic model to estimate microsprinkler droplet distribution
Directory of Open Access Journals (Sweden)
Conceição Marco Antônio Fonseca
2003-01-01
Full Text Available Experimental determination of microsprinkler droplets is difficult and time-consuming. This determination, however, could be achieved using ballistic models. The present study aimed to compare simulated and measured values of microsprinkler droplet diameters. Experimental measurements were made using the flour method, and simulations using a ballistic model adopted by the SIRIAS computational software. Drop diameters quantified in the experiment varied between 0.30 mm and 1.30 mm, while the simulated between 0.28 mm and 1.06 mm. The greatest differences between simulated and measured values were registered at the highest radial distance from the emitter. The model presented a performance classified as excellent for simulating microsprinkler drop distribution.
A Dynamic Travel Time Estimation Model Based on Connected Vehicles
Directory of Open Access Journals (Sweden)
Daxin Tian
2015-01-01
Full Text Available With advances in connected vehicle technology, dynamic vehicle route guidance models gradually become indispensable equipment for drivers. Traditional route guidance models are designed to direct a vehicle along the shortest path from the origin to the destination without considering the dynamic traffic information. In this paper a dynamic travel time estimation model is presented which can collect and distribute traffic data based on the connected vehicles. To estimate the real-time travel time more accurately, a road link dynamic dividing algorithm is proposed. The efficiency of the model is confirmed by simulations, and the experiment results prove the effectiveness of the travel time estimation method.
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...
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
Conrads, P. A.; Telis, P.; Henkel, H.
2011-12-01
Successful restoration of the Everglades depends on restoring or approximating the former natural volume, timing, and distribution of wetland sheetflow and the corresponding response of the natural system to these changes. The Everglades Depth Estimation Network (EDEN) is a network of over 280 real-time water-level gages maintained by multiple State and Federal agencies including the South Florida Water Management District, the U.S. Geological Survey (USGS), Everglades National Park, and Big Cypress National Preserve (fig. 1). The real-time water-level data is integrated with ground-elevation and real-time water-surface modeling results and provides scientists and managers with current (2000-present), on-line water-depth information for the entire freshwater portion of the greater Everglades. Presented on a 400-square-meter grid spacing, EDEN offers a consistent and documented dataset that can be used by scientists and managers to guide large-scale field operations, integrate hydrologic and ecological responses, and support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan. Data from the multiple agencies are combined in the USGS National Water Information System (NWIS) database and then served real-time by NWISweb to scientists, managers, and the general public. The water-level surfaces are posted on the EDENweb. By combining the daily water-level surfaces with the ground-elevation model and using the EDEN applications, a full suite of hydrologic data is made available to scientists and others including: water depth, hydroperiod, water-surface slope, surface animations of water elevation and water depth over time, and transects of water depth animated over time. EDEN's computations of these important ecological drivers provide biologists and ecologists with the data necessary to examine landscape and trophic-level responses to hydrodynamic changes in the Everglades. In
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
Comparison of Estimation Procedures for Multilevel AR(1 Models
Directory of Open Access Journals (Sweden)
Tanja eKrone
2016-04-01
Full Text Available To estimate a time series model for multiple individuals, a multilevel model may be used.In this paper we compare two estimation methods for the autocorrelation in Multilevel AR(1 models, namely Maximum Likelihood Estimation (MLE and Bayesian Markov Chain Monte Carlo.Furthermore, we examine the difference between modeling fixed and random individual parameters.To this end, we perform a simulation study with a fully crossed design, in which we vary the length of the time series (10 or 25, the number of individuals per sample (10 or 25, the mean of the autocorrelation (-0.6 to 0.6 inclusive, in steps of 0.3 and the standard deviation of the autocorrelation (0.25 or 0.40.We found that the random estimators of the population autocorrelation show less bias and higher power, compared to the fixed estimators. As expected, the random estimators profit strongly from a higher number of individuals, while this effect is small for the fixed estimators.The fixed estimators profit slightly more from a higher number of time points than the random estimators.When possible, random estimation is preferred to fixed estimation.The difference between MLE and Bayesian estimation is nearly negligible. The Bayesian estimation shows a smaller bias, but MLE shows a smaller variability (i.e., standard deviation of the parameter estimates.Finally, better results are found for a higher number of individuals and time points, and for a lower individual variability of the autocorrelation. The effect of the size of the autocorrelation differs between outcome measures.
Linear Regression Models for Estimating True Subsurface ...
Indian Academy of Sciences (India)
47
of the processing time and memory space required to carry out the inversion with the. 29. SCLS algorithm. ... consumption of time and memory space for the iterative computations to converge at. 54 minimum data ..... colour scale and blanking as the observed true resistivity models, for visual assessment. 163. The accuracy ...
Genetic Prediction Models and Heritability Estimates for Functional ...
African Journals Online (AJOL)
This paper discusses these methodologies and their advantages and disadvantages. Heritability estimates obtained from these models are also reviewed. Linear methodologies can model binary and actual longevity, while RR and TM methodologies model binary survival. PH procedures model the hazard function of a cow ...
LBA-ECO LC-08 Ecosystem Demography Model Estimated C, NPP, and Biomass For Amazonia
National Aeronautics and Space Administration — ABSTRACT: This data set provides Ecosystem Demography Model (ED) estimates of potential above-ground net primary production (NPP) (kg C/m2/y), potential average live...
LBA-ECO LC-08 Ecosystem Demography Model Estimated C, NPP, and Biomass For Amazonia
National Aeronautics and Space Administration — This data set provides Ecosystem Demography Model (ED) estimates of potential above-ground net primary production (NPP) (kg C/m2/y), potential average live biomass...
Ferro, Mark A
2014-01-01
The aim of this research was to examine, in an exploratory manner, whether cross-sectional multiple imputation generates valid parameter estimates for a latent growth curve model in a longitudinal data set with nonmonotone missingness. A simulated longitudinal data set of N = 5000 was generated and consisted of a continuous dependent variable, assessed at three measurement occasions and a categorical time-invariant independent variable. Missing data had a nonmonotone pattern and the proportion of missingness increased from the initial to the final measurement occasion (5%-20%). Three methods were considered to deal with missing data: listwise deletion, full-information maximum likelihood, and multiple imputation. A latent growth curve model was specified and analysis of variance was used to compare parameter estimates between the full data set and missing data approaches. Multiple imputation resulted in significantly lower slope variance compared with the full data set. There were no differences in any parameter estimates between the multiple imputation and full-information maximum likelihood approaches. This study suggested that in longitudinal studies with nonmonotone missingness, cross-sectional imputation at each time point may be viable and produces estimates comparable with those obtained with full-information maximum likelihood. Future research pursuing the validity of this method is warranted. Copyright © 2014 Elsevier Inc. All rights reserved.
Warren, John
2009-01-01
Background and Aims There is a widely used crude method to estimate the age of hedgerows (Hooper's rule) based on species' richness. The aim of this study was to try and establish a similar field method for estimating the age of grasslands based on the accumulation of macro-somatic mutations. Methods A countrywide survey was carried out by the British public to investigate the relationship between grassland age and the number of Ranunculus repens (creeping buttercup) plants with extra petals. In addition the relationship between grassland age and R. repens pollen viability was also investigated. Key Results Each plant with flowers with additional petals in a sample of 100 was found to equate to approx. 7 years. A higher significant correlation was observed between pollen viability and population age; however, this is not amenable to providing field estimates. Conclusions The age of British grasslands can be easily and reliably estimated in the field by counting the number flowers with additional petals in R. repens in meadows up to 200 years old. An attempt to estimate the heritability of extra petals suggests that the phenotype results from the slow accumulation of somatic mutations in a species that primarily reproduces vegetatively. PMID:19491088
Warren, John
2009-09-01
There is a widely used crude method to estimate the age of hedgerows (Hooper's rule) based on species' richness. The aim of this study was to try and establish a similar field method for estimating the age of grasslands based on the accumulation of macro-somatic mutations. A countrywide survey was carried out by the British public to investigate the relationship between grassland age and the number of Ranunculus repens (creeping buttercup) plants with extra petals. In addition the relationship between grassland age and R. repens pollen viability was also investigated. Each plant with flowers with additional petals in a sample of 100 was found to equate to approx. 7 years. A higher significant correlation was observed between pollen viability and population age; however, this is not amenable to providing field estimates. The age of British grasslands can be easily and reliably estimated in the field by counting the number flowers with additional petals in R. repens in meadows up to 200 years old. An attempt to estimate the heritability of extra petals suggests that the phenotype results from the slow accumulation of somatic mutations in a species that primarily reproduces vegetatively.
Explicit estimating equations for semiparametric generalized linear latent variable models
Ma, Yanyuan
2010-07-05
We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.
Model for estimating of population abundance using line transect sampling
Abdulraqeb Abdullah Saeed, Gamil; Muhammad, Noryanti; Zun Liang, Chuan; Yusoff, Wan Nur Syahidah Wan; Zuki Salleh, Mohd
2017-09-01
Today, many studies use the nonparametric methods for estimating objects abundance, for the simplicity, the parametric methods are widely used by biometricians. This paper is designed to present the proposed model for estimating of population abundance using line transect technique. The proposed model is appealing because it is strictly monotonically decreasing with perpendicular distance and it satisfies the shoulder conditions. The statistical properties and inference of the proposed model are discussed. In the presented detection function, theoretically, the proposed model is satisfied the line transect assumption, that leads us to study the performance of this model. We use this model as a reference for the future research of density estimation. In this paper we also study the assumption of the detection function and introduce the corresponding model in order to apply the simulation in future work.
Battery Calendar Life Estimator Manual Modeling and Simulation
Energy Technology Data Exchange (ETDEWEB)
Jon P. Christophersen; Ira Bloom; Ed Thomas; Vince Battaglia
2012-10-01
The Battery Life Estimator (BLE) Manual has been prepared to assist developers in their efforts to estimate the calendar life of advanced batteries for automotive applications. Testing requirements and procedures are defined by the various manuals previously published under the United States Advanced Battery Consortium (USABC). The purpose of this manual is to describe and standardize a method for estimating calendar life based on statistical models and degradation data acquired from typical USABC battery testing.
How to use COSMIC Functional Size in Effort Estimation Models?
Gencel, Cigdem
2008-01-01
Although Functional Size Measurement (FSM) methods have become widely used by the software organizations, the functional size based effort estimation still needs further investigation. Most of the studies on effort estimation consider total functional size of the software as the primary input to estimation models and they mostly focus on identifying the project parameters which might have a significant effect on the size-effort relationship. This study brings suggestions on how to use COSMIC ...
Parameter Estimation for a Class of Lifetime Models
Directory of Open Access Journals (Sweden)
Xinyang Ji
2014-01-01
Full Text Available Our purpose in this paper is to present a better method of parametric estimation for a bivariate nonlinear regression model, which takes the performance indicator of rubber aging as the dependent variable and time and temperature as the independent variables. We point out that the commonly used two-step method (TSM, which splits the model and estimate parameters separately, has limitation. Instead, we apply the Marquardt’s method (MM to implement parametric estimation directly for the model and compare these two methods of parametric estimation by random simulation. Our results show that MM has better effect of data fitting, more reasonable parametric estimates, and smaller prediction error compared with TSM.
System Estimation of Panel Data Models under Long-Range Dependence
DEFF Research Database (Denmark)
Ergemen, Yunus Emre
using conditional-sum-of-squares criteria based on projected series by which latent characteristics are proxied. Resulting estimates are consistent and asymptotically normal at standard parametric rates. A simulation study provides reliability on the estimation method. The method is then applied......A general dynamic panel data model is considered that incorporates individual and interactive fixed effects allowing for contemporaneous correlation in model innovations. The model accommodates general stationary or nonstationary long-range dependence through interactive fixed effects...
Estimating climate change impact on irrigation demand using integrated modelling
International Nuclear Information System (INIS)
Zupanc, Vesna; Pintar, Marina
2004-01-01
Water is basic element in agriculture, and along with the soil characteristics, it remains the essential for the growth and evolution of plants. Trends of air temperature and precipitation for Slovenia indicate the increase of the air temperature and reduction of precipitation during the vegetation period, which will have a substantial impact on rural economy in Slovenia. The impact of climate change will be substantial for soil the water balance. Distinctive drought periods in past years had great impact on rural plants in light soils. Climate change will most probably also result in drought in soils which otherwise provide optimal water supply for plants. Water balance in the cross section of the rooting depth is significant for the agriculture. Mathematical models enable smaller amount of measurements in a certain area by means of measurements carried out only in characteristic points serving for verification and calibration of the model. Combination of on site measurements and mathematical modelling proved to be an efficient method for understanding of processes in nature. Climate scenarios made for the estimation of the impact of climate change are based on the general circulation models. A study based on a hundred year set of monthly data showed that in Slovenia temperature would increase at min. by 2.3 o C, and by 5.6 o C at max and by 4.5 o C in average. Valid methodology for the estimate of the impact of climate change applies the model using a basic set of data for a thirty year period (1961-1990) and a changed set of climate input parameters on one hand, and, on the other, a comparison of output results of the model. Estimating climate change impact on irrigation demand for West Slovenia for peaches and nectarines grown on Cambisols and Fluvisols was made using computer model SWAP. SWAP is a precise and power too[ for the estimation of elements of soil water balance at the level of cross section of the monitored and studied profile from the soil surface
A model for estimating carbon accumulation in cork products
Directory of Open Access Journals (Sweden)
Ana C. Dias
2014-08-01
Full Text Available Aim of study: This study aims to develop a calculation model for estimating carbon accumulation in cork products, both whilst in use and when in landfills, and to apply the model to Portugal as an example.Area of study: The model is applicable worldwide and the case-study is Portugal.Material and methods: The model adopts a flux-data method based on a lifetime analysis and quantifies carbon accumulation in cork products according to three approaches that differ on how carbon stocks (or emissions are allocated to cork product consuming and producing countries. These approaches are: stock-change, production and atmospheric-flow. The effect on carbon balance of methane emissions from the decay of cork products in landfills is also evaluated.Main results: The model was applied to Portugal and the results show that carbon accumulation in cork products in the period between 1990 and 2010 varied between 24 and 92 Gg C year-1. The atmospheric-flow approach provided the highest carbon accumulation over the whole period due to the net export of carbon in cork products. The production approach ranked second because exported cork products were mainly manufactured from domestically produced cork. The net carbon balance in cork products was also a net carbon accumulation with all the approaches, ranging from 5 to 81 Gg C eq year-1.Research highlights: The developed model can be applied to other countries and may be a step forward to consider carbon accumulation in cork products in national greenhouse gas inventories, as well as in future climate agreements.Keywords: Atmospheric-flow approach; Greenhouse gas balance; Modelling; Production approach; Stock-change approach.
Consistent estimation of linear panel data models with measurement error
Meijer, Erik; Spierdijk, Laura; Wansbeek, Thomas
2017-01-01
Measurement error causes a bias towards zero when estimating a panel data linear regression model. The panel data context offers various opportunities to derive instrumental variables allowing for consistent estimation. We consider three sources of moment conditions: (i) restrictions on the
The Development of an Empirical Model for Estimation of the ...
African Journals Online (AJOL)
Nassiri P
rate, daily water consumption, smoking habits, drugs that interfere with the thermoregulatory processes, and exposure to other harmful agents. Conclusions: Eventually, based on the criteria, a model for estimation of the workers' sensitivity to heat stress was presented for the first time, by which the sensitivity is estimated in ...
Asymptotics for Estimating Equations in Hidden Markov Models
DEFF Research Database (Denmark)
Hansen, Jørgen Vinsløv; Jensen, Jens Ledet
Results on asymptotic normality for the maximum likelihood estimate in hidden Markov models are extended in two directions. The stationarity assumption is relaxed, which allows for a covariate process influencing the hidden Markov process. Furthermore a class of estimating equations is considered...
Performances of estimators of linear auto-correlated error model ...
African Journals Online (AJOL)
The performances of five estimators of linear models with autocorrelated disturbance terms are compared when the independent variable is exponential. The results reveal that for both small and large samples, the Ordinary Least Squares (OLS) compares favourably with the Generalized least Squares (GLS) estimators in ...
Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model
DEFF Research Database (Denmark)
Åberg, Andreas; Widd, Anders; Abildskov, Jens
2016-01-01
A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests...
Estimation for the Multiple Factor Model When Data Are Missing.
Finkbeiner, Carl
1979-01-01
A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with five heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation. (Author/CTM)
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of nonlinear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Inverse Gaussian model for small area estimation via Gibbs sampling
African Journals Online (AJOL)
We present a Bayesian method for estimating small area parameters under an inverse Gaussian model. The method is extended to estimate small area parameters for finite populations. The Gibbs sampler is proposed as a mechanism for implementing the Bayesian paradigm. We illustrate the method by application to ...
Person Appearance Modeling and Orientation Estimation using Spherical Harmonics
Liem, M.C.; Gavrila, D.M.
2013-01-01
We present a novel approach for the joint estimation of a person's overall body orientation, 3D shape and texture, from overlapping cameras. Overall body orientation (i.e. rotation around torso major axis) is estimated by minimizing the difference between a learned texture model in a canonical
Performances of estimators of linear model with auto-correlated ...
African Journals Online (AJOL)
A Monte Carlo Study of the small sampling properties of five estimators of a linear model with Autocorrelated error terms is discussed. The independent variable was specified as standard normal data. The estimators of the slop coefficients β with the help of Ordinary Least Squares (OLS), increased with increased ...
Parameter Estimation for a Computable General Equilibrium Model
DEFF Research Database (Denmark)
Arndt, Channing; Robinson, Sherman; Tarp, Finn
2002-01-01
We introduce a maximum entropy approach to parameter estimation for computable general equilibrium (CGE) models. The approach applies information theory to estimating a system of non-linear simultaneous equations. It has a number of advantages. First, it imposes all general equilibrium constraints...
Review Genetic prediction models and heritability estimates for ...
African Journals Online (AJOL)
edward
2015-05-09
May 9, 2015 ... cattle in South Africa. Linear models, random regression (RR) models, threshold models (TMs) and ...... Heritability for longevity has been estimated with TMs in Canadian Holsteins (Boettcher et al., 1999),. Spanish ... simulation to incorporate the tri-gamma function (γ) as used by Sasaki et al. (2012) and ...
On mixture model complexity estimation for music recommender systems
Balkema, W.; van der Heijden, Ferdinand; Meijerink, B.
2006-01-01
Content-based music navigation systems are in need of robust music similarity measures. Current similarity measures model each song with the same model parameters. We propose methods to efficiently estimate the required number of model parameters of each individual song. First results of a study on
Parameter estimation of electricity spot models from futures prices
Aihara, ShinIchi; Bagchi, Arunabha; Imreizeeq, E.S.N.; Walter, E.
We consider a slight perturbation of the Schwartz-Smith model for the electricity futures prices and the resulting modified spot model. Using the martingale property of the modified price under the risk neutral measure, we derive the arbitrage free model for the spot and futures prices. We estimate
DEFF Research Database (Denmark)
Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.
2011-01-01
the uncertainty of the weather radar rainfall input. The main findings of this work, is that the input uncertainty propagate through the urban drainage model with significant effects on the model result. The GLUE methodology is in general a usable way to explore this uncertainty although; the exact width......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...
Modeling, State Estimation and Control of Unmanned Helicopters
Lau, Tak Kit
error modeling and the filtering method for the sensor noise compensation. Moreover, we provide a fully automatic algorithm to tune our method. Finally, we evaluate our method on an instrumented gasoline helicopter. Experiments show that the technique enables the robust positioning of flying helicopters when no GNSS measurement is available. The design of an autopilot for an unmanned helicopter is made difficult by its nonlinear, coupled and non-minimum phase dynamics. Here, we consider a reinforcement learning approach to transfer motion skills from human to machine, and hence to achieve autonomous flight control. By making efficient use of a series of state-and-action pairs given by a human pilot, our algorithm bootstraps a parameterized control policy and learns to hover and follow trajectories after one manual flight. One key observation our algorithm is based on is that, although it is often difficult to retrieve the human pilots' hidden desiderata that formulate their state-feedback mechanisms in controlling the helicopters, it is possible to intercept the states of a helicopter and the actions by a human pilot and then to fit both into a model. We demonstrate the performance of our learning controller in experiments. The results described in this dissertation shed new and important light on the technology necessary to advance the current state of the unmanned helicopters. From a comprehensive dynamics modeling that addresses perplexing cross-couplings on the unmanned helicopters, to a robust state estimation against GNSS outage and a learn-from-scarce-sample control for an unmanned helicopter, we provide a starting point for the cultivation of the next-generation unmanned helicopters that can operate with the least possible human intervention.
Parameter Estimation for the Thurstone Case III Model.
Mackay, David B.; Chaiy, Seoil
1982-01-01
The ability of three estimation criteria to recover parameters of the Thurstone Case V and Case III models from comparative judgment data was investigated via Monte Carlo techniques. Significant differences in recovery are shown to exist. (Author/JKS)
Multilevel models improve precision and speed of IC50 estimates.
Vis, Daniel J; Bombardelli, Lorenzo; Lightfoot, Howard; Iorio, Francesco; Garnett, Mathew J; Wessels, Lodewyk Fa
2016-05-01
Experimental variation in dose-response data of drugs tested on cell lines result in inaccuracies in the estimate of a key drug sensitivity characteristic: the IC50. We aim to improve the precision of the half-limiting dose (IC50) estimates by simultaneously employing all dose-responses across all cell lines and drugs, rather than using a single drug-cell line response. We propose a multilevel mixed effects model that takes advantage of all available dose-response data. The new estimates are highly concordant with the currently used Bayesian model when the data are well behaved. Otherwise, the multilevel model is clearly superior. The multilevel model yields a significant reduction of extreme IC50 estimates, an increase in precision and it runs orders of magnitude faster.
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....... For a comparison the parameters are also estimated by an output error method, where the sum of squared simulation error is minimized. The former methodology is optimal for short-term prediction whereas the latter is optimal for simulations. Hence, depending on the purpose it is possible to select whether...... 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...
New aerial survey and hierarchical model to estimate manatee abundance
Langimm, Cahterine A.; Dorazio, Robert M.; Stith, Bradley M.; Doyle, Terry J.
2011-01-01
Monitoring the response of endangered and protected species to hydrological restoration is a major component of the adaptive management framework of the Comprehensive Everglades Restoration Plan. The endangered Florida manatee (Trichechus manatus latirostris) lives at the marine-freshwater interface in southwest Florida and is likely to be affected by hydrologic restoration. To provide managers with prerestoration information on distribution and abundance for postrestoration comparison, we developed and implemented a new aerial survey design and hierarchical statistical model to estimate and map abundance of manatees as a function of patch-specific habitat characteristics, indicative of manatee requirements for offshore forage (seagrass), inland fresh drinking water, and warm-water winter refuge. We estimated the number of groups of manatees from dual-observer counts and estimated the number of individuals within groups by removal sampling. Our model is unique in that we jointly analyzed group and individual counts using assumptions that allow probabilities of group detection to depend on group size. Ours is the first analysis of manatee aerial surveys to model spatial and temporal abundance of manatees in association with habitat type while accounting for imperfect detection. We conducted the study in the Ten Thousand Islands area of southwestern Florida, USA, which was expected to be affected by the Picayune Strand Restoration Project to restore hydrology altered for a failed real-estate development. We conducted 11 surveys in 2006, spanning the cold, dry season and warm, wet season. To examine short-term and seasonal changes in distribution we flew paired surveys 1–2 days apart within a given month during the year. Manatees were sparsely distributed across the landscape in small groups. Probability of detection of a group increased with group size; the magnitude of the relationship between group size and detection probability varied among surveys. Probability
Fundamental Frequency and Model Order Estimation Using Spatial Filtering
DEFF Research Database (Denmark)
Karimian-Azari, Sam; Jensen, Jesper Rindom; Christensen, Mads Græsbøll
2014-01-01
In signal processing applications of harmonic-structured signals, estimates of the fundamental frequency and number of harmonics are often necessary. In real scenarios, a desired signal is contaminated by different levels of noise and interferers, which complicate the estimation of the signal...... extend this procedure to account for inharmonicity using unconstrained model order estimation. The simulations show that beamforming improves the performance of the joint estimates of fundamental frequency and the number of harmonics in low signal to interference (SIR) levels, and an experiment...... on a trumpet signal show the applicability on real signals....
Context Tree Estimation in Variable Length Hidden Markov Models
Dumont, Thierry
2011-01-01
We address the issue of context tree estimation in variable length hidden Markov models. We propose an estimator of the context tree of the hidden Markov process which needs no prior upper bound on the depth of the context tree. We prove that the estimator is strongly consistent. This uses information-theoretic mixture inequalities in the spirit of Finesso and Lorenzo(Consistent estimation of the order for Markov and hidden Markov chains(1990)) and E.Gassiat and S.Boucheron (Optimal error exp...
Moore, Bria M; Brady, Samuel L; Mirro, Amy E; Kaufman, Robert A
2014-07-01
patient size in their dose calculation, and was found to agree in the chest to better than an average of 5% (27.6/26.2) and in the abdominopelvic region to better than 2% (73.4/75.0). For organs fully covered within the scan volume, the average correlation of SSDE and organ absolute dose was found to be better than ± 10%. In addition, this study provides a complete list of organ dose correlation factors (CF(organ)(SSDE)) for the chest and abdominopelvic regions, and describes a simple methodology to estimate individual pediatric patient organ dose based on patient SSDE.
Estimation and prediction under local volatility jump-diffusion model
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.
Optimal difference-based estimation for partially linear models
Zhou, Yuejin
2017-12-16
Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.
Modelling Water Uptake Provides a New Perspective on Grass and Tree Coexistence.
Directory of Open Access Journals (Sweden)
Michael G Mazzacavallo
Full Text Available Root biomass distributions have long been used to infer patterns of resource uptake. These patterns are used to understand plant growth, plant coexistence and water budgets. Root biomass, however, may be a poor indicator of resource uptake because large roots typically do not absorb water, fine roots do not absorb water from dry soils and roots of different species can be difficult to differentiate. In a sub-tropical savanna, Kruger Park, South Africa, we used a hydrologic tracer experiment to describe the abundance of active grass and tree roots across the soil profile. We then used this tracer data to parameterize a water movement model (Hydrus 1D. The model accounted for water availability and estimated grass and tree water uptake by depth over a growing season. Most root biomass was found in shallow soils (0-20 cm and tracer data revealed that, within these shallow depths, half of active grass roots were in the top 12 cm while half of active tree roots were in the top 21 cm. However, because shallow soils provided roots with less water than deep soils (20-90 cm, the water movement model indicated that grass and tree water uptake was twice as deep as would be predicted from root biomass or tracer data alone: half of grass and tree water uptake occurred in the top 23 and 43 cm, respectively. Niche partitioning was also greater when estimated from water uptake rather than tracer uptake. Contrary to long-standing assumptions, shallow grass root distributions absorbed 32% less water than slightly deeper tree root distributions when grasses and trees were assumed to have equal water demands. Quantifying water uptake revealed deeper soil water uptake, greater niche partitioning and greater benefits of deep roots than would be estimated from root biomass or tracer uptake data alone.
Campbell, D A; Chkrebtii, O
2013-12-01
Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.
Estimation of Nonlinear Dynamic Panel Data Models with Individual Effects
Directory of Open Access Journals (Sweden)
Yi Hu
2014-01-01
Full Text Available This paper suggests a generalized method of moments (GMM based estimation for dynamic panel data models with individual specific fixed effects and threshold effects simultaneously. We extend Hansen’s (Hansen, 1999 original setup to models including endogenous regressors, specifically, lagged dependent variables. To address the problem of endogeneity of these nonlinear dynamic panel data models, we prove that the orthogonality conditions proposed by Arellano and Bond (1991 are valid. The threshold and slope parameters are estimated by GMM, and asymptotic distribution of the slope parameters is derived. Finite sample performance of the estimation is investigated through Monte Carlo simulations. It shows that the threshold and slope parameter can be estimated accurately and also the finite sample distribution of slope parameters is well approximated by the asymptotic distribution.
Models for estimating macronutrients in Mimosa scabrella Bentham
Directory of Open Access Journals (Sweden)
Saulo Jorge Téo
2010-09-01
Full Text Available The aim of this work was to adjust and test different statistical models for estimating macronutrient content in theabove-ground biomass of bracatinga (Mimosa scabrella Bentham. The data were collected from 25 bracatinga trees, all native to thenorth of the metropolitan region of Curitiba, Paraná state, Brazil. To determine the biomass and macronutrient content, the trees wereseparated into the compartments leaves, branches < 4 cm, branches 4 cm, wood and stem barks. Different statistical models wereadjusted to estimate N, P, K, Ca and Mg contents in the tree compartments, using dendrometric variables as the model independentvariables. Based on the results, the equations developed for estimating macronutrient contents were, in general, satisfactory. The mostaccurate estimates were obtained for the stem biomass compartments and the sum of the biomass compartments. In some cases, theequations had a better performance when crown and stem dimensions, age and dominant height were included as independentvariables.
Improving the realism of hydrologic model through multivariate parameter estimation
Rakovec, Oldrich; Kumar, Rohini; Attinger, Sabine; Samaniego, Luis
2017-04-01
Increased availability and quality of near real-time observations should improve understanding of predictive skills of hydrological models. Recent studies have shown the limited capability of river discharge data alone to adequately constrain different components of distributed model parameterizations. In this study, the GRACE satellite-based total water storage (TWS) anomaly is used to complement the discharge data with an aim to improve the fidelity of mesoscale hydrologic model (mHM) through multivariate parameter estimation. The study is conducted in 83 European basins covering a wide range of hydro-climatic regimes. The model parameterization complemented with the TWS anomalies leads to statistically significant improvements in (1) discharge simulations during low-flow period, and (2) evapotranspiration estimates which are evaluated against independent (FLUXNET) data. Overall, there is no significant deterioration in model performance for the discharge simulations when complemented by information from the TWS anomalies. However, considerable changes in the partitioning of precipitation into runoff components are noticed by in-/exclusion of TWS during the parameter estimation. A cross-validation test carried out to assess the transferability and robustness of the calibrated parameters to other locations further confirms the benefit of complementary TWS data. In particular, the evapotranspiration estimates show more robust performance when TWS data are incorporated during the parameter estimation, in comparison with the benchmark model constrained against discharge only. This study highlights the value for incorporating multiple data sources during parameter estimation to improve the overall realism of hydrologic model and its applications over large domains. Rakovec, O., Kumar, R., Attinger, S. and Samaniego, L. (2016): Improving the realism of hydrologic model functioning through multivariate parameter estimation. Water Resour. Res., 52, http://dx.doi.org/10
Directory of Open Access Journals (Sweden)
Peter Scarborough
2016-11-01
Full Text Available Abstract Background The DisMod II model is designed to estimate epidemiological parameters on diseases where measured data are incomplete and has been used to provide estimates of disease incidence for the Global Burden of Disease study. We assessed the external validity of the DisMod II model by comparing modelled estimates of the incidence of first acute myocardial infarction (AMI in England in 2010 with estimates derived from a linked dataset of hospital records and death certificates. Methods Inputs for DisMod II were prevalence rates of ever having had an AMI taken from a population health survey, total mortality rates and AMI mortality rates taken from death certificates. By definition, remission rates were zero. We estimated first AMI incidence in an external dataset from England in 2010 using a linked dataset including all hospital admissions and death certificates since 1998. 95 % confidence intervals were derived around estimates from the external dataset and DisMod II estimates based on sampling variance and reported uncertainty in prevalence estimates respectively. Results Estimates of the incidence rate for the whole population were higher in the DisMod II results than the external dataset (+54 % for men and +26 % for women. Age-specific results showed that the DisMod II results over-estimated incidence for all but the oldest age groups. Confidence intervals for the DisMod II and external dataset estimates did not overlap for most age groups. Conclusion By comparison with AMI incidence rates in England, DisMod II did not achieve external validity for age-specific incidence rates, but did provide global estimates of incidence that are of similar magnitude to measured estimates. The model should be used with caution when estimating age-specific incidence rates.
Bayesian Nonparametric Model for Estimating Multistate Travel Time Distribution
Directory of Open Access Journals (Sweden)
Emmanuel Kidando
2017-01-01
Full Text Available Multistate models, that is, models with more than two distributions, are preferred over single-state probability models in modeling the distribution of travel time. Literature review indicated that the finite multistate modeling of travel time using lognormal distribution is superior to other probability functions. In this study, we extend the finite multistate lognormal model of estimating the travel time distribution to unbounded lognormal distribution. In particular, a nonparametric Dirichlet Process Mixture Model (DPMM with stick-breaking process representation was used. The strength of the DPMM is that it can choose the number of components dynamically as part of the algorithm during parameter estimation. To reduce computational complexity, the modeling process was limited to a maximum of six components. Then, the Markov Chain Monte Carlo (MCMC sampling technique was employed to estimate the parameters’ posterior distribution. Speed data from nine links of a freeway corridor, aggregated on a 5-minute basis, were used to calculate the corridor travel time. The results demonstrated that this model offers significant flexibility in modeling to account for complex mixture distributions of the travel time without specifying the number of components. The DPMM modeling further revealed that freeway travel time is characterized by multistate or single-state models depending on the inclusion of onset and offset of congestion periods.
Bayesian estimation of parameters in a regional hydrological model
Directory of Open Access Journals (Sweden)
K. Engeland
2002-01-01
Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1 process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis
Analysis and Management of Animal Populations: Modeling, Estimation and Decision Making
Williams, B.K.; Nichols, J.D.; Conroy, M.J.
2002-01-01
This book deals with the processes involved in making informed decisions about the management of animal populations. It covers the modeling of population responses to management actions, the estimation of quantities needed in the modeling effort, and the application of these estimates and models to the development of sound management decisions. The book synthesizes and integrates in a single volume the methods associated with these themes, as they apply to ecological assessment and conservation of animal populations. KEY FEATURES * Integrates population modeling, parameter estimation and * decision-theoretic approaches to management in a single, cohesive framework * Provides authoritative, state-of-the-art descriptions of quantitative * approaches to modeling, estimation and decision-making * Emphasizes the role of mathematical modeling in the conduct of science * and management * Utilizes a unifying biological context, consistent mathematical notation, * and numerous biological examples
Input-output model for MACCS nuclear accident impacts estimation¹
Energy Technology Data Exchange (ETDEWEB)
Outkin, Alexander V. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bixler, Nathan E. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vargas, Vanessa N [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-01-27
Since the original economic model for MACCS was developed, better quality economic data (as well as the tools to gather and process it) and better computational capabilities have become available. The update of the economic impacts component of the MACCS legacy model will provide improved estimates of business disruptions through the use of Input-Output based economic impact estimation. This paper presents an updated MACCS model, bases on Input-Output methodology, in which economic impacts are calculated using the Regional Economic Accounting analysis tool (REAcct) created at Sandia National Laboratories. This new GDP-based model allows quick and consistent estimation of gross domestic product (GDP) losses due to nuclear power plant accidents. This paper outlines the steps taken to combine the REAcct Input-Output-based model with the MACCS code, describes the GDP loss calculation, and discusses the parameters and modeling assumptions necessary for the estimation of long-term effects of nuclear power plant accidents.
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...
Surface-source modeling and estimation using biomagnetic measurements.
Yetik, Imam Samil; Nehorai, Arye; Muravchik, Carlos H; Haueisen, Jens; Eiselt, Michael
2006-10-01
We propose a number of electric source models that are spatially distributed on an unknown surface for biomagnetism. These can be useful to model, e.g., patches of electrical activity on the cortex. We use a realistic head (or another organ) model and discuss the special case of a spherical head model with radial sensors resulting in more efficient computations of the estimates for magnetoencephalography. We derive forward solutions, maximum likelihood (ML) estimates, and Cramér-Rao bound (CRB) expressions for the unknown source parameters. A model selection method is applied to decide on the most appropriate model. We also present numerical examples to compare the performances and computational costs of the different models and illustrate when it is possible to distinguish between surface and focal sources or line sources. Finally, we apply our methods to real biomagnetic data of phantom human torso and demonstrate the applicability of them.
Marginal Maximum Likelihood Estimation of Item Response Models in R
Directory of Open Access Journals (Sweden)
Matthew S. Johnson
2007-02-01
Full Text Available Item response theory (IRT models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other fields have successfully utilized IRT-like models in a wide variety of applications. This paper discusses the three major methods of estimation in IRT and develops R functions utilizing the built-in capabilities of the R environment to find the marginal maximum likelihood estimates of the generalized partial credit model. The currently available R packages ltm is also discussed.
Estimation of shape model parameters for 3D surfaces
DEFF Research Database (Denmark)
Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen
2008-01-01
is applied to a database of 3D surfaces from a section of the porcine pelvic bone extracted from 33 CT scans. A leave-one-out validation shows that the parameters of the first 3 modes of the shape model can be predicted with a mean difference within [-0.01,0.02] from the true mean, with a standard deviation......Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D...... surfaces using distance maps, which enables the estimation of model parameters without the requirement of point correspondence. For applications with acquisition limitations such as speed and cost, this formulation enables the fitting of a statistical shape model to arbitrarily sampled data. The method...
Bayesian analysis for uncertainty estimation of a canopy transpiration model
Samanta, S.; Mackay, D. S.; Clayton, M. K.; Kruger, E. L.; Ewers, B. E.
2007-04-01
A Bayesian approach was used to fit a conceptual transpiration model to half-hourly transpiration rates for a sugar maple (Acer saccharum) stand collected over a 5-month period and probabilistically estimate its parameter and prediction uncertainties. The model used the Penman-Monteith equation with the Jarvis model for canopy conductance. This deterministic model was extended by adding a normally distributed error term. This extension enabled using Markov chain Monte Carlo simulations to sample the posterior parameter distributions. The residuals revealed approximate conformance to the assumption of normally distributed errors. However, minor systematic structures in the residuals at fine timescales suggested model changes that would potentially improve the modeling of transpiration. Results also indicated considerable uncertainties in the parameter and transpiration estimates. This simple methodology of uncertainty analysis would facilitate the deductive step during the development cycle of deterministic conceptual models by accounting for these uncertainties while drawing inferences from data.
Estimating Structural Models of Corporate Bond Prices in Indonesian Corporations
Directory of Open Access Journals (Sweden)
Lenny Suardi
2014-08-01
Full Text Available This paper applies the maximum likelihood (ML approaches to implementing the structural model of corporate bond, as suggested by Li and Wong (2008, in Indonesian corporations. Two structural models, extended Merton and Longstaff & Schwartz (LS models, are used in determining these prices, yields, yield spreads and probabilities of default. ML estimation is used to determine the volatility of irm value. Since irm value is unobserved variable, Duan (1994 suggested that the irst step of ML estimation is to derive the likelihood function for equity as the option on the irm value. The second step is to ind parameters such as the drift and volatility of irm value, that maximizing this function. The irm value itself is extracted by equating the pricing formula to the observed equity prices. Equity, total liabilities, bond prices data and the irm's parameters (irm value, volatility of irm value, and default barrier are substituted to extended Merton and LS bond pricing formula in order to valuate the corporate bond.These models are implemented to a sample of 24 bond prices in Indonesian corporation during period of 2001-2005, based on criteria of Eom, Helwege and Huang (2004. The equity and bond prices data were obtained from Indonesia Stock Exchange for irms that issued equity and provided regular inancial statement within this period. The result shows that both models, in average, underestimate the bond prices and overestimate the yields and yield spread. ";} // -->activate javascript
Estimation of Continuous Velocity Model Variations in Rock Deformation Tests.
Flynn, J. W.; Tomas, R.; Benson, P. M.
2017-12-01
Seismic interferometry, using either seismic waves coda or ambient noise, is a passive technique to image the sub-surface seismic velocity structure, which directly relates to the physical properties of the material through which they travel. The methodology estimates the Green's function for the volume between two seismic stations by cross-correlating long time series of ambient noise recorded at both stations, with the Green's function being effectively the seismogram recorded at one station due to an impulsive or instantaneous energy source at the second station. In laboratory rock deformation experiments, changes in the velocity structure of the rock sample are generally measured through active surveys using an array of AE piezoelectric P-wave transducers, producing a time series of ultrasonic velocities in both axial and radial directions. The velocity information from the active surveys is used to provide a time dependent velocity model for the inversion of AE event source locations. These velocity measurements are carried out at regular intervals throughout the laboratory test, causing the interruption of passive AE monitoring for the length of the surveys. There is therefore a trade-off between the frequency at which the active velocity surveys are carried out to optimise the velocity model and the availability of a complete AE record during the rock deformation test.This study proposes to use noise interferometry to provide a continuous measurement of velocity variations in a rock sample during a laboratory rock deformation experiment without the need to carry out active velocity surveys while simultaneously passively monitoring AE activity. The continuous noise source in this test, is an AE transducer fed with a white gaussian noise signal from a function generator. Data from all AE transducers is continuously acquired and recorded during the deformation experiment. The cross correlation of the continuous AE record is used to produce a continuous velocity
Comparing interval estimates for small sample ordinal CFA models.
Natesan, Prathiba
2015-01-01
Robust maximum likelihood (RML) and asymptotically generalized least squares (AGLS) methods have been recommended for fitting ordinal structural equation models. Studies show that some of these methods underestimate standard errors. However, these studies have not investigated the coverage and bias of interval estimates. An estimate with a reasonable standard error could still be severely biased. This can only be known by systematically investigating the interval estimates. The present study compares Bayesian, RML, and AGLS interval estimates of factor correlations in ordinal confirmatory factor analysis models (CFA) for small sample data. Six sample sizes, 3 factor correlations, and 2 factor score distributions (multivariate normal and multivariate mildly skewed) were studied. Two Bayesian prior specifications, informative and relatively less informative were studied. Undercoverage of confidence intervals and underestimation of standard errors was common in non-Bayesian methods. Underestimated standard errors may lead to inflated Type-I error rates. Non-Bayesian intervals were more positive biased than negatively biased, that is, most intervals that did not contain the true value were greater than the true value. Some non-Bayesian methods had non-converging and inadmissible solutions for small samples and non-normal data. Bayesian empirical standard error estimates for informative and relatively less informative priors were closer to the average standard errors of the estimates. The coverage of Bayesian credibility intervals was closer to what was expected with overcoverage in a few cases. Although some Bayesian credibility intervals were wider, they reflected the nature of statistical uncertainty that comes with the data (e.g., small sample). Bayesian point estimates were also more accurate than non-Bayesian estimates. The results illustrate the importance of analyzing coverage and bias of interval estimates, and how ignoring interval estimates can be misleading
Procedures for parameter estimates of computational models for localized failure
Iacono, C.
2007-01-01
In the last years, many computational models have been developed for tensile fracture in concrete. However, their reliability is related to the correct estimate of the model parameters, not all directly measurable during laboratory tests. Hence, the development of inverse procedures is needed, that
Estimation of pure autoregressive vector models for revenue series ...
African Journals Online (AJOL)
This paper aims at applying multivariate approach to Box and Jenkins univariate time series modeling to three vector series. General Autoregressive Vector Models with time varying coefficients are estimated. The first vector is a response vector, while others are predictor vectors. By matrix expansion each vector, whether ...
GMM estimation in panel data models with measurement error
Wansbeek, T.J.
Griliches and Hausman (J. Econom. 32 (1986) 93) have introduced GMM estimation in panel data models with measurement error. We present a simple, systematic approach to derive moment conditions for such models under a variety of assumptions. (C) 2001 Elsevier Science S.A. All rights reserved.
Estimating classification images with generalized linear and additive models.
Knoblauch, Kenneth; Maloney, Laurence T
2008-12-22
Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal noise, however, the advantage of the GLM over the LM decreases and GLM is no more accurate than LM. We then introduce the Generalized Additive Model (GAM), an extension of GLM that can be used to estimate smooth classification images adaptively. We show that this approach is more robust to the presence of internal noise, and finally, we demonstrate that GAM is readily adapted to estimation of higher order (nonlinear) classification images and to testing their significance.
Bases for the Creation of Electric Energy Price Estimate Model
International Nuclear Information System (INIS)
Toljan, I.; Klepo, M.
1995-01-01
The paper presents the basic principles for the creation and introduction of a new model for the electric energy price estimate and its significant influence on the tariff system functioning. There is also a review of the model used presently for the electric energy price estimate which is based on the model of objectivized values of electric energy plants and production, transmission and distribution facilities, followed by proposed changes which would result in functional and organizational improvements within the electric energy system as the most complex subsystem of the whole power system. The model is based on substantial and functional connection of the optimization and analysis system with the electric energy economic dispatching, including marginal cost estimate and their influence on the tariff system as the main means in achieving better electric energy system's functioning quality. (author). 10 refs., 2 figs
Unemployment estimation: Spatial point referenced methods and models
Pereira, Soraia
2017-06-26
Portuguese Labor force survey, from 4th quarter of 2014 onwards, started geo-referencing the sampling units, namely the dwellings in which the surveys are carried. This opens new possibilities in analysing and estimating unemployment and its spatial distribution across any region. The labor force survey choose, according to an preestablished sampling criteria, a certain number of dwellings across the nation and survey the number of unemployed in these dwellings. Based on this survey, the National Statistical Institute of Portugal presently uses direct estimation methods to estimate the national unemployment figures. Recently, there has been increased interest in estimating these figures in smaller areas. Direct estimation methods, due to reduced sampling sizes in small areas, tend to produce fairly large sampling variations therefore model based methods, which tend to
Cook, D A
2006-04-01
Models that estimate the probability of death of intensive care unit patients can be used to stratify patients according to the severity of their condition and to control for casemix and severity of illness. These models have been used for risk adjustment in quality monitoring, administration, management and research and as an aid to clinical decision making. Models such as the Mortality Prediction Model family, SAPS II, APACHE II, APACHE III and the organ system failure models provide estimates of the probability of in-hospital death of ICU patients. This review examines methods to assess the performance of these models. The key attributes of a model are discrimination (the accuracy of the ranking in order of probability of death) and calibration (the extent to which the model's prediction of probability of death reflects the true risk of death). These attributes should be assessed in existing models that predict the probability of patient mortality, and in any subsequent model that is developed for the purposes of estimating these probabilities. The literature contains a range of approaches for assessment which are reviewed and a survey of the methodologies used in studies of intensive care mortality models is presented. The systematic approach used by Standards for Reporting Diagnostic Accuracy provides a framework to incorporate these theoretical considerations of model assessment and recommendations are made for evaluation and presentation of the performance of models that estimate the probability of death of intensive care patients.
Application of Parameter Estimation for Diffusions and Mixture Models
DEFF Research Database (Denmark)
Nolsøe, Kim
error models. This is obtained by constructing an estimating function through projections of some chosen function of Yti+1 onto functions of previous observations Yti ; : : : ; Yt0 . The process of interest Xti+1 is partially observed through a measurement equation Yti+1 = h(Xti+1)+ noice, where h......(:) is restricted to be a polynomial. Through a simulation study we compare for the CIR process the obtained estimator with an estimator derived from utilizing the extended Kalman filter. The simulation study shows that the two estimation methods perform equally well.......The first part of this thesis proposes a method to determine the preferred number of structures, their proportions and the corresponding geometrical shapes of an m-membered ring molecule. This is obtained by formulating a statistical model for the data and constructing an algorithm which samples...
The problematic estimation of "imitation effects" in multilevel models
Directory of Open Access Journals (Sweden)
2003-09-01
Full Text Available It seems plausible that a person's demographic behaviour may be influenced by that among other people in the community, for example because of an inclination to imitate. When estimating multilevel models from clustered individual data, some investigators might perhaps feel tempted to try to capture this effect by simply including on the right-hand side the average of the dependent variable, constructed by aggregation within the clusters. However, such modelling must be avoided. According to simulation experiments based on real fertility data from India, the estimated effect of this obviously endogenous variable can be very different from the true effect. Also the other community effect estimates can be strongly biased. An "imitation effect" can only be estimated under very special assumptions that in practice will be hard to defend.
Development on electromagnetic impedance function modeling and its estimation
International Nuclear Information System (INIS)
Sutarno, D.
2015-01-01
Today the Electromagnetic methods such as magnetotellurics (MT) and controlled sources audio MT (CSAMT) is used in a broad variety of applications. Its usefulness in poor seismic areas and its negligible environmental impact are integral parts of effective exploration at minimum cost. As exploration was forced into more difficult areas, the importance of MT and CSAMT, in conjunction with other techniques, has tended to grow continuously. However, there are obviously important and difficult problems remaining to be solved concerning our ability to collect process and interpret MT as well as CSAMT in complex 3D structural environments. This talk aim at reviewing and discussing the recent development on MT as well as CSAMT impedance functions modeling, and also some improvements on estimation procedures for the corresponding impedance functions. In MT impedance modeling, research efforts focus on developing numerical method for computing the impedance functions of three dimensionally (3-D) earth resistivity models. On that reason, 3-D finite elements numerical modeling for the impedances is developed based on edge element method. Whereas, in the CSAMT case, the efforts were focused to accomplish the non-plane wave problem in the corresponding impedance functions. Concerning estimation of MT and CSAMT impedance functions, researches were focused on improving quality of the estimates. On that objective, non-linear regression approach based on the robust M-estimators and the Hilbert transform operating on the causal transfer functions, were used to dealing with outliers (abnormal data) which are frequently superimposed on a normal ambient MT as well as CSAMT noise fields. As validated, the proposed MT impedance modeling method gives acceptable results for standard three dimensional resistivity models. Whilst, the full solution based modeling that accommodate the non-plane wave effect for CSAMT impedances is applied for all measurement zones, including near-, transition
Development on electromagnetic impedance function modeling and its estimation
Energy Technology Data Exchange (ETDEWEB)
Sutarno, D., E-mail: Sutarno@fi.itb.ac.id [Earth Physics and Complex System Division Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung (Indonesia)
2015-09-30
Today the Electromagnetic methods such as magnetotellurics (MT) and controlled sources audio MT (CSAMT) is used in a broad variety of applications. Its usefulness in poor seismic areas and its negligible environmental impact are integral parts of effective exploration at minimum cost. As exploration was forced into more difficult areas, the importance of MT and CSAMT, in conjunction with other techniques, has tended to grow continuously. However, there are obviously important and difficult problems remaining to be solved concerning our ability to collect process and interpret MT as well as CSAMT in complex 3D structural environments. This talk aim at reviewing and discussing the recent development on MT as well as CSAMT impedance functions modeling, and also some improvements on estimation procedures for the corresponding impedance functions. In MT impedance modeling, research efforts focus on developing numerical method for computing the impedance functions of three dimensionally (3-D) earth resistivity models. On that reason, 3-D finite elements numerical modeling for the impedances is developed based on edge element method. Whereas, in the CSAMT case, the efforts were focused to accomplish the non-plane wave problem in the corresponding impedance functions. Concerning estimation of MT and CSAMT impedance functions, researches were focused on improving quality of the estimates. On that objective, non-linear regression approach based on the robust M-estimators and the Hilbert transform operating on the causal transfer functions, were used to dealing with outliers (abnormal data) which are frequently superimposed on a normal ambient MT as well as CSAMT noise fields. As validated, the proposed MT impedance modeling method gives acceptable results for standard three dimensional resistivity models. Whilst, the full solution based modeling that accommodate the non-plane wave effect for CSAMT impedances is applied for all measurement zones, including near-, transition
Asymptotic distribution theory for break point estimators in models estimated via 2SLS
Boldea, O.; Hall, A.R.; Han, S.
2012-01-01
In this paper, we present a limiting distribution theory for the break point estimator in a linear regression model with multiple structural breaks obtained by minimizing a Two Stage Least Squares (2SLS) objective function. Our analysis covers both the case in which the reduced form for the
Neural Models: An Option to Estimate Seismic Parameters of Accelerograms
Alcántara, L.; García, S.; Ovando-Shelley, E.; Macías, M. A.
2014-12-01
Seismic instrumentation for recording strong earthquakes, in Mexico, goes back to the 60´s due the activities carried out by the Institute of Engineering at Universidad Nacional Autónoma de México. However, it was after the big earthquake of September 19, 1985 (M=8.1) when the project of seismic instrumentation assumes a great importance. Currently, strong ground motion networks have been installed for monitoring seismic activity mainly along the Mexican subduction zone and in Mexico City. Nevertheless, there are other major regions and cities that can be affected by strong earthquakes and have not yet begun their seismic instrumentation program or this is still in development.Because of described situation some relevant earthquakes (e.g. Huajuapan de León Oct 24, 1980 M=7.1, Tehuacán Jun 15, 1999 M=7 and Puerto Escondido Sep 30, 1999 M= 7.5) have not been registered properly in some cities, like Puebla and Oaxaca, and that were damaged during those earthquakes. Fortunately, the good maintenance work carried out in the seismic network has permitted the recording of an important number of small events in those cities. So in this research we present a methodology based on the use of neural networks to estimate significant duration and in some cases the response spectra for those seismic events. The neural model developed predicts significant duration in terms of magnitude, epicenter distance, focal depth and soil characterization. Additionally, for response spectra we used a vector of spectral accelerations. For training the model we selected a set of accelerogram records obtained from the small events recorded in the strong motion instruments installed in the cities of Puebla and Oaxaca. The final results show that neural networks as a soft computing tool that use a multi-layer feed-forward architecture provide good estimations of the target parameters and they also have a good predictive capacity to estimate strong ground motion duration and response spectra.
Advanced empirical estimate of information value for credit scoring models
Directory of Open Access Journals (Sweden)
Martin Řezáč
2011-01-01
Full Text Available Credit scoring, it is a term for a wide spectrum of predictive models and their underlying techniques that aid financial institutions in granting credits. These methods decide who will get credit, how much credit they should get, and what further strategies will enhance the profitability of the borrowers to the lenders. Many statistical tools are avaiable for measuring quality, within the meaning of the predictive power, of credit scoring models. Because it is impossible to use a scoring model effectively without knowing how good it is, quality indexes like Gini, Kolmogorov-Smirnov statisic and Information value are used to assess quality of given credit scoring model. The paper deals primarily with the Information value, sometimes called divergency. Commonly it is computed by discretisation of data into bins using deciles. One constraint is required to be met in this case. Number of cases have to be nonzero for all bins. If this constraint is not fulfilled there are some practical procedures for preserving finite results. As an alternative method to the empirical estimates one can use the kernel smoothing theory, which allows to estimate unknown densities and consequently, using some numerical method for integration, to estimate value of the Information value. The main contribution of this paper is a proposal and description of the empirical estimate with supervised interval selection. This advanced estimate is based on requirement to have at least k, where k is a positive integer, observations of socres of both good and bad client in each considered interval. A simulation study shows that this estimate outperform both the empirical estimate using deciles and the kernel estimate. Furthermore it shows high dependency on choice of the parameter k. If we choose too small value, we get overestimated value of the Information value, and vice versa. Adjusted square root of number of bad clients seems to be a reasonable compromise.
Estimation and variable selection for generalized additive partial linear models
Wang, Li
2011-08-01
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.
Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2011-01-01
, propagated exponentially, can lead to severely sub-optimal plans. Modern optimizers typically maintain one-dimensional statistical summaries and make the attribute value independence and join uniformity assumptions for efficiently estimating selectivities. Therefore, selectivity estimation errors in today......’s optimizers are frequently caused by missed correlations between attributes. We present a selectivity estimation approach that does not make the independence assumptions. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution of all...
Occupancy Estimation and Modeling : Inferring Patterns and Dynamics of Species Occurrence
MacKenzie, D.I.; Nichols, J.D.; Royle, J. Andrew; Pollock, K.H.; Bailey, L.L.; Hines, J.E.
2006-01-01
This is the first book to examine the latest methods in analyzing presence/absence data surveys. Using four classes of models (single-species, single-season; single-species, multiple season; multiple-species, single-season; and multiple-species, multiple-season), the authors discuss the practical sampling situation, present a likelihood-based model enabling direct estimation of the occupancy-related parameters while allowing for imperfect detectability, and make recommendations for designing studies using these models. It provides authoritative insights into the latest in estimation modeling; discusses multiple models which lay the groundwork for future study designs; addresses critical issues of imperfect detectibility and its effects on estimation; and explores the role of probability in estimating in detail.
MODELS TO ESTIMATE BRAZILIAN INDIRECT TENSILE STRENGTH OF LIMESTONE IN SATURATED STATE
Directory of Open Access Journals (Sweden)
Zlatko Briševac
2016-06-01
Full Text Available There are a number of methods of estimating physical and mechanical characteristics. Principally, the most widely used is the regression, but recently the more sophisticated methods such as neural networks has frequently been applied, as well. This paper presents the models of a simple and a multiple regression and the neural networks – types Radial Basis Function and Multiple Layer Perceptron, which can be used for the estimate of the Brazilian indirect tensile strength in saturated conditions. The paper includes the issues of collecting the data for the analysis and modelling and the overview of the performed analysis of the efficacy assessment of the estimate of each model. After the assessment, the model which provides the best estimate was selected, including the model which could have the most wide-spread application in the engineering practice.
Parameter estimation and model selection in computational biology.
Directory of Open Access Journals (Sweden)
Gabriele Lillacci
2010-03-01
Full Text Available A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection.
Dynamic ventilation scintigraphy: a comparison of parameter estimation gating models
International Nuclear Information System (INIS)
Hack, S.N.; Paoni, R.A.; Stratton, H.; Valvano, M.; Line, B.R.; Cooper, J.A.
1988-01-01
Two procedures for providing the synchronization of ventilation scintigraphic data to create dynamic displays of the pulmonary cycle are described and compared. These techniques are based on estimating instantaneous lung volume by pneumotachometry and by scintigraphy. Twenty-three patients were studied by these two techniques. The results indicate that the estimation of the times of end-inspiration and end-expiration are equivalent by the two techniques but the morphologies of the two estimated time-volume waveforms are not equivalent. Ventilation cinescintigraphy based on time division gating but not on isovolume division gating can be equivalently generated from list mode acquired data by employing either technique described
Parameter and State Estimator for State Space Models
Directory of Open Access Journals (Sweden)
Ruifeng Ding
2014-01-01
Full Text Available This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
Parameter and state estimator for state space models.
Ding, Ruifeng; Zhuang, Linfan
2014-01-01
This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.
A practical model for pressure probe system response estimation (with review of existing models)
Hall, B. F.; Povey, T.
2018-04-01
The accurate estimation of the unsteady response (bandwidth) of pneumatic pressure probe systems (probe, line and transducer volume) is a common practical problem encountered in the design of aerodynamic experiments. Understanding the bandwidth of the probe system is necessary to capture unsteady flow features accurately. Where traversing probes are used, the desired traverse speed and spatial gradients in the flow dictate the minimum probe system bandwidth required to resolve the flow. Existing approaches for bandwidth estimation are either complex or inaccurate in implementation, so probes are often designed based on experience. Where probe system bandwidth is characterized, it is often done experimentally, requiring careful experimental set-up and analysis. There is a need for a relatively simple but accurate model for estimation of probe system bandwidth. A new model is presented for the accurate estimation of pressure probe bandwidth for simple probes commonly used in wind tunnel environments; experimental validation is provided. An additional, simple graphical method for air is included for convenience.
Energy Technology Data Exchange (ETDEWEB)
Moore, Bria M.; Brady, Samuel L., E-mail: samuel.brady@stjude.org; Kaufman, Robert A. [Department of Radiological Sciences, St Jude Children' s Research Hospital, Memphis, Tennessee 38105 (United States); Mirro, Amy E. [Department of Biomedical Engineering, Washington University, St Louis, Missouri 63130 (United States)
2014-07-15
previously published pediatric patient doses that accounted for patient size in their dose calculation, and was found to agree in the chest to better than an average of 5% (27.6/26.2) and in the abdominopelvic region to better than 2% (73.4/75.0). Conclusions: For organs fully covered within the scan volume, the average correlation of SSDE and organ absolute dose was found to be better than ±10%. In addition, this study provides a complete list of organ dose correlation factors (CF{sub SSDE}{sup organ}) for the chest and abdominopelvic regions, and describes a simple methodology to estimate individual pediatric patient organ dose based on patient SSDE.
A distributed approach for parameters estimation in System Biology models
International Nuclear Information System (INIS)
Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.
2009-01-01
Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.
Near Shore Wave Modeling and applications to wave energy estimation
Zodiatis, G.; Galanis, G.; Hayes, D.; Nikolaidis, A.; Kalogeri, C.; Adam, A.; Kallos, G.; Georgiou, G.
2012-04-01
The estimation of the wave energy potential at the European coastline is receiving increased attention the last years as a result of the adaptation of novel policies in the energy market, the concernsfor global warming and the nuclear energy security problems. Within this framework, numerical wave modeling systems keep a primary role in the accurate description of wave climate and microclimate that is a prerequisite for any wave energy assessment study. In the present work two of the most popular wave models are used for the estimation of the wave parameters at the coastline of Cyprus: The latest parallel version of the wave model WAM (ECMWF version), which employs new parameterization of shallow water effects, and the SWAN model, classically used for near shore wave simulations. The results obtained from the wave models near shores are studied by an energy estimation point of view: The wave parameters that mainly affect the energy temporal and spatial distribution, that is the significant wave height and the mean wave period, are statistically analyzed,focusing onpossible different aspects captured by the two models. Moreover, the wave spectrum distribution prevailing in different areas are discussed contributing, in this way, to the wave energy assessmentin the area. This work is a part of two European projects focusing on the estimation of the wave energy distribution around Europe: The MARINA platform (http://www.marina-platform.info/ index.aspx) and the Ewave (http://www.oceanography.ucy.ac.cy/ewave/) projects.
Synchronous Generator Model Parameter Estimation Based on Noisy Dynamic Waveforms
Berhausen, Sebastian; Paszek, Stefan
2016-01-01
In recent years, there have occurred system failures in many power systems all over the world. They have resulted in a lack of power supply to a large number of recipients. To minimize the risk of occurrence of power failures, it is necessary to perform multivariate investigations, including simulations, of power system operating conditions. To conduct reliable simulations, the current base of parameters of the models of generating units, containing the models of synchronous generators, is necessary. In the paper, there is presented a method for parameter estimation of a synchronous generator nonlinear model based on the analysis of selected transient waveforms caused by introducing a disturbance (in the form of a pseudorandom signal) in the generator voltage regulation channel. The parameter estimation was performed by minimizing the objective function defined as a mean square error for deviations between the measurement waveforms and the waveforms calculated based on the generator mathematical model. A hybrid algorithm was used for the minimization of the objective function. In the paper, there is described a filter system used for filtering the noisy measurement waveforms. The calculation results of the model of a 44 kW synchronous generator installed on a laboratory stand of the Institute of Electrical Engineering and Computer Science of the Silesian University of Technology are also given. The presented estimation method can be successfully applied to parameter estimation of different models of high-power synchronous generators operating in a power system.
Model calibration and parameter estimation for environmental and water resource systems
Sun, Ne-Zheng
2015-01-01
This three-part book provides a comprehensive and systematic introduction to the development of useful models for complex systems. Part 1 covers the classical inverse problem for parameter estimation in both deterministic and statistical frameworks, Part 2 is dedicated to system identification, hyperparameter estimation, and model dimension reduction, and Part 3 considers how to collect data and construct reliable models for prediction and decision-making. For the first time, topics such as multiscale inversion, stochastic field parameterization, level set method, machine learning, global sensitivity analysis, data assimilation, model uncertainty quantification, robust design, and goal-oriented modeling, are systematically described and summarized in a single book from the perspective of model inversion, and elucidated with numerical examples from environmental and water resources modeling. Readers of this book will not only learn basic concepts and methods for simple parameter estimation, but also get famili...
ESTIMATION OF EFFICIENCY OF THE COMPETITIVE COOPERATION MODEL
Directory of Open Access Journals (Sweden)
Natalia N. Liparteliani
2014-01-01
Full Text Available Competitive cooperation model of regional travel agencies and travel market participants is considered. Evaluation of the model using mathematical and statistical methods was carried out. Relationship marketing provides a travel company certain economic advantages.
Coupling Hydrologic and Hydrodynamic Models to Estimate PMF
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.
Estimation of the Human Absorption Cross Section Via Reverberation Models
DEFF Research Database (Denmark)
Steinböck, Gerhard; Pedersen, Troels; Fleury, Bernard Henri
2018-01-01
and compare the obtained results to those of Sabine's model. We find that the absorption by persons is large enough to be measured with a wideband channel sounder and that estimates of the human absorption cross section differ for the two models. The obtained values are comparable to values reported...... in the literature. We also suggest the use of controlled environments with low average absorption coefficients to obtain more reliable estimates. The obtained values can be used to predict the change of reverberation time with persons in the propagation environment. This allows prediction of channel characteristics...... relevant in communication systems, e.g. path loss and rms delay spread, for various population densities....
Capabilities of stochastic rainfall models as data providers for urban hydrology
Haberlandt, Uwe
2017-04-01
For planning of urban drainage systems using hydrological models, long, continuous precipitation series with high temporal resolution are needed. Since observed time series are often too short or not available everywhere, the use of synthetic precipitation is a common alternative. This contribution compares three precipitation models regarding their suitability to provide 5 minute continuous rainfall time series for a) sizing of drainage networks for urban flood protection and b) dimensioning of combined sewage systems for pollution reduction. The rainfall models are a parametric stochastic model (Haberlandt et al., 2008), a non-parametric probabilistic approach (Bárdossy, 1998) and a stochastic downscaling of dynamically simulated rainfall (Berg et al., 2013); all models are operated both as single site and multi-site generators. The models are applied with regionalised parameters assuming that there is no station at the target location. Rainfall and discharge characteristics are utilised for evaluation of the model performance. The simulation results are compared against results obtained from reference rainfall stations not used for parameter estimation. The rainfall simulations are carried out for the federal states of Baden-Württemberg and Lower Saxony in Germany and the discharge simulations for the drainage networks of the cities of Hamburg, Brunswick and Freiburg. Altogether, the results show comparable simulation performance for the three models, good capabilities for single site simulations but low skills for multi-site simulations. Remarkably, there is no significant difference in simulation performance comparing the tasks flood protection with pollution reduction, so the models are finally able to simulate both the extremes and the long term characteristics of rainfall equally well. Bárdossy, A., 1998. Generating precipitation time series using simulated annealing. Wat. Resour. Res., 34(7): 1737-1744. Berg, P., Wagner, S., Kunstmann, H., Schädler, G
Biomass models to estimate carbon stocks for hardwood tree species
Energy Technology Data Exchange (ETDEWEB)
Ruiz-Peinado, R.; Montero, G.; Rio, M. del
2012-11-01
To estimate forest carbon pools from forest inventories it is necessary to have biomass models or biomass expansion factors. In this study, tree biomass models were developed for the main hardwood forest species in Spain: Alnus glutinosa, Castanea sativa, Ceratonia siliqua, Eucalyptus globulus, Fagus sylvatica, Fraxinus angustifolia, Olea europaea var. sylvestris, Populus x euramericana, Quercus canariensis, Quercus faginea, Quercus ilex, Quercus pyrenaica and Quercus suber. Different tree biomass components were considered: stem with bark, branches of different sizes, above and belowground biomass. For each species, a system of equations was fitted using seemingly unrelated regression, fulfilling the additivity property between biomass components. Diameter and total height were explored as independent variables. All models included tree diameter whereas for the majority of species, total height was only considered in the stem biomass models and in some of the branch models. The comparison of the new biomass models with previous models fitted separately for each tree component indicated an improvement in the accuracy of the models. A mean reduction of 20% in the root mean square error and a mean increase in the model efficiency of 7% in comparison with recently published models. So, the fitted models allow estimating more accurately the biomass stock in hardwood species from the Spanish National Forest Inventory data. (Author) 45 refs.
Estimation of oil toxicity using an additive toxicity model
International Nuclear Information System (INIS)
French, D.
2000-01-01
The impacts to aquatic organisms resulting from acute exposure to aromatic mixtures released from oil spills can be modeled using a newly developed toxicity model. This paper presented a summary of the model development for the toxicity of monoaromatic and polycyclic aromatic hydrocarbon mixtures. This is normally difficult to quantify because oils are mixtures of a variety of hydrocarbons with different toxicities and environmental fates. Also, aromatic hydrocarbons are volatile, making it difficult to expose organism to constant concentrations in bioassay tests. This newly developed and validated model corrects toxicity for time and temperature of exposure. In addition, it estimates the toxicity of each aromatic in the oil-derived mixture. The toxicity of the mixture can be estimated by the weighted sum of the toxicities of the individual compounds. Acute toxicity is estimated as LC50 (lethal concentration to 50 per cent of exposed organisms). Sublethal effects levels are estimated from LC50s. The model was verified with available oil bioassay data. It was concluded that oil toxicity is a function of the aromatic content and composition in the oil as well as the fate and partitioning of those components in the environment. 81 refs., 19 tabs., 1 fig
Parameter Estimation for Single Diode Models of Photovoltaic Modules
Energy Technology Data Exchange (ETDEWEB)
Hansen, Clifford [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Photovoltaic and Distributed Systems Integration Dept.
2015-03-01
Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions. A single diode model requires a number of parameters to be estimated from measured I - V curves. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells. In contrast, module testing frequently records I - V curves over a wide range of irradi ance and temperature conditions which, when available , should also be used to parameterize the performance model. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves . We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module.
Groundwater Modelling For Recharge Estimation Using Satellite Based Evapotranspiration
Soheili, Mahmoud; (Tom) Rientjes, T. H. M.; (Christiaan) van der Tol, C.
2017-04-01
Groundwater movement is influenced by several factors and processes in the hydrological cycle, from which, recharge is of high relevance. Since the amount of aquifer extractable water directly relates to the recharge amount, estimation of recharge is a perquisite of groundwater resources management. Recharge is highly affected by water loss mechanisms the major of which is actual evapotranspiration (ETa). It is, therefore, essential to have detailed assessment of ETa impact on groundwater recharge. The objective of this study was to evaluate how recharge was affected when satellite-based evapotranspiration was used instead of in-situ based ETa in the Salland area, the Netherlands. The Methodology for Interactive Planning for Water Management (MIPWA) model setup which includes a groundwater model for the northern part of the Netherlands was used for recharge estimation. The Surface Energy Balance Algorithm for Land (SEBAL) based actual evapotranspiration maps from Waterschap Groot Salland were also used. Comparison of SEBAL based ETa estimates with in-situ abased estimates in the Netherlands showed that these SEBAL estimates were not reliable. As such results could not serve for calibrating root zone parameters in the CAPSIM model. The annual cumulative ETa map produced by the model showed that the maximum amount of evapotranspiration occurs in mixed forest areas in the northeast and a portion of central parts. Estimates ranged from 579 mm to a minimum of 0 mm in the highest elevated areas with woody vegetation in the southeast of the region. Variations in mean seasonal hydraulic head and groundwater level for each layer showed that the hydraulic gradient follows elevation in the Salland area from southeast (maximum) to northwest (minimum) of the region which depicts the groundwater flow direction. The mean seasonal water balance in CAPSIM part was evaluated to represent recharge estimation in the first layer. The highest recharge estimated flux was for autumn
Models for estimating photosynthesis parameters from in situ production profiles
Kovač, Žarko; Platt, Trevor; Sathyendranath, Shubha; Antunović, Suzana
2017-12-01
The rate of carbon assimilation in phytoplankton primary production models is mathematically prescribed with photosynthesis irradiance functions, which convert a light flux (energy) into a material flux (carbon). Information on this rate is contained in photosynthesis parameters: the initial slope and the assimilation number. The exactness of parameter values is crucial for precise calculation of primary production. Here we use a model of the daily production profile based on a suite of photosynthesis irradiance functions and extract photosynthesis parameters from in situ measured daily production profiles at the Hawaii Ocean Time-series station Aloha. For each function we recover parameter values, establish parameter distributions and quantify model skill. We observe that the choice of the photosynthesis irradiance function to estimate the photosynthesis parameters affects the magnitudes of parameter values as recovered from in situ profiles. We also tackle the problem of parameter exchange amongst the models and the effect it has on model performance. All models displayed little or no bias prior to parameter exchange, but significant bias following parameter exchange. The best model performance resulted from using optimal parameter values. Model formulation was extended further by accounting for spectral effects and deriving a spectral analytical solution for the daily production profile. The daily production profile was also formulated with time dependent growing biomass governed by a growth equation. The work on parameter recovery was further extended by exploring how to extract photosynthesis parameters from information on watercolumn production. It was demonstrated how to estimate parameter values based on a linearization of the full analytical solution for normalized watercolumn production and from the solution itself, without linearization. The paper complements previous works on photosynthesis irradiance models by analysing the skill and consistency of
Estimating Drilling Cost and Duration Using Copulas Dependencies Models
Directory of Open Access Journals (Sweden)
M. Al Kindi
2017-03-01
Full Text Available Estimation of drilling budget and duration is a high-level challenge for oil and gas industry. This is due to the many uncertain activities in the drilling procedure such as material prices, overhead cost, inflation, oil prices, well type, and depth of drilling. Therefore, it is essential to consider all these uncertain variables and the nature of relationships between them. This eventually leads into the minimization of the level of uncertainty and yet makes a "good" estimation points for budget and duration given the well type. In this paper, the copula probability theory is used in order to model the dependencies between cost/duration and MRI (mechanical risk index. The MRI is a mathematical computation, which relates various drilling factors such as: water depth, measured depth, true vertical depth in addition to mud weight and horizontal displacement. In general, the value of MRI is utilized as an input for the drilling cost and duration estimations. Therefore, modeling the uncertain dependencies between MRI and both cost and duration using copulas is important. The cost and duration estimates for each well were extracted from the copula dependency model where research study simulate over 10,000 scenarios. These new estimates were later compared to the actual data in order to validate the performance of the procedure. Most of the wells show moderate - weak relationship of MRI dependence, which means that the variation in these wells can be related to MRI but to the extent that it is not the primary source.
Deconvolution Estimation in Measurement Error Models: The R Package decon
Wang, Xiao-Feng; Wang, Bin
2011-01-01
Data from many scientific areas often come with measurement error. Density or distribution function estimation from contaminated data and nonparametric regression with errors-in-variables are two important topics in measurement error models. In this paper, we present a new software package decon for R, which contains a collection of functions that use the deconvolution kernel methods to deal with the measurement error problems. The functions allow the errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the fast Fourier transform algorithm for density estimation with error-free data to the deconvolution kernel estimation. We discuss the practical selection of the smoothing parameter in deconvolution methods and illustrate the use of the package through both simulated and real examples. PMID:21614139
Parameter estimation for groundwater models under uncertain irrigation data
Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.
System Level Modelling and Performance Estimation of Embedded Systems
DEFF Research Database (Denmark)
Tranberg-Hansen, Anders Sejer
The advances seen in the semiconductor industry within the last decade have brought the possibility of integrating evermore functionality onto a single chip forming functionally highly advanced embedded systems. These integration possibilities also imply that as the design complexity increases, so...... an efficient system level design methodology, a modelling framework for performance estimation and design space exploration at the system level is required. This thesis presents a novel component based modelling framework for system level modelling and performance estimation of embedded systems. The framework...... is performed by having the framework produce detailed quantitative information about the system model under investigation. The project is part of the national Danish research project, Danish Network of Embedded Systems (DaNES), which is funded by the Danish National Advanced Technology Foundation. The project...
The Impact of Statistical Leakage Models on Design Yield Estimation
Directory of Open Access Journals (Sweden)
Rouwaida Kanj
2011-01-01
Full Text Available Device mismatch and process variation models play a key role in determining the functionality and yield of sub-100 nm design. Average characteristics are often of interest, such as the average leakage current or the average read delay. However, detecting rare functional fails is critical for memory design and designers often seek techniques that enable accurately modeling such events. Extremely leaky devices can inflict functionality fails. The plurality of leaky devices on a bitline increase the dimensionality of the yield estimation problem. Simplified models are possible by adopting approximations to the underlying sum of lognormals. The implications of such approximations on tail probabilities may in turn bias the yield estimate. We review different closed form approximations and compare against the CDF matching method, which is shown to be most effective method for accurate statistical leakage modeling.
Estimation of traffic accident costs: a prompted model.
Hejazi, Rokhshad; Shamsudin, Mad Nasir; Radam, Alias; Rahim, Khalid Abdul; Ibrahim, Zelina Zaitun; Yazdani, Saeed
2013-01-01
Traffic accidents are the reason for 25% of unnatural deaths in Iran. The main objective of this study is to find a simple model for the estimation of economic costs especially in Islamic countries (like Iran) in a straightforward manner. The model can show the magnitude of traffic accident costs with monetary equivalent. Data were collected from different sources that included traffic police records, insurance companies and hospitals. The conceptual framework, in our study, was based on the method of Ayati. He used this method for the estimation of economic costs in Iran. We promoted his method via minimum variables. Our final model has only three available variables which can be taken from insurance companies and police records. The running model showed that the traffic accident costs were US$2.2 million in 2007 for our case study route.
Estimating a dynamic model of sex selection in China.
Ebenstein, Avraham
2011-05-01
High ratios of males to females in China, which have historically concerned researchers (Sen 1990), have increased in the wake of China's one-child policy, which began in 1979. Chinese policymakers are currently attempting to correct the imbalance in the sex ratio through initiatives that provide financial compensation to parents with daughters. Other scholars have advocated a relaxation of the one-child policy to allow more parents to have a son without engaging in sex selection. In this article, I present a model of fertility choice when parents have access to a sex-selection technology and face a mandated fertility limit. By exploiting variation in fines levied in China for unsanctioned births, I estimate the relative price of a son and daughter for mothers observed in China's census data (1982-2000). I find that a couple's first son is worth 1.42 years of income more than a first daughter, and the premium is highest among less-educated mothers and families engaged in agriculture. Simulations indicate that a subsidy of 1 year of income to families without a son would reduce the number of "missing girls" by 67% but impose an annual cost of 1.8% of Chinese gross domestic product (GDP). Alternatively, a three-child policy would reduce the number of "missing girls" by 56% but increase the fertility rate by 35%.
Cheng, Guang
2014-02-01
We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled nonparametrically, and propose an estimation procedure based on a spline approximation of the nonparametric part of the model and the generalized estimating equations (GEE). Although the model in consideration is natural and useful in many practical applications, the literature on this model is very limited because of challenges in dealing with dependent data for nonparametric additive models. We show that the proposed estimators are consistent and asymptotically normal even if the covariance structure is misspecified. An explicit consistent estimate of the asymptotic variance is also provided. Moreover, we derive the semiparametric efficiency score and information bound under general moment conditions. By showing that our estimators achieve the semiparametric information bound, we effectively establish their efficiency in a stronger sense than what is typically considered for GEE. The derivation of our asymptotic results relies heavily on the empirical processes tools that we develop for the longitudinal/clustered data. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2014 ISI/BS.
PDS-Modelling and Regional Bayesian Estimation of Extreme Rainfalls
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan; Harremoës, Poul
1994-01-01
Since 1979 a country-wide system of raingauges has been operated in Denmark in order to obtain a better basis for design and analysis of urban drainage systems. As an alternative to the traditional non-parametric approach the Partial Duration Series method is employed in the modelling of extreme ....... The application of the Bayesian approach is derived in case of both exponential and generalized Pareto distributed exceedances. Finally, the aspect of including economic perspectives in the estimation of the design events is briefly discussed....... in Denmark cannot be justified. In order to obtain an estimation procedure at non-monitored sites and to improve at-site estimates a regional Bayesian approach is adopted. The empirical regional distributions of the parameters in the Partial Duration Series model are used as prior information...
Fan, Xitao; Wang, Lin; Thompson, Bruce
1999-01-01
A Monte Carlo simulation study investigated the effects on 10 structural equation modeling fit indexes of sample size, estimation method, and model specification. Some fit indexes did not appear to be comparable, and it was apparent that estimation method strongly influenced almost all fit indexes examined, especially for misspecified models. (SLD)
Comparison of physically based catchment models for estimating Phosphorus losses
Nasr, Ahmed Elssidig; Bruen, Michael
2003-01-01
As part of a large EPA-funded research project, coordinated by TEAGASC, the Centre for Water Resources Research at UCD reviewed the available distributed physically based catchment models with a potential for use in estimating phosphorous losses for use in implementing the Water Framework Directive. Three models, representative of different levels of approach and complexity, were chosen and were implemented for a number of Irish catchments. This paper reports on (i) the lessons and experience...
Simplified evacuation model for estimating mitigation of early population exposures
International Nuclear Information System (INIS)
Strenge, D.L.
1980-12-01
The application of a simple evacuation model to the prediction of expected population exposures following acute releases of activity to the atmosphere is described. The evacuation model of Houston is coupled with a normalized Gaussian dispersion calculation to estimate the time integral of population exposure. The methodology described can be applied to specific sites to determine the expected reduction of population exposures due to evacuation
Comparison of two intelligent models to estimate the instantaneous ...
Indian Academy of Sciences (India)
... they are 85.46 (w/m2), 3.08 (w/m2) and 5.41, respectively. As the results indicate, both models are able to estimate the amount of radiation well, while the neural network has a higher accuracy. The output of the modes for six other cities of Iran, with similar climate conditions, also proves the ability of the proposed models.
Time-of-flight estimation based on covariance models
van der Heijden, Ferdinand; Tuquerres, G.; Regtien, Paulus P.L.
We address the problem of estimating the time-of-flight (ToF) of a waveform that is disturbed heavily by additional reflections from nearby objects. These additional reflections cause interference patterns that are difficult to predict. The introduction of a model for the reflection in terms of a
Empirical Models for the Estimation of Global Solar Radiation in ...
African Journals Online (AJOL)
Empirical Models for the Estimation of Global Solar Radiation in Yola, Nigeria. ... and average daily wind speed (WS) for the interval of three years (2010 – 2012) measured using various instruments for Yola of recorded data collected from the Center for Atmospheric Research (CAR), Anyigba are presented and analyzed.
Revised models and genetic parameter estimates for production and ...
African Journals Online (AJOL)
Genetic parameters for production and reproduction traits in the Elsenburg Dormer sheep stud were estimated using records of 11743 lambs born between 1943 and 2002. An animal model with direct and maternal additive, maternal permanent and temporary environmental effects was fitted for traits considered traits of the ...
Determining input values for a simple parametric model to estimate ...
African Journals Online (AJOL)
Estimating soil evaporation (Es) is an important part of modelling vineyard evapotranspiration for irrigation purposes. Furthermore, quantification of possible soil texture and trellis effects is essential. Daily Es from six topsoils packed into lysimeters was measured under grapevines on slanting and vertical trellises, ...
inverse gaussian model for small area estimation via gibbs sampling
African Journals Online (AJOL)
ADMIN
(1994) extended the work by Fries and. Bhattacharyya (1983) to include the maximum likelihood analysis of the two-factor inverse. Gaussian model for the unbalanced and interaction case for the estimation of small area parameters in finite populations. The object of this article is to develop a Bayesian approach for small ...
An Approach to Quality Estimation in Model-Based Development
DEFF Research Database (Denmark)
Holmegaard, Jens Peter; Koch, Peter; Ravn, Anders Peter
2004-01-01
We present an approach to estimation of parameters for design space exploration in Model-Based Development, where synthesis of a system is done in two stages. Component qualities like space, execution time or power consumption are defined in a repository by platform dependent values. Connectors...
Constrained Optimization Approaches to Estimation of Structural Models
DEFF Research Database (Denmark)
Iskhakov, Fedor; Rust, John; Schjerning, Bertel
2015-01-01
We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (SJ, 2012). They used an inefficient version of the nested fixed point algorithm that relies on successive app...
Constrained Optimization Approaches to Estimation of Structural Models
DEFF Research Database (Denmark)
Iskhakov, Fedor; Jinhyuk, Lee; Rust, John
2016-01-01
We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (SJ, 2012). Their implementation of the nested fixed point algorithm used successive approximations to solve t...
Performances of estimators of linear model with auto-correlated ...
African Journals Online (AJOL)
Performances of estimators of linear model with auto-correlated error terms when the independent variable is normal. ... On the other hand, the same slope coefficients β , under Generalized Least Squares (GLS) decreased with increased autocorrelation when the sample size T is small. Journal of the Nigerian Association ...
ON THE ESTIMATION AND PREDICTION IN MIXED LINEAR MODELS
Directory of Open Access Journals (Sweden)
LÓPEZ L.A.
1998-01-01
Full Text Available Beginning with the classical Gauss-Markov Linear Model for mixed effects and using the technique of the Lagrange multipliers to obtain an alternative method for the estimation of linear predictors. A structural method is also discussed in order to obtain the variance and covariance matrixes and their inverses.
Method of moments estimation of GO-GARCH models
Boswijk, H.P.; van der Weide, R.
2009-01-01
We propose a new estimation method for the factor loading matrix in generalized orthogonal GARCH (GO-GARCH) models. The method is based on the eigenvectors of a suitably defined sample autocorrelation matrix of squares and cross-products of the process. The method can therefore be easily applied to
Bayesian nonparametric estimation of hazard rate in monotone Aalen model
Czech Academy of Sciences Publication Activity Database
Timková, Jana
2014-01-01
Roč. 50, č. 6 (2014), s. 849-868 ISSN 0023-5954 Institutional support: RVO:67985556 Keywords : Aalen model * Bayesian estimation * MCMC Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.541, year: 2014 http://library.utia.cas.cz/separaty/2014/SI/timkova-0438210.pdf
Mathematical models for estimating radio channels utilization when ...
African Journals Online (AJOL)
Definition of the radio channel utilization indicator is given. Mathematical models for radio channels utilization assessment by real-time flows transfer in the wireless self-organized network are presented. Estimated experiments results according to the average radio channel utilization productivity with and without buffering of ...
Efficient Bayesian Estimation and Combination of GARCH-Type Models
D. David (David); L.F. Hoogerheide (Lennart)
2010-01-01
textabstractThis paper proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient procedure named AdMitIS. The methodology automatically constructs a mixture of Student-t distributions as an approximation
An improved COCOMO software cost estimation model | Duke ...
African Journals Online (AJOL)
In this paper, we discuss the methodologies adopted previously in software cost estimation using the COnstructive COst MOdels (COCOMOs). From our analysis, COCOMOs produce very high software development efforts, which eventually produce high software development costs. Consequently, we propose its extension, ...
Remote sensing estimates of impervious surfaces for pluvial flood modelling
DEFF Research Database (Denmark)
Kaspersen, Per Skougaard; Drews, Martin
This paper investigates the accuracy of medium resolution (MR) satellite imagery in estimating impervious surfaces for European cities at the detail required for pluvial flood modelling. Using remote sensing techniques enables precise and systematic quantification of the influence of the past 30...
Models for estimation of carbon sequestered by Cupressus ...
African Journals Online (AJOL)
This study compared models for estimating carbon sequestered aboveground in Cupressus lusitanica plantation stands at Wondo Genet College of Forestry and Natural Resources, Ethiopia. Relationships of carbon storage with tree component and stand age were also investigated. Thirty trees of three different ages (5, ...
Eigenspace perturbations for structural uncertainty estimation of turbulence closure models
Jofre, Lluis; Mishra, Aashwin; Iaccarino, Gianluca
2017-11-01
With the present state of computational resources, a purely numerical resolution of turbulent flows encountered in engineering applications is not viable. Consequently, investigations into turbulence rely on various degrees of modeling. Archetypal amongst these variable resolution approaches would be RANS models in two-equation closures, and subgrid-scale models in LES. However, owing to the simplifications introduced during model formulation, the fidelity of all such models is limited, and therefore the explicit quantification of the predictive uncertainty is essential. In such scenario, the ideal uncertainty estimation procedure must be agnostic to modeling resolution, methodology, and the nature or level of the model filter. The procedure should be able to give reliable prediction intervals for different Quantities of Interest, over varied flows and flow conditions, and at diametric levels of modeling resolution. In this talk, we present and substantiate the Eigenspace perturbation framework as an uncertainty estimation paradigm that meets these criteria. Commencing from a broad overview, we outline the details of this framework at different modeling resolution. Thence, using benchmark flows, along with engineering problems, the efficacy of this procedure is established. This research was partially supported by NNSA under the Predictive Science Academic Alliance Program (PSAAP) II, and by DARPA under the Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) project (technical monitor: Dr Fariba Fahroo).
Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation
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.
Sparse Estimation Using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Badiu, Mihai Alin
2015-01-01
-valued models, this paper proposes a GSM model - the Bessel K model - that induces concave penalty functions for the estimation of complex sparse signals. The properties of the Bessel K model are analyzed when it is applied to Type I and Type II estimation. This analysis reveals that, by tuning the parameters...... of the mixing pdf different penalty functions are invoked depending on the estimation type used, the value of the noise variance, and whether real or complex signals are estimated. Using the Bessel K model, we derive a sparse estimator based on a modification of the expectation-maximization algorithm formulated......In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regression task in real-valued signal models. Motivated by the relative scarcity of formal tools for SBL in complex...
Bayes estimation of the general hazard rate model
International Nuclear Information System (INIS)
Sarhan, A.
1999-01-01
In reliability theory and life testing models, the life time distributions are often specified by choosing a relevant hazard rate function. Here a general hazard rate function h(t)=a+bt c-1 , where c, a, b are constants greater than zero, is considered. The parameter c is assumed to be known. The Bayes estimators of (a,b) based on the data of type II/item-censored testing without replacement are obtained. A large simulation study using Monte Carlo Method is done to compare the performance of Bayes with regression estimators of (a,b). The criterion for comparison is made based on the Bayes risk associated with the respective estimator. Also, the influence of the number of failed items on the accuracy of the estimators (Bayes and regression) is investigated. Estimations for the parameters (a,b) of the linearly increasing hazard rate model h(t)=a+bt, where a, b are greater than zero, can be obtained as the special case, letting c=2
Tyre pressure monitoring using a dynamical model-based estimator
Reina, Giulio; Gentile, Angelo; Messina, Arcangelo
2015-04-01
In the last few years, various control systems have been investigated in the automotive field with the aim of increasing the level of safety and stability, avoid roll-over, and customise handling characteristics. One critical issue connected with their integration is the lack of state and parameter information. As an example, vehicle handling depends to a large extent on tyre inflation pressure. When inflation pressure drops, handling and comfort performance generally deteriorate. In addition, it results in an increase in fuel consumption and in a decrease in lifetime. Therefore, it is important to keep tyres within the normal inflation pressure range. This paper introduces a model-based approach to estimate online tyre inflation pressure. First, basic vertical dynamic modelling of the vehicle is discussed. Then, a parameter estimation framework for dynamic analysis is presented. Several important vehicle parameters including tyre inflation pressure can be estimated using the estimated states. This method aims to work during normal driving using information from standard sensors only. On the one hand, the driver is informed about the inflation pressure and he is warned for sudden changes. On the other hand, accurate estimation of the vehicle states is available as possible input to onboard control systems.
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
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan
1997-01-01
A regional estimation procedure that combines the index-flood concept with an empirical Bayes method for inferring regional information is introduced. The model is based on the partial duration series approach with generalized Pareto (GP) distributed exceedances. The prior information of the model...... parameters is inferred from regional data using generalized least squares (GLS) regression. Two different Bayesian T-year event estimators are introduced: a linear estimator that requires only some moments of the prior distributions to be specified and a parametric estimator that is based on specified...... families of prior distributions. The regional method is applied to flood records from 48 New Zealand catchments. In the case of a strongly heterogeneous intersite correlation structure, the GLS procedure provides a more efficient estimate of the regional GP shape parameter as compared to the usually...
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...... Carlo experiment. We find that estimation of the parameters in the transition function can be problematic but that there may be significant benefits in terms of forecast performance....
Parameter estimation in nonlinear models for pesticide degradation
International Nuclear Information System (INIS)
Richter, O.; Pestemer, W.; Bunte, D.; Diekkrueger, B.
1991-01-01
A wide class of environmental transfer models is formulated as ordinary or partial differential equations. With the availability of fast computers, the numerical solution of large systems became feasible. The main difficulty in performing a realistic and convincing simulation of the fate of a substance in the biosphere is not the implementation of numerical techniques but rather the incomplete data basis for parameter estimation. Parameter estimation is a synonym for statistical and numerical procedures to derive reasonable numerical values for model parameters from data. The classical method is the familiar linear regression technique which dates back to the 18th century. Because it is easy to handle, linear regression has long been established as a convenient tool for analysing relationships. However, the wide use of linear regression has led to an overemphasis of linear relationships. In nature, most relationships are nonlinear and linearization often gives a poor approximation of reality. Furthermore, pure regression models are not capable to map the dynamics of a process. Therefore, realistic models involve the evolution in time (and space). This leads in a natural way to the formulation of differential equations. To establish the link between data and dynamical models, numerical advanced parameter identification methods have been developed in recent years. This paper demonstrates the application of these techniques to estimation problems in the field of pesticide dynamics. (7 refs., 5 figs., 2 tabs.)
Novel mathematical model to estimate ball impact force in soccer.
Iga, Takahito; Nunome, Hiroyuki; Sano, Shinya; Sato, Nahoko; Ikegami, Yasuo
2017-11-22
To assess ball impact force during soccer kicking is important to quantify from both performance and chronic injury prevention perspectives. We aimed to verify the appropriateness of previous models used to estimate ball impact force and to propose an improved model to better capture the time history of ball impact force. A soccer ball was fired directly onto a force platform (10 kHz) at five realistic kicking ball velocities and ball behaviour was captured by a high-speed camera (5,000 Hz). The time history of ball impact force was estimated using three existing models and two new models. A new mathematical model that took into account a rapid change in ball surface area and heterogeneous ball deformation showed a distinctive advantage to estimate the peak forces and its occurrence times and to reproduce time history of ball impact forces more precisely, thereby reinforcing the possible mechanics of 'footballer's ankle'. Ball impact time was also systematically shortened when ball velocity increases in contrast to practical understanding for producing faster ball velocity, however, the aspect of ball contact time must be considered carefully from practical point of view.
A Bayesian Markov geostatistical model for estimation of hydrogeological properties
International Nuclear Information System (INIS)
Rosen, L.; Gustafson, G.
1996-01-01
A geostatistical methodology based on Markov-chain analysis and Bayesian statistics was developed for probability estimations of hydrogeological and geological properties in the siting process of a nuclear waste repository. The probability estimates have practical use in decision-making on issues such as siting, investigation programs, and construction design. The methodology is nonparametric which makes it possible to handle information that does not exhibit standard statistical distributions, as is often the case for classified information. Data do not need to meet the requirements on additivity and normality as with the geostatistical methods based on regionalized variable theory, e.g., kriging. The methodology also has a formal way for incorporating professional judgments through the use of Bayesian statistics, which allows for updating of prior estimates to posterior probabilities each time new information becomes available. A Bayesian Markov Geostatistical Model (BayMar) software was developed for implementation of the methodology in two and three dimensions. This paper gives (1) a theoretical description of the Bayesian Markov Geostatistical Model; (2) a short description of the BayMar software; and (3) an example of application of the model for estimating the suitability for repository establishment with respect to the three parameters of lithology, hydraulic conductivity, and rock quality designation index (RQD) at 400--500 meters below ground surface in an area around the Aespoe Hard Rock Laboratory in southeastern Sweden
Settumba, Stella Nalukwago; Sweeney, Sedona; Seeley, Janet; Biraro, Samuel; Mutungi, Gerald; Munderi, Paula; Grosskurth, Heiner; Vassall, Anna
2015-06-01
To explore the chronic disease services in Uganda: their level of utilisation, the total service costs and unit costs per visit. Full financial and economic cost data were collected from 12 facilities in two districts, from the provider's perspective. A combination of ingredients-based and step-down allocation costing approaches was used. The diseases under study were diabetes, hypertension, chronic obstructive pulmonary disease (COPD), epilepsy and HIV infection. Data were collected through a review of facility records, direct observation and structured interviews with health workers. Provision of chronic care services was concentrated at higher-level facilities. Excluding drugs, the total costs for NCD care fell below 2% of total facility costs. Unit costs per visit varied widely, both across different levels of the health system, and between facilities of the same level. This variability was driven by differences in clinical and drug prescribing practices. Most patients reported directly to higher-level facilities, bypassing nearby peripheral facilities. NCD services in Uganda are underfunded particularly at peripheral facilities. There is a need to estimate the budget impact of improving NCD care and to standardise treatment guidelines. © 2015 The Authors. Tropical Medicine & International Health Published by John Wiley & Sons Ltd.
Application of an estimation model to predict future transients at US nuclear power plants
International Nuclear Information System (INIS)
Hallbert, B.P.; Blackman, H.S.
1987-01-01
A model developed by R.A. Fisher was applied to a set of Licensee Event Reports (LERs) summarizing transient initiating events at US commercial nuclear power plants. The empirical Bayes model was examined to study the feasibility of estimating the number of categories of transients which have not yet occurred at nuclear power plants. An examination of the model's predictive ability using an existing sample of data provided support for use of the model to estimate future transients. The estimate indicates that an approximate fifteen percent increase in the number of categories of transient initiating events may be expected during the period 1983--1993, assuming a stable process of transients. Limitations of the model and other possible applications are discussed. 10 refs., 1 fig., 3 tabs
Franz, K. J.; Hogue, T. S.
2011-11-01
The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE), and the Shuffle Complex Evolution Metropolis (SCEM). Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
Directory of Open Access Journals (Sweden)
K. J. Franz
2011-11-01
Full Text Available The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE, and the Shuffle Complex Evolution Metropolis (SCEM. Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, J.; Engsted, T.
1999-07-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, Jan; Engsted, Tom [Aarhus School of Business, Aarhus (Denmark)
2001-01-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distributed lag (ARDL) model in estimating energy demand relationships. Recent research, however, shows that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are cointegrated. In this paper, we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using cointegration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the cointegration/ECM approach give very similar results. (Author)
International Nuclear Information System (INIS)
Barnett, C.S.
1991-01-01
The Double Contingency Principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative, intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive to search for a quantitative, probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies as a function of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess effectiveness of the DCP. (Author)
Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models
Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea
2014-05-01
Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.
Wycherley, Thomas; Ferguson, Megan; O'Dea, Kerin; McMahon, Emma; Liberato, Selma; Brimblecombe, Julie
2016-12-01
Determine how very-remote Indigenous community (RIC) food and beverage (F&B) turnover quantities and associated dietary intake estimates derived from only stores, compare with values derived from all community F&B providers. F&B turnover quantity and associated dietary intake estimates (energy, micro/macronutrients and major contributing food types) were derived from 12-months transaction data of all F&B providers in three RICs (NT, Australia). F&B turnover quantities and dietary intake estimates from only stores (plus only the primary store in multiple-store communities) were expressed as a proportion of complete F&B provider turnover values. Food types and macronutrient distribution (%E) estimates were quantitatively compared. Combined stores F&B turnover accounted for the majority of F&B quantity (98.1%) and absolute dietary intake estimates (energy [97.8%], macronutrients [≥96.7%] and micronutrients [≥83.8%]). Macronutrient distribution estimates from combined stores and only the primary store closely aligned complete provider estimates (≤0.9% absolute). Food types were similar using combined stores, primary store or complete provider turnover. Evaluating combined stores F&B turnover represents an efficient method to estimate total F&B turnover quantity and associated dietary intake in RICs. In multiple-store communities, evaluating only primary store F&B turnover provides an efficient estimate of macronutrient distribution and major food types. © 2016 Public Health Association of Australia.
Estimating Model Parameters of Adaptive Software Systems in Real-Time
Kumar, Dinesh; Tantawi, Asser; Zhang, Li
Adaptive software systems have the ability to adapt to changes in workload and execution environment. In order to perform resource management through model based control in such systems, an accurate mechanism for estimating the software system's model parameters is required. This paper deals with real-time estimation of a performance model for adaptive software systems that process multiple classes of transactional workload. First, insights in to the static performance model estimation problem are provided. Then an Extended Kalman Filter (EKF) design is combined with an open queueing network model to dynamically estimate the model parameters in real-time. Specific problems that are encountered in the case of multiple classes of workload are analyzed. These problems arise mainly due to the under-deterministic nature of the estimation problem. This motivates us to propose a modified design of the filter. Insights for choosing tuning parameters of the modified design, i.e., number of constraints and sampling intervals are provided. The modified filter design is shown to effectively tackle problems with multiple classes of workload through experiments.
Bayesian parameter estimation in dynamic population model via particle Markov chain Monte Carlo
Directory of Open Access Journals (Sweden)
Meng Gao
2012-12-01
Full Text Available In nature, population dynamics are subject to multiple sources of stochasticity. State-space models (SSMs provide an ideal framework for incorporating both environmental noises and measurement errors into dynamic population models. In this paper, we present a recently developed method, Particle Markov Chain Monte Carlo (Particle MCMC, for parameter estimation in nonlinear SSMs. We use one effective algorithm of Particle MCMC, Particle Gibbs sampling algorithm, to estimate the parameters of a state-space model of population dynamics. The posterior distributions of parameters are derived given the conjugate prior distribution. Numerical simulations showed that the model parameters can be accurately estimated, no matter the deterministic model is stable, periodic or chaotic. Moreover, we fit the model to 16 representative time series from Global Population Dynamics Database (GPDD. It is verified that the results of parameter and state estimation using Particle Gibbs sampling algorithm are satisfactory for a majority of time series. For other time series, the quality of parameter estimation can also be improved, if prior knowledge is constrained. In conclusion, Particle Gibbs sampling algorithm provides a new Bayesian parameter inference method for studying population dynamics.
Energy Technology Data Exchange (ETDEWEB)
Galanti, Eli; Kaspi, Yohai [Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot (Israel); Durante, Daniele; Finocchiaro, Stefano; Iess, Luciano, E-mail: eli.galanti@weizmann.ac.il [Dipartimento di Ingegneria Meccanica e Aerospaziale, Sapienza Universita di Roma, Rome (Italy)
2017-07-01
The upcoming Juno spacecraft measurements have the potential of improving our knowledge of Jupiter’s gravity field. The analysis of the Juno Doppler data will provide a very accurate reconstruction of spatial gravity variations, but these measurements will be very accurate only over a limited latitudinal range. In order to deduce the full gravity field of Jupiter, additional information needs to be incorporated into the analysis, especially regarding the Jovian flow structure and its depth, which can influence the measured gravity field. In this study we propose a new iterative method for the estimation of the Jupiter gravity field, using a simulated Juno trajectory, a trajectory estimation model, and an adjoint-based inverse model for the flow dynamics. We test this method both for zonal harmonics only and with a full gravity field including tesseral harmonics. The results show that this method can fit some of the gravitational harmonics better to the “measured” harmonics, mainly because of the added information from the dynamical model, which includes the flow structure. Thus, it is suggested that the method presented here has the potential of improving the accuracy of the expected gravity harmonics estimated from the Juno and Cassini radio science experiments.
Modeling extreme events: Sample fraction adaptive choice in parameter estimation
Neves, Manuela; Gomes, Ivette; Figueiredo, Fernanda; Gomes, Dora Prata
2012-09-01
When modeling extreme events there are a few primordial parameters, among which we refer the extreme value index and the extremal index. The extreme value index measures the right tail-weight of the underlying distribution and the extremal index characterizes the degree of local dependence in the extremes of a stationary sequence. Most of the semi-parametric estimators of these parameters show the same type of behaviour: nice asymptotic properties, but a high variance for small values of k, the number of upper order statistics to be used in the estimation, and a high bias for large values of k. This shows a real need for the choice of k. Choosing some well-known estimators of those parameters we revisit the application of a heuristic algorithm for the adaptive choice of k. The procedure is applied to some simulated samples as well as to some real data sets.
Modeling of Closed-Die Forging for Estimating Forging Load
Sheth, Debashish; Das, Santanu; Chatterjee, Avik; Bhattacharya, Anirban
2017-02-01
Closed die forging is one common metal forming process used for making a range of products. Enough load is to exert on the billet for deforming the material. This forging load is dependent on work material property and frictional characteristics of the work material with the punch and die. Several researchers worked on estimation of forging load for specific products under different process variables. Experimental data on deformation resistance and friction were used to calculate the load. In this work, theoretical estimation of forging load is made to compare this value with that obtained through LS-DYNA model facilitating the finite element analysis. Theoretical work uses slab method to assess forging load for an axi-symmetric upsetting job made of lead. Theoretical forging load estimate shows slightly higher value than the experimental one; however, simulation shows quite close matching with experimental forging load, indicating possibility of wide use of this simulation software.
Nonparametric Estimation of Distributions in Random Effects Models
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.
Carotenuto, Federico; Gualtieri, Giovanni; Miglietta, Franco; Riccio, Angelo; Toscano, Piero; Wohlfahrt, Georg; Gioli, Beniamino
2018-02-22
CO 2 remains the greenhouse gas that contributes most to anthropogenic global warming, and the evaluation of its emissions is of major interest to both research and regulatory purposes. Emission inventories generally provide quite reliable estimates of CO 2 emissions. However, because of intrinsic uncertainties associated with these estimates, it is of great importance to validate emission inventories against independent estimates. This paper describes an integrated approach combining aircraft measurements and a puff dispersion modelling framework by considering a CO 2 industrial point source, located in Biganos, France. CO 2 density measurements were obtained by applying the mass balance method, while CO 2 emission estimates were derived by implementing the CALMET/CALPUFF model chain. For the latter, three meteorological initializations were used: (i) WRF-modelled outputs initialized by ECMWF reanalyses; (ii) WRF-modelled outputs initialized by CFSR reanalyses and (iii) local in situ observations. Governmental inventorial data were used as reference for all applications. The strengths and weaknesses of the different approaches and how they affect emission estimation uncertainty were investigated. The mass balance based on aircraft measurements was quite succesful in capturing the point source emission strength (at worst with a 16% bias), while the accuracy of the dispersion modelling, markedly when using ECMWF initialization through the WRF model, was only slightly lower (estimation with an 18% bias). The analysis will help in highlighting some methodological best practices that can be used as guidelines for future experiments.
A screening-level modeling approach to estimate nitrogen ...
This paper presents a screening-level modeling approach that can be used to rapidly estimate nutrient loading and assess numerical nutrient standard exceedance risk of surface waters leading to potential classification as impaired for designated use. It can also be used to explore best management practice (BMP) implementation to reduce loading. The modeling framework uses a hybrid statistical and process based approach to estimate source of pollutants, their transport and decay in the terrestrial and aquatic parts of watersheds. The framework is developed in the ArcGIS environment and is based on the total maximum daily load (TMDL) balance model. Nitrogen (N) is currently addressed in the framework, referred to as WQM-TMDL-N. Loading for each catchment includes non-point sources (NPS) and point sources (PS). NPS loading is estimated using export coefficient or event mean concentration methods depending on the temporal scales, i.e., annual or daily. Loading from atmospheric deposition is also included. The probability of a nutrient load to exceed a target load is evaluated using probabilistic risk assessment, by including the uncertainty associated with export coefficients of various land uses. The computed risk data can be visualized as spatial maps which show the load exceedance probability for all stream segments. In an application of this modeling approach to the Tippecanoe River watershed in Indiana, USA, total nitrogen (TN) loading and risk of standard exce
Forward models and state estimation in compensatory eye movements
Directory of Open Access Journals (Sweden)
Maarten A Frens
2009-11-01
Full Text Available The compensatory eye movement system maintains a stable retinal image, integrating information from different sensory modalities to compensate for head movements. Inspired by recent models of physiology of limb movements, we suggest that compensatory eye movements (CEM can be modeled as a control system with three essential building blocks: a forward model that predicts the effects of motor commands; a state estimator that integrates sensory feedback into this prediction; and, a feedback controller that translates a state estimate into motor commands. We propose a specific mapping of nuclei within the CEM system onto these control functions. Specifically, we suggest that the Flocculus is responsible for generating the forward model prediction and that the Vestibular Nuclei integrate sensory feedback to generate an estimate of current state. Finally, the brainstem motor nuclei – in the case of horizontal compensation this means the Abducens Nucleus and the Nucleus Prepositus Hypoglossi – implement a feedback controller, translating state into motor commands. While these efforts to understand the physiological control system as a feedback control system are in their infancy, there is the intriguing possibility that compensatory eye movements and targeted voluntary movements use the same cerebellar circuitry in fundamentally different ways.
Badea, Alexandra; Kane, Lauren; Anderson, Robert J; Qi, Yi; Foster, Mark; Cofer, Gary P; Medvitz, Neil; Buckley, Anne F; Badea, Andreas K; Wetsel, William C; Colton, Carol A
2016-11-15
Multivariate biomarkers are needed for detecting Alzheimer's disease (AD), understanding its etiology, and quantifying the effect of therapies. Mouse models provide opportunities to study characteristics of AD in well-controlled environments that can help facilitate development of early interventions. The CVN-AD mouse model replicates multiple AD hallmark pathologies, and we identified multivariate biomarkers characterizing a brain circuit disruption predictive of cognitive decline. In vivo and ex vivo magnetic resonance imaging (MRI) revealed that CVN-AD mice replicate the hippocampal atrophy (6%), characteristic of humans with AD, and also present changes in subcortical areas. The largest effect was in the fornix (23% smaller), which connects the septum, hippocampus, and hypothalamus. In characterizing the fornix with diffusion tensor imaging, fractional anisotropy was most sensitive (20% reduction), followed by radial (15%) and axial diffusivity (2%), in detecting pathological changes. These findings were strengthened by optical microscopy and ultrastructural analyses. Ultrastructual analysis provided estimates of axonal density, diameters, and myelination-through the g-ratio, defined as the ratio between the axonal diameter, and the diameter of the axon plus the myelin sheath. The fornix had reduced axonal density (47% fewer), axonal degeneration (13% larger axons), and abnormal myelination (1.5% smaller g-ratios). CD68 staining showed that white matter pathology could be secondary to neuronal degeneration, or due to direct microglial attack. In conclusion, these findings strengthen the hypothesis that the fornix plays a role in AD, and can be used as a disease biomarker and as a target for therapy. Copyright © 2016 Elsevier Inc. All rights reserved.
Estimating Parameters in Physical Models through Bayesian Inversion: A Complete Example
Allmaras, Moritz
2013-02-07
All mathematical models of real-world phenomena contain parameters that need to be estimated from measurements, either for realistic predictions or simply to understand the characteristics of the model. Bayesian statistics provides a framework for parameter estimation in which uncertainties about models and measurements are translated into uncertainties in estimates of parameters. This paper provides a simple, step-by-step example-starting from a physical experiment and going through all of the mathematics-to explain the use of Bayesian techniques for estimating the coefficients of gravity and air friction in the equations describing a falling body. In the experiment we dropped an object from a known height and recorded the free fall using a video camera. The video recording was analyzed frame by frame to obtain the distance the body had fallen as a function of time, including measures of uncertainty in our data that we describe as probability densities. We explain the decisions behind the various choices of probability distributions and relate them to observed phenomena. Our measured data are then combined with a mathematical model of a falling body to obtain probability densities on the space of parameters we seek to estimate. We interpret these results and discuss sources of errors in our estimation procedure. © 2013 Society for Industrial and Applied Mathematics.
Wilson, J. P.; Fischer, W. W.
2010-12-01
Fossil plants provide useful proxies of Earth’s climate because plants are closely connected, through physiology and morphology, to the environments in which they lived. Recent advances in quantitative hydraulic models of plant water transport provide new insight into the history of climate by allowing fossils to speak directly to environmental conditions based on preserved internal anatomy. We report results of a quantitative hydraulic model applied to one of the earliest terrestrial plants preserved in three dimensions, the ~396 million-year-old vascular plant Asteroxylon mackei. This model combines equations describing the rate of fluid flow through plant tissues with detailed observations of plant anatomy; this allows quantitative estimates of two critical aspects of plant function. First and foremost, results from these models quantify the supply of water to evaporative surfaces; second, results describe the ability of plant vascular systems to resist tensile damage from extreme environmental events, such as drought or frost. This approach permits quantitative comparisons of functional aspects of Asteroxylon with other extinct and extant plants, informs the quality of plant-based environmental proxies, and provides concrete data that can be input into climate models. Results indicate that despite their small size, water transport cells in Asteroxylon could supply a large volume of water to the plant's leaves--even greater than cells from some later-evolved seed plants. The smallest Asteroxylon tracheids have conductivities exceeding 0.015 m^2 / MPa * s, whereas Paleozoic conifer tracheids do not reach this threshold until they are three times wider. However, this increase in conductivity came at the cost of little to no adaptations for transport safety, placing the plant’s vegetative organs in jeopardy during drought events. Analysis of the thickness-to-span ratio of Asteroxylon’s tracheids suggests that environmental conditions of reduced relative
Sparse estimation of model-based diffuse thermal dust emission
Irfan, Melis O.; Bobin, Jérôme
2018-03-01
Component separation for the Planck High Frequency Instrument (HFI) data is primarily concerned with the estimation of thermal dust emission, which requires the separation of thermal dust from the cosmic infrared background (CIB). For that purpose, current estimation methods rely on filtering techniques to decouple thermal dust emission from CIB anisotropies, which tend to yield a smooth, low-resolution, estimation of the dust emission. In this paper, we present a new parameter estimation method, premise: Parameter Recovery Exploiting Model Informed Sparse Estimates. This method exploits the sparse nature of thermal dust emission to calculate all-sky maps of thermal dust temperature, spectral index, and optical depth at 353 GHz. premise is evaluated and validated on full-sky simulated data. We find the percentage difference between the premise results and the true values to be 2.8, 5.7, and 7.2 per cent at the 1σ level across the full sky for thermal dust temperature, spectral index, and optical depth at 353 GHz, respectively. A comparison between premise and a GNILC-like method over selected regions of our sky simulation reveals that both methods perform comparably within high signal-to-noise regions. However, outside of the Galactic plane, premise is seen to outperform the GNILC-like method with increasing success as the signal-to-noise ratio worsens.
Performance of monitoring networks estimated from a Gaussian plume model
International Nuclear Information System (INIS)
Seebregts, A.J.; Hienen, J.F.A.
1990-10-01
In support of the ECN study on monitoring strategies after nuclear accidents, the present report describes the analysis of the performance of a monitoring network in a square grid. This network is used to estimate the distribution of the deposition pattern after a release of radioactivity into the atmosphere. The analysis is based upon a single release, a constant wind direction and an atmospheric dispersion according to a simplified Gaussian plume model. A technique is introduced to estimate the parameters in this Gaussian model based upon measurements at specific monitoring locations and linear regression, although this model is intrinsically non-linear. With these estimated parameters and the Gaussian model the distribution of the contamination due to deposition can be estimated. To investigate the relation between the network and the accuracy of the estimates for the deposition, deposition data have been generated by the Gaussian model, including a measurement error by a Monte Carlo simulation and this procedure has been repeated for several grid sizes, dispersion conditions, number of measurements per location, and errors per single measurement. The present technique has also been applied for the mesh sizes of two networks in the Netherlands, viz. the Landelijk Meetnet Radioaciviteit (National Measurement Network on Radioactivity, mesh size approx. 35 km) and the proposed Landelijk Meetnet Nucleaire Incidenten (National Measurement Network on Nuclear Incidents, mesh size approx. 15 km). The results show accuracies of 11 and 7 percent, respectively, if monitoring locations are used more than 10 km away from the postulated accident site. These figures are based upon 3 measurements per location and a dispersion during neutral weather with a wind velocity of 4 m/s. For stable weather conditions and low wind velocities, i.e. a small plume, the calculated accuracies are at least a factor 1.5 worse.The present type of analysis makes a cost-benefit approach to the
Meta-analysis of choice set generation effects on route choice model estimates and predictions
DEFF Research Database (Denmark)
Prato, Carlo Giacomo
2012-01-01
are applied for model estimation and results are compared to the ‘true model estimates’. Last, predictions from the simulation of models estimated with objective choice sets are compared to the ‘postulated predicted routes’. A meta-analytical approach allows synthesizing the effect of judgments......Large scale applications of behaviorally realistic transport models pose several challenges to transport modelers on both the demand and the supply sides. On the supply side, path-based solutions to the user assignment equilibrium problem help modelers in enhancing the route choice behavior...... modeling, but require them to generate choice sets by selecting a path generation technique and its parameters according to personal judgments. This paper proposes a methodology and an experimental setting to provide general indications about objective judgments for an effective route choice set generation...
Le, Vu H.; Buscaglia, Robert; Chaires, Jonathan B.; Lewis, Edwin A.
2013-01-01
Isothermal Titration Calorimetry, ITC, is a powerful technique that can be used to estimate a complete set of thermodynamic parameters (e.g. Keq (or ΔG), ΔH, ΔS, and n) for a ligand binding interaction described by a thermodynamic model. Thermodynamic models are constructed by combination of equilibrium constant, mass balance, and charge balance equations for the system under study. Commercial ITC instruments are supplied with software that includes a number of simple interaction models, for example one binding site, two binding sites, sequential sites, and n-independent binding sites. More complex models for example, three or more binding sites, one site with multiple binding mechanisms, linked equilibria, or equilibria involving macromolecular conformational selection through ligand binding need to be developed on a case by case basis by the ITC user. In this paper we provide an algorithm (and a link to our MATLAB program) for the non-linear regression analysis of a multiple binding site model with up to four overlapping binding equilibria. Error analysis demonstrates that fitting ITC data for multiple parameters (e.g. up to nine parameters in the three binding site model) yields thermodynamic parameters with acceptable accuracy. PMID:23262283
Instrumental variables estimation under a structural Cox model
DEFF Research Database (Denmark)
Martinussen, Torben; Nørbo Sørensen, Ditte; Vansteelandt, Stijn
2017-01-01
Instrumental variable (IV) analysis is an increasingly popular tool for inferring the effect of an exposure on an outcome, as witnessed by the growing number of IV applications in epidemiology, for instance. The majority of IV analyses of time-to-event endpoints are, however, dominated by heuristic...... and instruments. We propose a novel class of estimators and derive their asymptotic properties. The methodology is illustrated using two real data applications, and using simulated data....... approaches. More rigorous proposals have either sidestepped the Cox model, or considered it within a restrictive context with dichotomous exposure and instrument, amongst other limitations. The aim of this article is to reconsider IV estimation under a structural Cox model, allowing for arbitrary exposure...
Modelling, Estimation and Control of Networked Complex Systems
Chiuso, Alessandro; Frasca, Mattia; Rizzo, Alessandro; Schenato, Luca; Zampieri, Sandro
2009-01-01
The paradigm of complexity is pervading both science and engineering, leading to the emergence of novel approaches oriented at the development of a systemic view of the phenomena under study; the definition of powerful tools for modelling, estimation, and control; and the cross-fertilization of different disciplines and approaches. This book is devoted to networked systems which are one of the most promising paradigms of complexity. It is demonstrated that complex, dynamical networks are powerful tools to model, estimate, and control many interesting phenomena, like agent coordination, synchronization, social and economics events, networks of critical infrastructures, resources allocation, information processing, or control over communication networks. Moreover, it is shown how the recent technological advances in wireless communication and decreasing in cost and size of electronic devices are promoting the appearance of large inexpensive interconnected systems, each with computational, sensing and mobile cap...
Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering
El Gharamti, Mohamad
2013-10-01
Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the
Estimation Model for Concrete Slump Recovery by Using Superplasticizer
Chaiyakrit Raoupatham; Ram Hari Dhakal; Chalermchai Wanichlamlert
2015-01-01
This paper aimed to introduce the solution of concrete slump recovery using chemical admixture type-F (superplasticizer, naphthalene base) to the practice in order to solve unusable concrete problem due to concrete loss its slump, especially for those tropical countries that have faster slump loss rate. In the other hand, randomly adding superplasticizer into concrete can cause concrete to segregate. Therefore, this paper also develops the estimation model used to calcula...
MATHEMATICAL MODEL FOR ESTIMATION OF MECHANICAL SYSTEM CONDITION IN DYNAMICS
Directory of Open Access Journals (Sweden)
D. N. Mironov
2011-01-01
Full Text Available The paper considers an estimation of a complicated mechanical system condition in dynamics with due account of material degradation and accumulation of micro-damages. An element of continuous medium has been simulated and described with the help of a discrete element. The paper contains description of a model for determination of mechanical system longevity in accordance with number of cycles and operational period.
Estimation of Continuous Time Models in Economics: an Overview
Clifford R. Wymer
2009-01-01
The dynamics of economic behaviour is often developed in theory as a continuous time system. Rigorous estimation and testing of such systems, and the analysis of some aspects of their properties, is of particular importance in distinguishing between competing hypotheses and the resulting models. The consequences for the international economy during the past eighteen months of failures in the financial sector, and particularly the banking sector, make it essential that the dynamics of financia...
Estimation and Inference for Very Large Linear Mixed Effects Models
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...
Top-down estimates of European CH4 and N2O emissions based on four different inverse models
Bergamaschi, P.; Corazza, M.; Karstens, U.; Athanassiadou, M.; Thompson, R.L.; Pison, I.; Manning, A.J.; Bousquet, P.; Segers, A.; Vermeulen, A.T.; Janssens-Maenhout, G.; Schmidt, M.; Ramonet, M.; Meinhardt, F.; Aalto, T.; Haszpra, L.; Moncrieff, J.; Popa, M.E.; Lowry, D.; Steinbacher, M.; Jordan, A.; O'Doherty, S.; Piacentino, S.; Dlugokencky, E.
2015-01-01
European CH4 and N2O emissions are estimated for 2006 and 2007 using four inverse modelling systems, based on different global and regional Eulerian and Lagrangian transport models. This ensemble approach is designed to provide more realistic estimates of the overall uncertainties in the derived
A High Effective Fuzzy Synthetic Evaluation Multi-model Estimation
Directory of Open Access Journals (Sweden)
Yang LIU
2014-01-01
Full Text Available In view of the questions that the algorithm flow of variable structure multi-model method (VSMM is too complex and the tracking performance is inefficient and therefore it is so difficult to apply VSMM into installing equipment. The paper presents a high-performance variable structure multi-model method basing on multi-factor fuzzy synthetic evaluation (HEFS_VSMM. Under the guidance of variable structure method, HEFS_VSMM uses the technique of multi-factor fuzzy synthetic evaluation in the strategy of model set adaptive to select the appropriate model set in real time and reduce the computation complexity of the model evaluation, firstly. Secondly, select the model set center according to the evaluation results of each model and set the property value for current model set. Thirdly, choose different processes basing on the current model set property value to simplify the logical complexity of the algorithm. At last, the algorithm gets the total estimation by the theories of optimal information fusion on the above-mentioned processing results. The results of simulation show that, compared with the FSMM and EMA, the mean of estimation error belonging to position, velocity and acceleration in the HEFS_VSMM is improved from -0.029 (m, -0.350 (m/s, -10.051(m/s2 to -0.023 (m, 0.052 (m/s, -5.531 (m/s2. The algorithm cycle is reduced from 0.0051(s to 0.0025 (s.
Estimating radiation and temperature data for crop simulation model
International Nuclear Information System (INIS)
Ferrer, A.B.; Centeno, H.G.S.; Sheehy, J.E.
1996-01-01
Weather (radiation and temperature) and crop characteristics determine the potential production of an irrigated rice crop. Daily weather data are important inputs to ORYZA 1, an eco-physiological crop model. However, in most cases, missing values occur and sometimes daily weather data are not readily available. More than 20 years of historic daily weather data had been collected from six stations in the Philippines -- Albay, Butuan, Munoz, Batac, Aborlan, and Los Banos. Methods to estimate daily weather data values were made by deriving long-term monthly means and (1) using the same value per month, (2) linearly interpolating between months, and (3) using SIMMETEO weather generator. A validated ORYZA 1 was run using actual daily weather data. The model was run again using weather data obtained from each estimation procedure and the predicted yields from the different simulation runs were compared. The yield predicted using the different weather data sets for each site difference by as much as 20 percent. Among the three estimation procedures used, the interpolated monthly mean values of weather data gave results comparable with those of model runs using actual weather data
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.
[Foundation of preoperative prognosis estimation model for glioblastoma multiforme].
Jiang, H H; Feng, G Y; Liu, D; Ren, X H; Cui, Y; Lin, S
2017-08-15
Objective: This study explored the preoperative prognostic factors of patients with glioblastoma multiforme (GBM) in order to propose a preoperative prognosis estimation model. Methods: The clinical data of 416 patients diagnosed with GBM in Beijing Tiantan Hospital affiliated to Capital Medical University from 2008 to 2015 were retrospectively reviewed.A total of nine factors: gender, age, duration of symptoms, preoperative epilepsy, preoperative muscle weakness, preoperative headache, preoperative KPS score, tumor location and tumor diameter were enrolled in the survival analysis.The significant factors identified by Kaplan-Meier plot were further collected in the multivariate Cox regression analysis.On the basis of multivariate analysis results, a preoperative prognosis estimation model was founded. Results: Univariate analysis showed that Age ≥50 years, without preoperative epilepsy, tumor located in non-frontotemporal lobe, tumor diameter ≥6 cm and preoperative KPS score preoperative epilepsy, tumor located in non-frontotemporal lobe were independent risk factors ( P <0.05). The prognostic estimation model based on the independent risk factors divided the whole cohort into three subgroups with different survival ( P <0.001). Conclusions: The more risk factors, the higher score but poorer prognosis. Patients in the high-risk group had lower gross total resection degree but higher rate of postoperative complications, which suggested that aggressive resection was not suitable for high-risk patients.
Ranger, Jochen; Kuhn, Jörg-Tobias; Szardenings, Carsten
2016-05-01
Psychological tests are usually analysed with item response models. Recently, some alternative measurement models have been proposed that were derived from cognitive process models developed in experimental psychology. These models consider the responses but also the response times of the test takers. Two such models are the Q-diffusion model and the D-diffusion model. Both models can be calibrated with the diffIRT package of the R statistical environment via marginal maximum likelihood (MML) estimation. In this manuscript, an alternative approach to model calibration is proposed. The approach is based on weighted least squares estimation and parallels the standard estimation approach in structural equation modelling. Estimates are determined by minimizing the discrepancy between the observed and the implied covariance matrix. The estimator is simple to implement, consistent, and asymptotically normally distributed. Least squares estimation also provides a test of model fit by comparing the observed and implied covariance matrix. The estimator and the test of model fit are evaluated in a simulation study. Although parameter recovery is good, the estimator is less efficient than the MML estimator. © 2016 The British Psychological Society.
Bayesian model selection: Evidence estimation based on DREAM simulation and bridge sampling
Volpi, Elena; Schoups, Gerrit; Firmani, Giovanni; Vrugt, Jasper A.
2017-04-01
Bayesian inference has found widespread application in Earth and Environmental Systems Modeling, providing an effective tool for prediction, data assimilation, parameter estimation, uncertainty analysis and hypothesis testing. Under multiple competing hypotheses, the Bayesian approach also provides an attractive alternative to traditional information criteria (e.g. AIC, BIC) for model selection. The key variable for Bayesian model selection is the evidence (or marginal likelihood) that is the normalizing constant in the denominator of Bayes theorem; while it is fundamental for model selection, the evidence is not required for Bayesian inference. It is computed for each hypothesis (model) by averaging the likelihood function over the prior parameter distribution, rather than maximizing it as by information criteria; the larger a model evidence the more support it receives among a collection of hypothesis as the simulated values assign relatively high probability density to the observed data. Hence, the evidence naturally acts as an Occam's razor, preferring simpler and more constrained models against the selection of over-fitted ones by information criteria that incorporate only the likelihood maximum. Since it is not particularly easy to estimate the evidence in practice, Bayesian model selection via the marginal likelihood has not yet found mainstream use. We illustrate here the properties of a new estimator of the Bayesian model evidence, which provides robust and unbiased estimates of the marginal likelihood; the method is coined Gaussian Mixture Importance Sampling (GMIS). GMIS uses multidimensional numerical integration of the posterior parameter distribution via bridge sampling (a generalization of importance sampling) of a mixture distribution fitted to samples of the posterior distribution derived from the DREAM algorithm (Vrugt et al., 2008; 2009). Some illustrative examples are presented to show the robustness and superiority of the GMIS estimator with
Bayesian parameter estimation and interpretation for an intermediate model of tree-ring width
Directory of Open Access Journals (Sweden)
S. E. Tolwinski-Ward
2013-07-01
Full Text Available We present a Bayesian model for estimating the parameters of the VS-Lite forward model of tree-ring width for a particular chronology and its local climatology. The scheme also provides information about the uncertainty of the parameter estimates, as well as the model error in representing the observed proxy time series. By inferring VS-Lite's parameters independently for synthetically generated ring-width series at several hundred sites across the United States, we show that the algorithm is skillful. We also infer optimal parameter values for modeling observed ring-width data at the same network of sites. The estimated parameter values covary in physical space, and their locations in multidimensional parameter space provide insight into the dominant climatic controls on modeled tree-ring growth at each site as well as the stability of those controls. The estimation procedure is useful for forward and inverse modeling studies using VS-Lite to quantify the full range of model uncertainty stemming from its parameterization.
Interpolation of Missing Precipitation Data Using Kernel Estimations for Hydrologic Modeling
Directory of Open Access Journals (Sweden)
Hyojin Lee
2015-01-01
Full Text Available Precipitation is the main factor that drives hydrologic modeling; therefore, missing precipitation data can cause malfunctions in hydrologic modeling. Although interpolation of missing precipitation data is recognized as an important research topic, only a few methods follow a regression approach. In this study, daily precipitation data were interpolated using five different kernel functions, namely, Epanechnikov, Quartic, Triweight, Tricube, and Cosine, to estimate missing precipitation data. This study also presents an assessment that compares estimation of missing precipitation data through Kth nearest neighborhood (KNN regression to the five different kernel estimations and their performance in simulating streamflow using the Soil Water Assessment Tool (SWAT hydrologic model. The results show that the kernel approaches provide higher quality interpolation of precipitation data compared with the KNN regression approach, in terms of both statistical data assessment and hydrologic modeling performance.
Time-varying coefficient estimation in SURE models. Application to portfolio management
DEFF Research Database (Denmark)
Casas, Isabel; Ferreira, Eva; Orbe, Susan
This paper provides a detailed analysis of the asymptotic properties of a kernel estimator for a Seemingly Unrelated Regression Equations model with time-varying coefficients (tv-SURE) under very general conditions. Theoretical results together with a simulation study differentiates the cases for...
Overmars, K.P.; Tabeau, A.A.; Stehfest, E.; Meijl, van J.C.M.
2012-01-01
Estimates for deforestation and forest degradation were shown to account for about 17% of greenhouse gas emissions. The implementation of REDD is suggested to provide substantial emission reductions at low costs. Proper calculation of such a costs requires integrated modeling approach involving
Locatelli, R.; Bousquet, P.; Chevallier, F.; Fortems-Cheney, A.; Szopa, S.; Saunois, M.; Agusti-Panareda, A.; Bergmann, D.; Bian, H.; Cameron-Smith, P.; Chipperfield, M.P.; Gloor, E.; Houweling, S.; Kawa, S.R.; Krol, M.C.; Patra, P.K.; Prinn, R.G.; Rigby, M.; Saito, R.; Wilson, C.
2013-01-01
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise,
Macho, Siegfried; Ledermann, Thomas
2011-01-01
The phantom model approach for estimating, testing, and comparing specific effects within structural equation models (SEMs) is presented. The rationale underlying this novel method consists in representing the specific effect to be assessed as a total effect within a separate latent variable model, the phantom model that is added to the main…
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python
Directory of Open Access Journals (Sweden)
Thomas V Wiecki
2013-08-01
Full Text Available The diffusion model is a commonly used tool to infer latent psychological processes underlying decision making, and to link them to neural mechanisms based on reaction times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of reaction time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model, which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject / condition than non-hierarchical method, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g. fMRI influence decision making parameters. This paper will first describe the theoretical background of drift-diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the chi-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs
Challenges in Species Tree Estimation Under the Multispecies Coalescent Model.
Xu, Bo; Yang, Ziheng
2016-12-01
The multispecies coalescent (MSC) model has emerged as a powerful framework for inferring species phylogenies while accounting for ancestral polymorphism and gene tree-species tree conflict. A number of methods have been developed in the past few years to estimate the species tree under the MSC. The full likelihood methods (including maximum likelihood and Bayesian inference) average over the unknown gene trees and accommodate their uncertainties properly but involve intensive computation. The approximate or summary coalescent methods are computationally fast and are applicable to genomic datasets with thousands of loci, but do not make an efficient use of information in the multilocus data. Most of them take the two-step approach of reconstructing the gene trees for multiple loci by phylogenetic methods and then treating the estimated gene trees as observed data, without accounting for their uncertainties appropriately. In this article we review the statistical nature of the species tree estimation problem under the MSC, and explore the conceptual issues and challenges of species tree estimation by focusing mainly on simple cases of three or four closely related species. We use mathematical analysis and computer simulation to demonstrate that large differences in statistical performance may exist between the two classes of methods. We illustrate that several counterintuitive behaviors may occur with the summary methods but they are due to inefficient use of information in the data by summary methods and vanish when the data are analyzed using full-likelihood methods. These include (i) unidentifiability of parameters in the model, (ii) inconsistency in the so-called anomaly zone, (iii) singularity on the likelihood surface, and (iv) deterioration of performance upon addition of more data. We discuss the challenges and strategies of species tree inference for distantly related species when the molecular clock is violated, and highlight the need for improving the
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.
Ardö, Jonas
2015-12-01
Africa is an important part of the global carbon cycle. It is also a continent facing potential problems due to increasing resource demand in combination with climate change-induced changes in resource supply. Quantifying the pools and fluxes constituting the terrestrial African carbon cycle is a challenge, because of uncertainties in meteorological driver data, lack of validation data, and potentially uncertain representation of important processes in major ecosystems. In this paper, terrestrial primary production estimates derived from remote sensing and a dynamic vegetation model are compared and quantified for major African land cover types. Continental gross primary production estimates derived from remote sensing were higher than corresponding estimates derived from a dynamic vegetation model. However, estimates of continental net primary production from remote sensing were lower than corresponding estimates from the dynamic vegetation model. Variation was found among land cover classes, and the largest differences in gross primary production were found in the evergreen broadleaf forest. Average carbon use efficiency (NPP/GPP) was 0.58 for the vegetation model and 0.46 for the remote sensing method. Validation versus in situ data of aboveground net primary production revealed significant positive relationships for both methods. A combination of the remote sensing method with the dynamic vegetation model did not strongly affect this relationship. Observed significant differences in estimated vegetation productivity may have several causes, including model design and temperature sensitivity. Differences in carbon use efficiency reflect underlying model assumptions. Integrating the realistic process representation of dynamic vegetation models with the high resolution observational strength of remote sensing may support realistic estimation of components of the carbon cycle and enhance resource monitoring, providing suitable validation data is available.
Stochastic linear hybrid systems: Modeling, estimation, and application
Seah, Chze Eng
Hybrid systems are dynamical systems which have interacting continuous state and discrete state (or mode). Accurate modeling and state estimation of hybrid systems are important in many applications. We propose a hybrid system model, known as the Stochastic Linear Hybrid System (SLHS), to describe hybrid systems with stochastic linear system dynamics in each mode and stochastic continuous-state-dependent mode transitions. We then develop a hybrid estimation algorithm, called the State-Dependent-Transition Hybrid Estimation (SDTHE) algorithm, to estimate the continuous state and discrete state of the SLHS from noisy measurements. It is shown that the SDTHE algorithm is more accurate or more computationally efficient than existing hybrid estimation algorithms. Next, we develop a performance analysis algorithm to evaluate the performance of the SDTHE algorithm in a given operating scenario. We also investigate sufficient conditions for the stability of the SDTHE algorithm. The proposed SLHS model and SDTHE algorithm are illustrated to be useful in several applications. In Air Traffic Control (ATC), to facilitate implementations of new efficient operational concepts, accurate modeling and estimation of aircraft trajectories are needed. In ATC, an aircraft's trajectory can be divided into a number of flight modes. Furthermore, as the aircraft is required to follow a given flight plan or clearance, its flight mode transitions are dependent of its continuous state. However, the flight mode transitions are also stochastic due to navigation uncertainties or unknown pilot intents. Thus, we develop an aircraft dynamics model in ATC based on the SLHS. The SDTHE algorithm is then used in aircraft tracking applications to estimate the positions/velocities of aircraft and their flight modes accurately. Next, we develop an aircraft conformance monitoring algorithm to detect any deviations of aircraft trajectories in ATC that might compromise safety. In this application, the SLHS
Lagrangian speckle model and tissue-motion estimation--theory.
Maurice, R L; Bertrand, M
1999-07-01
It is known that when a tissue is subjected to movements such as rotation, shearing, scaling, etc., changes in speckle patterns that result act as a noise source, often responsible for most of the displacement-estimate variance. From a modeling point of view, these changes can be thought of as resulting from two mechanisms: one is the motion of the speckles and the other, the alterations of their morphology. In this paper, we propose a new tissue-motion estimator to counteract these speckle decorrelation effects. The estimator is based on a Lagrangian description of the speckle motion. This description allows us to follow local characteristics of the speckle field as if they were a material property. This method leads to an analytical description of the decorrelation in a way which enables the derivation of an appropriate inverse filter for speckle restoration. The filter is appropriate for linear geometrical transformation of the scattering function (LT), i.e., a constant-strain region of interest (ROI). As the LT itself is a parameter of the filter, a tissue-motion estimator can be formulated as a nonlinear minimization problem, seeking the best match between the pre-tissue-motion image and a restored-speckle post-motion image. The method is tested, using simulated radio-frequency (RF) images of tissue undergoing axial shear.
Directory of Open Access Journals (Sweden)
2009-01-01
Full Text Available In this paper, a method for model structure refinement is proposed and applied in estimation of cumulative number of vulnerabilities according to time. Security as a quality characteristic is presented and defined. Vulnerabilities are defined and their importance is assessed. Existing models used for number of vulnerabilities estimation are enumerated, inspecting their structure. The principles of genetic model generators are inspected. Model structure refinement is defined in comparison with model refinement and a method for model structure refinement is proposed. A case study shows how the method is applied and the obtained results.
Aralis, Hilary; Brookmeyer, Ron
2017-01-01
Multistate models provide an important method for analyzing a wide range of life history processes including disease progression and patient recovery following medical intervention. Panel data consisting of the states occupied by an individual at a series of discrete time points are often used to estimate transition intensities of the underlying continuous-time process. When transition intensities depend on the time elapsed in the current state and back transitions between states are possible, this intermittent observation process presents difficulties in estimation due to intractability of the likelihood function. In this manuscript, we present an iterative stochastic expectation-maximization algorithm that relies on a simulation-based approximation to the likelihood function and implement this algorithm using rejection sampling. In a simulation study, we demonstrate the feasibility and performance of the proposed procedure. We then demonstrate application of the algorithm to a study of dementia, the Nun Study, consisting of intermittently-observed elderly subjects in one of four possible states corresponding to intact cognition, impaired cognition, dementia, and death. We show that the proposed stochastic expectation-maximization algorithm substantially reduces bias in model parameter estimates compared to an alternative approach used in the literature, minimal path estimation. We conclude that in estimating intermittently observed semi-Markov models, the proposed approach is a computationally feasible and accurate estimation procedure that leads to substantial improvements in back transition estimates.
Directory of Open Access Journals (Sweden)
Chow Clara
2011-08-01
a cause of death did not substantively influence the pattern of mortality estimated. Substantially abbreviated and simplified verbal autopsy questionnaires might provide robust information about high-level mortality patterns.
Evapotranspiration Estimates for a Stochastic Soil-Moisture Model
Chaleeraktrakoon, Chavalit; Somsakun, Somrit
2009-03-01
Potential evapotranspiration is information that is necessary for applying a widely used stochastic model of soil moisture (I. Rodriguez Iturbe, A. Porporato, L. Ridolfi, V. Isham and D. R. Cox, Probabilistic modelling of water balance at a point: The role of climate, soil and vegetation, Proc. Roy. Soc. London A455 (1999) 3789-3805). An objective of the present paper is thus to find a proper estimate of the evapotranspiration for the stochastic model. This estimate is obtained by comparing the calculated soil-moisture distribution resulting from various techniques, such as Thornthwaite, Makkink, Jensen-Haise, FAO Modified Penman, and Blaney-Criddle, with an observed one. The comparison results using five sequences of daily soil-moisture for a dry season from November 2003 to April 2004 (Udornthani Province, Thailand) have indicated that all methods can be used if the weather information required is available. This is because their soil-moisture distributions are alike. In addition, the model is shown to have its ability in approximately describing the phenomenon at a weekly or biweekly time scale which is desirable for agricultural engineering applications.
Rainfall estimation with TFR model using Ensemble Kalman filter
Asyiqotur Rohmah, Nabila; Apriliani, Erna
2018-03-01
Rainfall fluctuation can affect condition of other environment, correlated with economic activity and public health. The increasing of global average temperature is influenced by the increasing of CO2 in the atmosphere, which caused climate change. Meanwhile, the forests as carbon sinks that help keep the carbon cycle and climate change mitigation. Climate change caused by rainfall intensity deviations can affect the economy of a region, and even countries. It encourages research on rainfall associated with an area of forest. In this study, the mathematics model that used is a model which describes the global temperatures, forest cover, and seasonal rainfall called the TFR (temperature, forest cover, and rainfall) model. The model will be discretized first, and then it will be estimated by the method of Ensemble Kalman Filter (EnKF). The result shows that the more ensembles used in estimation, the better the result is. Also, the accurateness of simulation result is influenced by measurement variable. If a variable is measurement data, the result of simulation is better.
The complex model of risk and progression of AMD estimation
Directory of Open Access Journals (Sweden)
V. S. Akopyan
2012-01-01
Full Text Available Purpose: to develop a method and a statistical model to estimate individual risk of AMD and the risk for progression to advanced AMD using clinical and genetic risk factors.Methods: A statistical risk assessment model was developed using stepwise binary logistic regression analysis. to estimate the population differences in the prevalence of allelic variants of genes and for the development of models adapted to the population of Moscow region genotyping and assessment of the influence of other risk factors was performed in two groups: patients with differ- ent stages of AMD (n = 74, and control group (n = 116. Genetic risk factors included in the study: polymorphisms in the complement system genes (C3 and CFH, genes at 10q26 locus (ARMS2 and HtRA1, polymorphism in the mitochondrial gene Mt-ND2. Clinical risk factors included in the study: age, gender, high body mass index, smoking history.Results: A comprehensive analysis of genetic and clinical risk factors for AMD in the study group was performed. Compiled statis- tical model assessment of individual risk of AMD, the sensitivity of the model — 66.7%, specificity — 78.5%, AUC = 0.76. Risk factors of late AMD, compiled a statistical model describing the probability of late AMD, the sensitivity of the model — 66.7%, specificity — 78.3%, AUC = 0.73. the developed system allows determining the most likely version of the current late AMD: dry or wet.Conclusion: the developed test system and the mathematical algorhythm for determining the risk of AMD, risk of progression to advanced AMD have fair diagnostic informative and promising for use in clinical practice.
Ecological niche modeling to estimate the distribution of Japanese encephalitis virus in Asia.
Miller, Robin H; Masuoka, Penny; Klein, Terry A; Kim, Heung-Chul; Somer, Todd; Grieco, John
2012-01-01
Culex tritaeniorhynchus is the primary vector of Japanese encephalitis virus (JEV), a leading cause of encephalitis in Asia. JEV is transmitted in an enzootic cycle involving large wading birds as the reservoirs and swine as amplifying hosts. The development of a JEV vaccine reduced the number of JE cases in regions with comprehensive childhood vaccination programs, such as in Japan and the Republic of Korea. However, the lack of vaccine programs or insufficient coverage of populations in other endemic countries leaves many people susceptible to JEV. The aim of this study was to predict the distribution of Culex tritaeniorhynchus using ecological niche modeling. An ecological niche model was constructed using the Maxent program to map the areas with suitable environmental conditions for the Cx. tritaeniorhynchus vector. Program input consisted of environmental data (temperature, elevation, rainfall) and known locations of vector presence resulting from an extensive literature search and records from MosquitoMap. The statistically significant Maxent model of the estimated probability of Cx. tritaeniorhynchus presence showed that the mean temperatures of the wettest quarter had the greatest impact on the model. Further, the majority of human Japanese encephalitis (JE) cases were located in regions with higher estimated probability of Cx. tritaeniorhynchus presence. Our ecological niche model of the estimated probability of Cx. tritaeniorhynchus presence provides a framework for better allocation of vector control resources, particularly in locations where JEV vaccinations are unavailable. Furthermore, this model provides estimates of vector probability that could improve vector surveillance programs and JE control efforts.
Two-stage estimation in copula models used in family studies
DEFF Research Database (Denmark)
Andersen, Elisabeth Anne Wreford
2005-01-01
In this paper register based family studies provide the motivation for studying a two-stage estimation procedure in copula models for multivariate failure time data. The asymptotic properties of the estimators in both parametric and semi-parametric models are derived, generalising the approach...... by Shih and Louis (Biometrics vol. 51, pp. 1384-1399, 1995b) and Glidden (Lifetime Data Analysis vol. 6, pp. 141-156, 2000). Because register based family studies often involve very large cohorts a method for analysing a sampled cohort is also derived together with the asymptotic properties...
Providing or designing? Constructing models in primary maths education (IF. 0.756)
van Dijk, I.M.A.W.; van Oers, H.J.M.; Terwel, J.
2003-01-01
The goal of this exploratory study was to uncover the construction processes which occur when pupils are taught to work with models in primary maths education. Two approaches were studied: 'providing models' versus 'designing models in co-construction'. A qualitative observational study involved two
Sun, Yanqing; Sun, Liuquan; Zhou, Jie
2013-07-01
This paper studies the generalized semiparametric regression model for longitudinal data where the covariate effects are constant for some and time-varying for others. Different link functions can be used to allow more flexible modelling of longitudinal data. The nonparametric components of the model are estimated using a local linear estimating equation and the parametric components are estimated through a profile estimating function. The method automatically adjusts for heterogeneity of sampling times, allowing the sampling strategy to depend on the past sampling history as well as possibly time-dependent covariates without specifically model such dependence. A [Formula: see text]-fold cross-validation bandwidth selection is proposed as a working tool for locating an appropriate bandwidth. A criteria for selecting the link function is proposed to provide better fit of the data. Large sample properties of the proposed estimators are investigated. Large sample pointwise and simultaneous confidence intervals for the regression coefficients are constructed. Formal hypothesis testing procedures are proposed to check for the covariate effects and whether the effects are time-varying. A simulation study is conducted to examine the finite sample performances of the proposed estimation and hypothesis testing procedures. The methods are illustrated with a data example.
ACEt: An R Package for Estimating Dynamic Heritability and Comparing Twin Models.
He, Liang; Pitkäniemi, Janne; Silventoinen, Karri; Sillanpää, Mikko J
2017-11-01
Estimating dynamic effects of age on the genetic and environmental variance components in twin studies may contribute to the investigation of gene-environment interactions, and may provide more insights into more accurate and powerful estimation of heritability. Existing parametric models for estimating dynamic variance components suffer from various drawbacks such as limitation of predefined functions. We present ACEt, an R package for fast estimating dynamic variance components and heritability that may change with respect to age or other moderators. Building on the twin models using penalized splines, ACEt provides a unified framework to incorporate a class of ACE models, in which each component can be modeled independently and is not limited by a linear or quadratic function. We demonstrate that ACEt is robust against misspecification of the number of spline knots, and offers a refined resolution of dynamic behavior of the genetic and environmental components and thus a detailed estimation of age-specific heritability. Moreover, we develop resampling methods for testing twin models with different variance functions including splines, log-linearity and constancy, which can be easily employed to verify various model assumptions. We evaluated the type I error rate and statistical power of the proposed hypothesis testing procedures under various scenarios using simulated datasets. Potential numerical issues and computational cost were also assessed through simulations. We applied the ACEt package to a Finnish twin cohort to investigate age-specific heritability of body mass index and height. Our results show that the age-specific variance components of these two traits exhibited substantially different patterns despite of comparable estimates of heritability. In summary, the ACEt R package offers a useful tool for the exploration of age-dependent heritability and model comparison in twin studies.
Computer model for estimating electric utility environmental noise
International Nuclear Information System (INIS)
Teplitzky, A.M.; Hahn, K.J.
1991-01-01
This paper reports on a computer code for estimating environmental noise emissions from the operation and the construction of electric power plants that was developed based on algorithms. The computer code (Model) is used to predict octave band sound power levels for power plant operation and construction activities on the basis of the equipment operating characteristics and calculates off-site sound levels for each noise source and for an entire plant. Estimated noise levels are presented either as A-weighted sound level contours around the power plant or as octave band levels at user defined receptor locations. Calculated sound levels can be compared with user designated noise criteria, and the program can assist the user in analyzing alternative noise control strategies
Dynamic plant uptake modelling and mass flux estimation
DEFF Research Database (Denmark)
Rein, Arno; Bauer-Gottwein, Peter; Trapp, Stefan
2011-01-01
Plants significantly influence contaminant transport and fate. Important processes are uptake of soil and groundwater contaminants, as well as biodegradation in plants and their root zones. Models for the prediction of chemical uptake into plants are required for the set-up of mass balances...... in environmental systems at different scales. Feedback mechanisms between plants and hydrological systems can play an important role. However, they have received little attention to date. Here, a new model concept for dynamic plant uptake models applying analytical matrix solutions is presented, which can...... be coupled to groundwater transport simulation tools. Exemplary simulations of plant uptake were carried out in order to estimate chemical concentrations in the soil–plant–air system and the influence of plants on contaminant mass fluxes from soil to groundwater....
Irrigation Requirement Estimation Using Vegetation Indices and Inverse Biophysical Modeling
Bounoua, Lahouari; Imhoff, Marc L.; Franks, Shannon
2010-01-01
We explore an inverse biophysical modeling process forced by satellite and climatological data to quantify irrigation requirements in semi-arid agricultural areas. We constrain the carbon and water cycles modeled under both equilibrium, balance between vegetation and climate, and non-equilibrium, water added through irrigation. We postulate that the degree to which irrigated dry lands vary from equilibrium climate conditions is related to the amount of irrigation. The amount of water required over and above precipitation is considered as an irrigation requirement. For July, results show that spray irrigation resulted in an additional amount of water of 1.3 mm per occurrence with a frequency of 24.6 hours. In contrast, the drip irrigation required only 0.6 mm every 45.6 hours or 46% of that simulated by the spray irrigation. The modeled estimates account for 87% of the total reported irrigation water use, when soil salinity is not important and 66% in saline lands.
Estimating organ doses from tube current modulated CT examinations using a generalized linear model.
Bostani, Maryam; McMillan, Kyle; Lu, Peiyun; Kim, Grace Hyun J; Cody, Dianna; Arbique, Gary; Greenberg, S Bruce; DeMarco, John J; Cagnon, Chris H; McNitt-Gray, Michael F
2017-04-01
(WED) and regional CTDI vol as variables and (b) using the same exponential relationship with the addition of categorical variables such as scanner model and organ to provide a more complete estimate of factors that may affect organ dose. Finally, estimates from generated models were compared to those obtained from SSDE and ImPACT. The Generalized Linear Model yielded organ dose estimates that were significantly closer to the MC reference organ dose values than were organ doses estimated via SSDE or ImPACT. Moreover, the GLM estimates were better than those of SSDE or ImPACT irrespective of whether or not categorical variables were used in the model. While the improvement associated with a categorical variable was substantial in estimating breast dose, the improvement was minor for other organs. The GLM approach extends the current CT dose estimation methods by allowing the use of additional variables to more accurately estimate organ dose from TCM scans. Thus, this approach may be able to overcome the limitations of current CT dose metrics to provide more accurate estimates of patient dose, in particular, dose to organs with considerable variability across the population. © 2017 American Association of Physicists in Medicine.
The Everglades Depth Estimation Network (EDEN) surface-water model, version 2
Telis, Pamela A.; Xie, Zhixiao; Liu, Zhongwei; Li, Yingru; Conrads, Paul
2015-01-01
The Everglades Depth Estimation Network (EDEN) is an integrated network of water-level gages, interpolation models that generate daily water-level and water-depth data, and applications that compute derived hydrologic data across the freshwater part of the greater Everglades landscape. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for EDEN in order for EDEN to provide quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan.
A diagnostic model to estimate winds and small-scale drag from Mars Observer PMIRR data
Barnes, J. R.
1993-01-01
Theoretical and modeling studies indicate that small-scale drag due to breaking gravity waves is likely to be of considerable importance for the circulation in the middle atmospheric region (approximately 40-100 km altitude) on Mars. Recent earth-based spectroscopic observations have provided evidence for the existence of circulation features, in particular, a warm winter polar region, associated with gravity wave drag. Since the Mars Observer PMIRR experiment will obtain temperature profiles extending from the surface up to about 80 km altitude, it will be extensively sampling middle atmospheric regions in which gravity wave drag may play a dominant role. Estimating the drag then becomes crucial to the estimation of the atmospheric winds from the PMIRR-observed temperatures. An interative diagnostic model based upon one previously developed and tested with earth satellite temperature data will be applied to the PMIRR measurements to produce estimates of the small-scale zonal drag and three-dimensional wind fields in the Mars middle atmosphere. This model is based on the primitive equations, and can allow for time dependence (the time tendencies used may be based upon those computed in a Fast Fourier Mapping procedure). The small-scale zonal drag is estimated as the residual in the zonal momentum equation; the horizontal winds having first been estimated from the meridional momentum equation and the continuity equation. The scheme estimates the vertical motions from the thermodynamic equation, and thus needs estimates of the diabatic heating based upon the observed temperatures. The latter will be generated using a radiative model. It is hoped that the diagnostic scheme will be able to produce good estimates of the zonal gravity wave drag in the Mars middle atmosphere, estimates that can then be used in other diagnostic or assimilation efforts, as well as more theoretical studies.
Comparison of data mining and allometric model in estimation of tree biomass.
Sanquetta, Carlos R; Wojciechowski, Jaime; Dalla Corte, Ana P; Behling, Alexandre; Péllico Netto, Sylvio; Rodrigues, Aurélio L; Sanquetta, Mateus N I
2015-08-07
The traditional method used to estimate tree biomass is allometry. In this method, models are tested and equations fitted by regression usually applying ordinary least squares, though other analogous methods are also used for this purpose. Due to the nature of tree biomass data, the assumptions of regression are not always accomplished, bringing uncertainties to the inferences. This article demonstrates that the Data Mining (DM) technique can be used as an alternative to traditional regression approach to estimate tree biomass in the Atlantic Forest, providing better results than allometry, and demonstrating simplicity, versatility and flexibility to apply to a wide range of conditions. Various DM approaches were examined regarding distance, number of neighbors and weighting, by using 180 trees coming from environmental restoration plantations in the Atlantic Forest biome. The best results were attained using the Chebishev distance, 1/d weighting and 5 neighbors. Increasing number of neighbors did not improve estimates. We also analyze the effect of the size of data set and number of variables in the results. The complete data set and the maximum number of predicting variables provided the best fitting. We compare DM to Schumacher-Hall model and the results showed a gain of up to 16.5% in reduction of the standard error of estimate. It was concluded that Data Mining can provide accurate estimates of tree biomass and can be successfully used for this purpose in environmental restoration plantations in the Atlantic Forest. This technique provides lower standard error of estimate than the Schumacher-Hall model and has the advantage of not requiring some statistical assumptions as do the regression models. Flexibility, versatility and simplicity are attributes of DM that corroborates its great potential for similar applications.
Modelling bulk canopy resistance from climatic variables for evapotranspiration estimation
Perez, P. J.; Martinez-Cob, A.; Lecina, S.; Castellvi, F.; Villalobos, F. J.
2003-04-01
Evapotranspiration is a component of the hydrological cycle whose accurate computation is needed for an adequate management of water resources. In particular, a high level of accuracy in crop evapotranspiration estimation can represent an important saving of economical and water resources at planning and management of irrigated areas. In the evapotranspiration process, bulk canopy resistance (r_c) is a primary factor and its correct modelling remains an important problem in the Penman-Monteith (PM) method, not only for tall crops but also for medium height and short crops under water stress. In this work, an alternative approach for modelling canopy resistance is presented against th PM method with constant canopy resistance. Variable r_c values are computed as function of a climatic resistance and compared with other two models, Katerji and Perrier and Todorovic. Hourly evapotranspiration values (ET_o) over grass were obtained with a weighing lysimeter and an eddy covariance system at the Ebro and Guadalquivir valleys (Spain) respectively. The main objective is to evaluate whether the use of variable rather than fixed r_c values, would improve the ET_o estimates obtained by applying the PM equation under the semiarid conditions of the two sites, where evaporative demand is high particularly during summer.
Estimating a marriage matching model with spillover effects.
Choo, Eugene; Siow, Aloysius
2006-08-01
We use marriage matching functions to study how marital patterns change when population supplies change. Specifically, we use a behavioral marriage matching function with spillover effects to rationalize marriage and cohabitation behavior in contemporary Canada. The model can estimate a couple's systematic gains to marriage and cohabitation relative to remaining single. These gains are invariant to changes in population supplies. Instead, changes in population supplies redistribute these gains between a couple. Although the model is behavioral, it is nonparametric. It can fit any observed cross-sectional marriage matching distribution. We use the estimated model to quantify the impacts of gender differences in mortality rates and the baby boom on observed marital behavior in Canada. The higher mortality rate of men makes men scarcer than women. We show that the scarceness of men modestly reduced the welfare of women and increased the welfare of men in the marriage market. On the other hand, the baby boom increased older men's net gains to entering the marriage market and lowered middle-aged women's net gains.
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.
DEFF Research Database (Denmark)
Tagesson, Håkan Torbern; Smith, Benjamin; Løfgren, Anders
2009-01-01
and the Beer-Lambert law. LAI estimates were compared with satellite-extrapolated field estimates of LAI, and the results were generally acceptable. NPP estimates directly from the dynamic vegetation model and estimates obtained by combining the model estimates with remote sensing information were, on average...
Luo, Rutao; Piovoso, Michael J.; Martinez-Picado, Javier; Zurakowski, Ryan
2012-01-01
Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3–5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients. PMID:22815727
Density estimation in a wolverine population using spatial capture-recapture models
Royle, J. Andrew; Magoun, Audrey J.; Gardner, Beth; Valkenbury, Patrick; Lowell, Richard E.; McKelvey, Kevin
2011-01-01
Classical closed-population capture-recapture models do not accommodate the spatial information inherent in encounter history data obtained from camera-trapping studies. As a result, individual heterogeneity in encounter probability is induced, and it is not possible to estimate density objectively because trap arrays do not have a well-defined sample area. We applied newly-developed, capture-recapture models that accommodate the spatial attribute inherent in capture-recapture data to a population of wolverines (Gulo gulo) in Southeast Alaska in 2008. We used camera-trapping data collected from 37 cameras in a 2,140-km2 area of forested and open habitats largely enclosed by ocean and glacial icefields. We detected 21 unique individuals 115 times. Wolverines exhibited a strong positive trap response, with an increased tendency to revisit previously visited traps. Under the trap-response model, we estimated wolverine density at 9.7 individuals/1,000-km2(95% Bayesian CI: 5.9-15.0). Our model provides a formal statistical framework for estimating density from wolverine camera-trapping studies that accounts for a behavioral response due to baited traps. Further, our model-based estimator does not have strict requirements about the spatial configuration of traps or length of trapping sessions, providing considerable operational flexibility in the development of field studies.
Micro, nanosystems and systems on chips modeling, control, and estimation
Voda, Alina
2013-01-01
Micro and nanosystems represent a major scientific and technological challenge, with actual and potential applications in almost all fields of the human activity. The aim of the present book is to present how concepts from dynamical control systems (modeling, estimation, observation, identification, feedback control) can be adapted and applied to the development of original very small-scale systems and of their human interfaces. The application fields presented here come from micro and nanorobotics, biochips, near-field microscopy (AFM and STM) and nanosystems networks. Alina Voda has drawn co
Directory of Open Access Journals (Sweden)
Hou Chengyu
2014-01-01
Full Text Available Skywave over-the-horizon (OTH radar systems have important long-range strategic warning values. They exploit skywave propagation reflection of high frequency signals from the ionosphere, which provides the ultra-long-range surveillance capabilities to detect and track maneuvering targets. Current OTH radar systems are capable of localizing targets in range and azimuth but are unable to achieve reliable instantaneous altitude estimation. Most existing height measurement methods of skywave OTH radar systems have taken advantage of the micromultipath effect and been considered in the flat earth model. However, the flat earth model is not proper since large error is inevitable, when the detection range is over one thousand kilometers. In order to avoid the error caused by the flat earth model, in this paper, an earth curvature model is introduced into OTH radar altimetry methods. The simulation results show that application of the earth curvature model can effectively reduce the estimation error.
Magis, David; Raiche, Gilles
2012-01-01
This paper focuses on two estimators of ability with logistic item response theory models: the Bayesian modal (BM) estimator and the weighted likelihood (WL) estimator. For the BM estimator, Jeffreys' prior distribution is considered, and the corresponding estimator is referred to as the Jeffreys modal (JM) estimator. It is established that under…
Estimation and Model Selection for Finite Mixtures of Latent Interaction Models
Hsu, Jui-Chen
2011-01-01
Latent interaction models and mixture models have received considerable attention in social science research recently, but little is known about how to handle if unobserved population heterogeneity exists in the endogenous latent variables of the nonlinear structural equation models. The current study estimates a mixture of latent interaction…
International Nuclear Information System (INIS)
Wei, Zhongbao; Meng, Shujuan; Xiong, Binyu; Ji, Dongxu; Tseng, King Jet
2016-01-01
Highlights: • Integrated online model identification and SOC estimate is explored. • Noise variances are online estimated in a data-driven way. • Identification bias caused by noise corruption is attenuated. • SOC is online estimated with high accuracy and fast convergence. • Algorithm comparison shows the superiority of proposed method. - Abstract: State of charge (SOC) estimators with online identified battery model have proven to have high accuracy and better robustness due to the timely adaption of time varying model parameters. In this paper, we show that the common methods for model identification are intrinsically biased if both the current and voltage sensors are corrupted with noises. The uncertainties in battery model further degrade the accuracy and robustness of SOC estimate. To address this problem, this paper proposes a novel technique which integrates the Frisch scheme based bias compensating recursive least squares (FBCRLS) with a SOC observer for enhanced model identification and SOC estimate. The proposed method online estimates the noise statistics and compensates the noise effect so that the model parameters can be extracted without bias. The SOC is further estimated in real time with the online updated and unbiased battery model. Simulation and experimental studies show that the proposed FBCRLS based observer effectively attenuates the bias on model identification caused by noise contamination and as a consequence provides more reliable estimate on SOC. The proposed method is also compared with other existing methods to highlight its superiority in terms of accuracy and convergence speed.
A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system
Metcalf, Jessica L; Wegener Parfrey, Laura; Gonzalez, Antonio; Lauber, Christian L; Knights, Dan; Ackermann, Gail; Humphrey, Gregory C; Gebert, Matthew J; Van Treuren, Will; Berg-Lyons, Donna; Keepers, Kyle; Guo, Yan; Bullard, James; Fierer, Noah; Carter, David O
2013-01-01
eLife digest Our bodies?especially our skin, our saliva, the lining of our mouth and our gastrointestinal tract?are home to a diverse collection of bacteria and other microorganisms called the microbiome. While the roles played by many of these microorganisms have yet to be identified, it is known that they contribute to the health and wellbeing of their host by metabolizing indigestible compounds, producing essential vitamins, and preventing the growth of harmful bacteria. They are important...
Directory of Open Access Journals (Sweden)
Tweya Hannock
2012-07-01
Full Text Available Abstract Background Routine monitoring of patients on antiretroviral therapy (ART is crucial for measuring program success and accurate drug forecasting. However, compiling data from patient registers to measure retention in ART is labour-intensive. To address this challenge, we conducted a pilot study in Malawi to assess whether patient ART retention could be determined using pharmacy records as compared to estimates of retention based on standardized paper- or electronic based cohort reports. Methods Twelve ART facilities were included in the study: six used paper-based registers and six used electronic data systems. One ART facility implemented an electronic data system in quarter three and was included as a paper-based system facility in quarter two only. Routine patient retention cohort reports, paper or electronic, were collected from facilities for both quarter two [April–June] and quarter three [July–September], 2010. Pharmacy stock data were also collected from the 12 ART facilities over the same period. Numbers of ART continuation bottles recorded on pharmacy stock cards at the beginning and end of each quarter were documented. These pharmacy data were used to calculate the total bottles dispensed to patients in each quarter with intent to estimate the number of patients retained on ART. Information for time required to determine ART retention was gathered through interviews with clinicians tasked with compiling the data. Results Among ART clinics with paper-based systems, three of six facilities in quarter two and four of five facilities in quarter three had similar numbers of patients retained on ART comparing cohort reports to pharmacy stock records. In ART clinics with electronic systems, five of six facilities in quarter two and five of seven facilities in quarter three had similar numbers of patients retained on ART when comparing retention numbers from electronically generated cohort reports to pharmacy stock records. Among
Jeurissen, Patrick P T; Maarse, Hans
2016-06-29
Sweden and Spain experiment with different provider models to reform healthcare provision. Both models have in common that they extend the role of the for-profit sector in healthcare. As the analysis of Saltman and Duran demonstrates, privatisation is an ambiguous and contested strategy that is used for quite different purposes. In our comment, we emphasize that their analysis leaves questions open on the consequences of privatisation for the performance of healthcare and the role of the public sector in healthcare provision. Furthermore, we briefly address the absence of the option of healthcare provision by not-for-profit providers in the privatisation strategy of Sweden and Spain. © 2016 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Diffuse solar radiation estimation models for Turkey's big cities
International Nuclear Information System (INIS)
Ulgen, Koray; Hepbasli, Arif
2009-01-01
A reasonably accurate knowledge of the availability of the solar resource at any place is required by solar engineers, architects, agriculturists, and hydrologists in many applications of solar energy such as solar furnaces, concentrating collectors, and interior illumination of buildings. For this purpose, in the past, various empirical models (or correlations) have been developed in order to estimate the solar radiation around the world. This study deals with diffuse solar radiation estimation models along with statistical test methods used to statistically evaluate their performance. Models used to predict monthly average daily values of diffuse solar radiation are classified in four groups as follows: (i) From the diffuse fraction or cloudness index, function of the clearness index, (ii) From the diffuse fraction or cloudness index, function of the relative sunshine duration or sunshine fraction, (iii) From the diffuse coefficient, function of the clearness index, and (iv) From the diffuse coefficient, function of the relative sunshine duration or sunshine fraction. Empirical correlations are also developed to establish a relationship between the monthly average daily diffuse fraction or cloudness index (K d ) and monthly average daily diffuse coefficient (K dd ) with the monthly average daily clearness index (K T ) and monthly average daily sunshine fraction (S/S o ) for the three big cities by population in Turkey (Istanbul, Ankara and Izmir). Although the global solar radiation on a horizontal surface and sunshine duration has been measured by the Turkish State Meteorological Service (STMS) over all country since 1964, the diffuse solar radiation has not been measured. The eight new models for estimating the monthly average daily diffuse solar radiation on a horizontal surface in three big cites are validated, and thus, the most accurate model is selected for guiding future projects. The new models are then compared with the 32 models available in the
Robust-BD Estimation and Inference for General Partially Linear Models
Directory of Open Access Journals (Sweden)
Chunming Zhang
2017-11-01
Full Text Available The classical quadratic loss for the partially linear model (PLM and the likelihood function for the generalized PLM are not resistant to outliers. This inspires us to propose a class of “robust-Bregman divergence (BD” estimators of both the parametric and nonparametric components in the general partially linear model (GPLM, which allows the distribution of the response variable to be partially specified, without being fully known. Using the local-polynomial function estimation method, we propose a computationally-efficient procedure for obtaining “robust-BD” estimators and establish the consistency and asymptotic normality of the “robust-BD” estimator of the parametric component β o . For inference procedures of β o in the GPLM, we show that the Wald-type test statistic W n constructed from the “robust-BD” estimators is asymptotically distribution free under the null, whereas the likelihood ratio-type test statistic Λ n is not. This provides an insight into the distinction from the asymptotic equivalence (Fan and Huang 2005 between W n and Λ n in the PLM constructed from profile least-squares estimators using the non-robust quadratic loss. Numerical examples illustrate the computational effectiveness of the proposed “robust-BD” estimators and robust Wald-type test in the appearance of outlying observations.
Pollutant Flux Estimation in an Estuary Comparison between Model and Field Measurements
Directory of Open Access Journals (Sweden)
Yen-Chang Chen
2014-08-01
Full Text Available This study proposes a framework for estimating pollutant flux in an estuary. An efficient method is applied to estimate the flux of pollutants in an estuary. A gauging station network in the Danshui River estuary is established to measure the data of water quality and discharge based on the efficient method. A boat mounted with an acoustic Doppler profiler (ADP traverses the river along a preselected path that is normal to the streamflow to measure the velocities, water depths and water quality for calculating pollutant flux. To know the characteristics of the estuary and to provide the basis for the pollutant flux estimation model, data of complete tidal cycles is collected. The discharge estimation model applies the maximum velocity and water level to estimate mean velocity and cross-sectional area, respectively. Thus, the pollutant flux of the estuary can be easily computed as the product of the mean velocity, cross-sectional area and pollutant concentration. The good agreement between the observed and estimated pollutant flux of the Danshui River estuary shows that the pollutant measured by the conventional and the efficient methods are not fundamentally different. The proposed method is cost-effective and reliable. It can be used to estimate pollutant flux in an estuary accurately and efficiently.
Satellite-based ET estimation using Landsat 8 images and SEBAL model
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Bruno Bonemberger da Silva
Full Text Available ABSTRACT Estimation of evapotranspiration is a key factor to achieve sustainable water management in irrigated agriculture because it represents water use of crops. Satellite-based estimations provide advantages compared to direct methods as lysimeters especially when the objective is to calculate evapotranspiration at a regional scale. The present study aimed to estimate the actual evapotranspiration (ET at a regional scale, using Landsat 8 - OLI/TIRS images and complementary data collected from a weather station. SEBAL model was used in South-West Paraná, region composed of irrigated and dry agricultural areas, native vegetation and urban areas. Five Landsat 8 images, row 223 and path 78, DOY 336/2013, 19/2014, 35/2014, 131/2014 and 195/2014 were used, from which ET at daily scale was estimated as a residual of the surface energy balance to produce ET maps. The steps for obtain ET using SEBAL include radiometric calibration, calculation of the reflectance, surface albedo, vegetation indexes (NDVI, SAVI and LAI and emissivity. These parameters were obtained based on the reflective bands of the orbital sensor with temperature surface estimated from thermal band. The estimated ET values in agricultural areas, native vegetation and urban areas using SEBAL algorithm were compatible with those shown in the literature and ET errors between the ET estimates from SEBAL model and Penman Monteith FAO 56 equation were less than or equal to 1.00 mm day-1.
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Gerhard Moser
2015-04-01
Full Text Available Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96% had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.
Locatelli, R.; Bousquet, P.; Chevallier, F.; Fortems-Cheney, A.; Szopa, S.; Saunois, M.; Agusti-Panareda, A.; Bergmann, D.; Bian, H.; Cameron-Smith, P.; Chipperfield, M. P.; Gloor, E.; Houweling, S.; Kawa, S. R.; Krol, M.; Patra, P. K.; Prinn, R. G.; Rigby, M.; Saito, R.; Wilson, C.
2013-10-01
A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System) inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure) is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr-1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr-1 in North America to 7 Tg yr-1 in Boreal Eurasia (from 23 to 48%, respectively). At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly question the consistency of
Directory of Open Access Journals (Sweden)
R. Locatelli
2013-10-01
Full Text Available A modelling experiment has been conceived to assess the impact of transport model errors on methane emissions estimated in an atmospheric inversion system. Synthetic methane observations, obtained from 10 different model outputs from the international TransCom-CH4 model inter-comparison exercise, are combined with a prior scenario of methane emissions and sinks, and integrated into the three-component PYVAR-LMDZ-SACS (PYthon VARiational-Laboratoire de Météorologie Dynamique model with Zooming capability-Simplified Atmospheric Chemistry System inversion system to produce 10 different methane emission estimates at the global scale for the year 2005. The same methane sinks, emissions and initial conditions have been applied to produce the 10 synthetic observation datasets. The same inversion set-up (statistical errors, prior emissions, inverse procedure is then applied to derive flux estimates by inverse modelling. Consequently, only differences in the modelling of atmospheric transport may cause differences in the estimated fluxes. In our framework, we show that transport model errors lead to a discrepancy of 27 Tg yr−1 at the global scale, representing 5% of total methane emissions. At continental and annual scales, transport model errors are proportionally larger than at the global scale, with errors ranging from 36 Tg yr−1 in North America to 7 Tg yr−1 in Boreal Eurasia (from 23 to 48%, respectively. At the model grid-scale, the spread of inverse estimates can reach 150% of the prior flux. Therefore, transport model errors contribute significantly to overall uncertainties in emission estimates by inverse modelling, especially when small spatial scales are examined. Sensitivity tests have been carried out to estimate the impact of the measurement network and the advantage of higher horizontal resolution in transport models. The large differences found between methane flux estimates inferred in these different configurations highly
Williamson, David H.; Ceccarelli, Daniela M.; Evans, Richard D.; Hill, Jos K.; Russ, Garry R.
2014-01-01
No-take marine reserves (NTMRs) are increasingly being established to conserve or restore biodiversity and to enhance the sustainability of fisheries. Although effectively designed and protected NTMR networks can yield conservation and fishery benefits, reserve effects often fail to manifest in systems where there are high levels of non-compliance by fishers (poaching). Obtaining reliable estimates of NTMR non-compliance can be expensive and logistically challenging, particularly in areas wit...
Research on parafoil stability using a rapid estimate model
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Hua YANG
2017-10-01
Full Text Available With the consideration of rotation between canopy and payload of parafoil system, a four-degree-of-freedom (4-DOF longitudinal static model was used to solve parafoil state variables in straight steady flight. The aerodynamic solution of parafoil system was a combination of vortex lattice method (VLM and engineering estimation method. Based on small disturbance assumption, a 6-DOF linear model that considers canopy additional mass was established with benchmark state calculated by 4-DOF static model. Modal analysis of a dynamic model was used to calculate the stability parameters. This method, which is based on a small disturbance linear model and modal analysis, is high-efficiency to the study of parafoil stability. It is well suited for rapid stability analysis in the preliminary stage of parafoil design. Using this method, this paper shows that longitudinal and lateral stability will both decrease when a steady climbing angle increases. This explains the wavy track of the parafoil observed during climbing.
Comparison of different models for non-invasive FFR estimation
Mirramezani, Mehran; Shadden, Shawn
2017-11-01
Coronary artery disease is a leading cause of death worldwide. Fractional flow reserve (FFR), derived from invasively measuring the pressure drop across a stenosis, is considered the gold standard to diagnose disease severity and need for treatment. Non-invasive estimation of FFR has gained recent attention for its potential to reduce patient risk and procedural cost versus invasive FFR measurement. Non-invasive FFR can be obtained by using image-based computational fluid dynamics to simulate blood flow and pressure in a patient-specific coronary model. However, 3D simulations require extensive effort for model construction and numerical computation, which limits their routine use. In this study we compare (ordered by increasing computational cost/complexity): reduced-order algebraic models of pressure drop across a stenosis; 1D, 2D (multiring) and 3D CFD models; as well as 3D FSI for the computation of FFR in idealized and patient-specific stenosis geometries. We demonstrate the ability of an appropriate reduced order algebraic model to closely predict FFR when compared to FFR from a full 3D simulation. This work was supported by the NIH, Grant No. R01-HL103419.
Principles of parametric estimation in modeling language competition.
Zhang, Menghan; Gong, Tao
2013-06-11
It is generally difficult to define reasonable parameters and interpret their values in mathematical models of social phenomena. Rather than directly fitting abstract parameters against empirical data, we should define some concrete parameters to denote the sociocultural factors relevant for particular phenomena, and compute the values of these parameters based upon the corresponding empirical data. Taking the example of modeling studies of language competition, we propose a language diffusion principle and two language inheritance principles to compute two critical parameters, namely the impacts and inheritance rates of competing languages, in our language competition model derived from the Lotka-Volterra competition model in evolutionary biology. These principles assign explicit sociolinguistic meanings to those parameters and calculate their values from the relevant data of population censuses and language surveys. Using four examples of language competition, we illustrate that our language competition model with thus-estimated parameter values can reliably replicate and predict the dynamics of language competition, and it is especially useful in cases lacking direct competition data.
Penalized Estimation in Large-Scale Generalized Linear Array Models
DEFF Research Database (Denmark)
Lund, Adam; Vincent, Martin; Hansen, Niels Richard
2017-01-01
Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension of the para......Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension...... of the parameter vector. A new design matrix free algorithm is proposed for computing the penalized maximum likelihood estimate for GLAMs, which, in particular, handles nondifferentiable penalty functions. The proposed algorithm is implemented and available via the R package glamlasso. It combines several ideas...
Models for estimating the radiation hazards of uranium mines
International Nuclear Information System (INIS)
Wise, K.N.
1990-01-01
Hazards to the health of workers in uranium mines derive from the decay products of radon and from uranium and its descendants. Radon daughters in mine atmospheres are either attached to aerosols or exist as free atoms and their physical state determines in which part of the lung the daughters deposit. The factors which influence the proportions of radon daughters attached to aerosols, their deposition in the lung and the dose received by the cells in lung tissue are discussed. The estimation of dose to tissue from inhalation of ingestion or uranium and daughters is based on a different set of models which have been applied in recent ICRP reports. The models used to describe the deposition of particulates, their movement in the gut and their uptake by organs, which form the basis for future limits on the concentration of uranium and daughters in air or on their intake with food, are outlined. 34 refs., 12 tabs., 9 figs
Fast Estimation of Multinomial Logit Models: R Package mnlogit
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Asad Hasan
2016-11-01
Full Text Available We present the R package mnlogit for estimating multinomial logistic regression models, particularly those involving a large number of categories and variables. Compared to existing software, mnlogit offers speedups of 10 - 50 times for modestly sized problems and more than 100 times for larger problems. Running in parallel mode on a multicore machine gives up to 4 times additional speedup on 8 processor cores. mnlogit achieves its computational efficiency by drastically speeding up computation of the log-likelihood function's Hessian matrix through exploiting structure in matrices that arise in intermediate calculations. This efficient exploitation of intermediate data structures allows mnlogit to utilize system memory much more efficiently, such that for most applications mnlogit requires less memory than comparable software by a factor that is proportional to the number of model categories.
Functional Model to Estimate the Inelastic Displacement Ratio
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Ceangu Vlad
2017-12-01
Full Text Available In this paper a functional model to estimate the inelastic displacement ratio as a function of the ductility factor is presented. The coefficients of the functional model are approximated using nonlinear regression. The used data is in the form of computed displacement for an inelastic single degree of freedom system with a fixed ductility factor. The inelastic seismic response spectra of constant ductility factors are used for generating data. A method for selecting ground-motions that have similar frequency content to that of the ones picked for the comparison is presented. The variability of the seismic response of nonlinear single degree of freedom systems with different hysteretic behavior is presented.
Static models, recursive estimators and the zero-variance approach
Rubino, Gerardo
2016-01-07
When evaluating dependability aspects of complex systems, most models belong to the static world, where time is not an explicit variable. These models suffer from the same problems than dynamic ones (stochastic processes), such as the frequent combinatorial explosion of the state spaces. In the Monte Carlo domain, on of the most significant difficulties is the rare event situation. In this talk, we describe this context and a recent technique that appears to be at the top performance level in the area, where we combined ideas that lead to very fast estimation procedures with another approach called zero-variance approximation. Both ideas produced a very efficient method that has the right theoretical property concerning robustness, the Bounded Relative Error one. Some examples illustrate the results.
Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model
Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long
2001-01-01
This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.
Satellite estimates of urban development for hydrological modelling
DEFF Research Database (Denmark)
Kaspersen, Per Skougaard; Drews, Martin
We investigate the applicability of medium resolution Landsat satellite imagery for mapping temporal changes in urban land cover in European cities for direct use in urban flood models. The overarching aim is to provide accurate and costand resource-efficient quantification of temporal changes...
Model estimation of energy flow in Oregon coastal seabird populations
Wiens, J.A.; Scott, J.M.
1976-01-01
A computer simulation model was used to explore the patterns and magnitudes of population density changes and population energy demands in Oregon populations of Sooty Shear-waters, Leach?s Storm-Petrels, Brandt?s Cormorants, and Common Murres. The species differ in seasonal distribution and abundance, with shearwaters attaining high densities during their migratory movements through Oregon waters, and murres exhibiting the greatest seasonal stability in population numbers. On a unit area basis, annual energy flow is greatest through murre and cormorant populations. However, because shearwaters occupy a larger area during their transit, they dominate the total energy flow through the four-species seabird ?community.?.....Consumption of various prey types is estimated by coupling model output of energy demands with information on dietary habits. This analysis suggests that murres annually consume nearly twice as many herring as any other prey and consume approximately equal quantities of anchovy, smelt, cod, and rockfish. Cormorants consume a relatively small quantity of bottom-dwelling fish, while stormpetrels take roughly equal quantities of euphausiids and hydrozoans. Anchovies account for 43% of the 62,506 metric tons of prey the four species are estimated to consume annually; 86% of this anchovy consumption is by shearwaters. The consumption of pelagic fishes by these four populations within the neritic zone may represent as much as 22% of the annual production of these fish.
KONVERGENSI ESTIMATOR DALAM MODEL MIXTURE BERBASIS MISSING DATA
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N Dwidayati
2014-11-01
Full Text Available Model mixture dapat mengestimasi proporsi pasien yang sembuh (cured dan fungsi survival pasien tak sembuh (uncured. Pada kajian ini, model mixture dikembangkan untuk analisis cure rate berbasis missing data. Ada beberapa metode yang dapat digunakan untuk analisis missing data. Salah satu metode yang dapat digunakan adalah Algoritma EM, Metode ini didasarkan pada dua langkah, yaitu: (1 Expectation Step dan (2 Maximization Step. Algoritma EM merupakan pendekatan iterasi untuk mempelajari model dari data dengan nilai hilang melalui empat langkah, yaitu(1 pilih himpunan inisial dari parameter untuk sebuah model, (2 tentukan nilai ekspektasi untuk data hilang, (3 buat induksi parameter model baru dari gabungan nilai ekspekstasi dan data asli, dan (4 jika parameter tidak converged, ulangi langkah 2 menggunakan model baru. Berdasar kajian yang dilakukan dapat ditunjukkan bahwa pada algoritma EM, log-likelihood untuk missing data mengalami kenaikan setelah dilakukan setiap iterasi dari algoritmanya. Dengan demikian berdasar algoritma EM, barisan likelihood konvergen jika likelihood terbatas ke bawah. Model mixture can estimate the proportion of recovering (cured patients and function of survival but do not recover (uncured patients. In this study, a model mixture has been developed to analyze the curing rate based on missing data. There are some methods applicable to analyze missing data. One of the methods is EM Algorithm, This method is based on two (2 steps, i.e.: ( 1 Expectation Step and ( 2 Maximization Step. EM Algorithm is an iteration approach to study the model from data with missing values in four (4 steps, i.e. (1 to choose initial set from parameters for a model, ( 2 to determine the expectation value for missing data, ( 3 to make induction for the new model parameter from the combined expectation values and the original data, and ( 4 if parameter is not converged, repeat step 2 using new model. The current study indicated that for
Welter, David E.; Doherty, John E.; Hunt, Randall J.; Muffels, Christopher T.; Tonkin, Matthew J.; Schreuder, Willem A.
2012-01-01
An object-oriented parameter estimation code was developed to incorporate benefits of object-oriented programming techniques for solving large parameter estimation modeling problems. The code is written in C++ and is a formulation and expansion of the algorithms included in PEST, a widely used parameter estimation code written in Fortran. The new code is called PEST++ and is designed to lower the barriers of entry for users and developers while providing efficient algorithms that can accommodate large, highly parameterized problems. This effort has focused on (1) implementing the most popular features of PEST in a fashion that is easy for novice or experienced modelers to use and (2) creating a software design that is easy to extend; that is, this effort provides a documented object-oriented framework designed from the ground up to be modular and extensible. In addition, all PEST++ source code and its associated libraries, as well as the general run manager source code, have been integrated in the Microsoft Visual Studio® 2010 integrated development environment. The PEST++ code is designed to provide a foundation for an open-source development environment capable of producing robust and efficient parameter estimation tools for the environmental modeling community into the future.
Parameter estimation of component reliability models in PSA model of Krsko NPP
International Nuclear Information System (INIS)
Jordan Cizelj, R.; Vrbanic, I.
2001-01-01
In the paper, the uncertainty analysis of component reliability models for independent failures is shown. The present approach for parameter estimation of component reliability models in NPP Krsko is presented. Mathematical approaches for different types of uncertainty analyses are introduced and used in accordance with some predisposed requirements. Results of the uncertainty analyses are shown in an example for time-related components. As the most appropriate uncertainty analysis proved the Bayesian estimation with the numerical estimation of a posterior, which can be approximated with some appropriate probability distribution, in this paper with lognormal distribution.(author)
Parameter and state estimation in a Neisseria meningitidis model: A study case of Niger
Bowong, S.; Mountaga, L.; Bah, A.; Tewa, J. J.; Kurths, J.
2016-12-01
Neisseria meningitidis (Nm) is a major cause of bacterial meningitidis outbreaks in Africa and the Middle East. The availability of yearly reported meningitis cases in the African meningitis belt offers the opportunity to analyze the transmission dynamics and the impact of control strategies. In this paper, we propose a method for the estimation of state variables that are not accessible to measurements and an unknown parameter in a Nm model. We suppose that the yearly number of Nm induced mortality and the total population are known inputs, which can be obtained from data, and the yearly number of new Nm cases is the model output. We also suppose that the Nm transmission rate is an unknown parameter. We first show how the recruitment rate into the population can be estimated using real data of the total population and Nm induced mortality. Then, we use an auxiliary system called observer whose solutions converge exponentially to those of the original model. This observer does not use the unknown infection transmission rate but only uses the known inputs and the model output. This allows us to estimate unmeasured state variables such as the number of carriers that play an important role in the transmission of the infection and the total number of infected individuals within a human community. Finally, we also provide a simple method to estimate the unknown Nm transmission rate. In order to validate the estimation results, numerical simulations are conducted using real data of Niger.
The LNT model provides the best approach for practical implementation of radiation protection.
Martin, C J
2005-01-01
This contribution argues the case that, at the present time, the linear-no-threshold (LNT) model provides the only rational framework on which practical radiation protection can be organized. Political, practical and healthcare difficulties with attempting to introduce an alternative approach, e.g. a threshold model, are discussed.
Effectiveness of Video Modeling Provided by Mothers in Teaching Play Skills to Children with Autism
Besler, Fatma; Kurt, Onur
2016-01-01
Video modeling is an evidence-based practice that can be used to provide instruction to individuals with autism. Studies show that this instructional practice is effective in teaching many types of skills such as self-help skills, social skills, and academic skills. However, in previous studies, videos used in the video modeling process were…
Volz, Erik M; Didelot, Xavier
2018-02-07
Non-parametric population genetic modeling provides a simple and flexible approach for studying demographic history and epidemic dynamics using pathogen sequence data. Existing Bayesian approaches are premised on stochastic processes with stationary increments which may provide an unrealistic prior for epidemic histories which feature extended period of exponential growth or decline. We show that non-parametric models defined in terms of the growth rate of the effective population size can provide a more realistic prior for epidemic history. We propose a non-parametric autoregressive model on the growth rate as a prior for effective population size, which corresponds to the dynamics expected under many epidemic situations. We demonstrate the use of this model within a Bayesian phylodynamic inference framework. Our method correctly reconstructs trends of epidemic growth and decline from pathogen genealogies even when genealogical data is sparse and conventional skyline estimators erroneously predict stable population size. We also propose a regression approach for relating growth rates of pathogen effective population size and time-varying variables that may impact the replicative fitness of a pathogen. The model is applied to real data from rabies virus and Staphylococcus aureus epidemics. We find a close correspondence between the estimated growth rates of a lineage of methicillin-resistant S. aureus and population-level prescription rates of β-lactam antibiotics. The new models are implemented in an open source R package called skygrowth which is available at https://github.com/mrc-ide/skygrowth. © The Author(s) 2018. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.
Utilising temperature differences as constraints for estimating parameters in a simple climate model
International Nuclear Information System (INIS)
Bodman, Roger W; Karoly, David J; Enting, Ian G
2010-01-01
Simple climate models can be used to estimate the global temperature response to increasing greenhouse gases. Changes in the energy balance of the global climate system are represented by equations that necessitate the use of uncertain parameters. The values of these parameters can be estimated from historical observations, model testing, and tuning to more complex models. Efforts have been made at estimating the possible ranges for these parameters. This study continues this process, but demonstrates two new constraints. Previous studies have shown that land-ocean temperature differences are only weakly correlated with global mean temperature for natural internal climate variations. Hence, these temperature differences provide additional information that can be used to help constrain model parameters. In addition, an ocean heat content ratio can also provide a further constraint. A pulse response technique was used to identify relative parameter sensitivity which confirmed the importance of climate sensitivity and ocean vertical diffusivity, but the land-ocean warming ratio and the land-ocean heat exchange coefficient were also found to be important. Experiments demonstrate the utility of the land-ocean temperature difference and ocean heat content ratio for setting parameter values. This work is based on investigations with MAGICC (Model for the Assessment of Greenhouse-gas Induced Climate Change) as the simple climate model.
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
Re-evaluating neonatal-age models for ungulates: does model choice affect survival estimates?
Grovenburg, Troy W; Monteith, Kevin L; Jacques, Christopher N; Klaver, Robert W; DePerno, Christopher S; Brinkman, Todd J; Monteith, Kyle B; Gilbert, Sophie L; Smith, Joshua B; Bleich, Vernon C; Swanson, Christopher C; Jenks, Jonathan A
2014-01-01
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.e., weekly
[Application of DNDC model in estimating cropland nitrate leaching].
Li, Hu; Wang, Li-Gang; Qiu, Jian-Jun
2009-07-01
The leaching amount of soil water and nitrate from winter wheat field under typical planting system in Jinan City of Shandong Province was measured with lysimeter during the whole growth season in 2008, and the feasibility of applying DNDC model to estimate this leaching amount was tested by the obtained data. On the whole, DNDC model could better simulate the soil water movement in the crop field, with the accuracy being acceptable. However, there existed definite deviation in the simulation of nitrate leaching. The simulated value (18.35 kg N x hm(-2)) was 3.46 kg N x hm(-2) higher than the observed value (14.89 kg N x hm(-2)), with a relative error of about 20%, which suggested that some related parameters were required to be further modified. The sensitivity test of DNDC model showed that cropland nitrate leaching was easily to be affected by irrigation and fertilization. It was proved that the model had definite applicability in the study area.
Uncertainty Model for Total Solar Irradiance Estimation on Australian Rooftops
Al-Saadi, Hassan; Zivanovic, Rastko; Al-Sarawi, Said
2017-11-01
The installations of solar panels on Australian rooftops have been in rise for the last few years, especially in the urban areas. This motivates academic researchers, distribution network operators and engineers to accurately address the level of uncertainty resulting from grid-connected solar panels. The main source of uncertainty is the intermittent nature of radiation, therefore, this paper presents a new model to estimate the total radiation incident on a tilted solar panel. Where a probability distribution factorizes clearness index, the model is driven upon clearness index with special attention being paid for Australia with the utilization of best-fit-correlation for diffuse fraction. The assessment of the model validity is achieved with the adoption of four goodness-of-fit techniques. In addition, the Quasi Monte Carlo and sparse grid methods are used as sampling and uncertainty computation tools, respectively. High resolution data resolution of solar irradiations for Adelaide city were used for this assessment, with an outcome indicating a satisfactory agreement between actual data variation and model.
Ensemble modeling of the Baltic Sea ecosystem to provide scenarios for management.
Meier, H E Markus; Andersson, Helén C; Arheimer, Berit; Donnelly, Chantal; Eilola, Kari; Gustafsson, Bo G; Kotwicki, Lech; Neset, Tina-Simone; Niiranen, Susa; Piwowarczyk, Joanna; Savchuk, Oleg P; Schenk, Frederik; Węsławski, Jan Marcin; Zorita, Eduardo
2014-02-01
We present a multi-model ensemble study for the Baltic Sea, and investigate the combined impact of changing climate, external nutrient supply, and fisheries on the marine ecosystem. The applied regional climate system model contains state-of-the-art component models for the atmosphere, sea ice, ocean, land surface, terrestrial and marine biogeochemistry, and marine food-web. Time-dependent scenario simulations for the period 1960-2100 are performed and uncertainties of future projections are estimated. In addition, reconstructions since 1850 are carried out to evaluate the models sensitivity to external stressors on long time scales. Information from scenario simulations are used to support decision-makers and stakeholders and to raise awareness of climate change, environmental problems, and possible abatement strategies among the general public using geovisualization. It is concluded that the study results are relevant for the Baltic Sea Action Plan of the Helsinki Commission.
Overgaard, Rune V; Jonsson, Niclas; Tornøe, Christoffer W; Madsen, Henrik
2005-02-01
Pharmacokinetic/pharmacodynamic modelling is most often performed using non-linear mixed-effects models based on ordinary differential equations with uncorrelated intra-individual residuals. More sophisticated residual error models as e.g. stochastic differential equations (SDEs) with measurement noise can in many cases provide a better description of the variations, which could be useful in various aspects of modelling. This general approach enables a decomposition of the intra-individual residual variation epsilon into system noise w and measurement noise e. The present work describes implementation of SDEs in a non-linear mixed-effects model, where parameter estimation was performed by a novel approximation of the likelihood function. This approximation is constructed by combining the First-Order Conditional Estimation (FOCE) method used in non-linear mixed-effects modelling with the Extended Kalman Filter used in models with SDEs. Fundamental issues concerning the proposed model and estimation algorithm are addressed by simulation studies, concluding that system noise can successfully be separated from measurement noise and inter-individual variability.
An Improved Swarm Optimization for Parameter Estimation and Biological Model Selection
Abdullah, Afnizanfaizal; Deris, Safaai; Mohamad, Mohd Saberi; Anwar, Sohail
2013-01-01
One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This
An improved swarm optimization for parameter estimation and biological model selection.
Directory of Open Access Journals (Sweden)
Afnizanfaizal Abdullah
Full Text Available One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete
Projected metastable Markov processes and their estimation with observable operator models
International Nuclear Information System (INIS)
Wu, Hao; Prinz, Jan-Hendrik; Noé, Frank
2015-01-01
The determination of kinetics of high-dimensional dynamical systems, such as macromolecules, polymers, or spin systems, is a difficult and generally unsolved problem — both in simulation, where the optimal reaction coordinate(s) are generally unknown and are difficult to compute, and in experimental measurements, where only specific coordinates are observable. Markov models, or Markov state models, are widely used but suffer from the fact that the dynamics on a coarsely discretized state spaced are no longer Markovian, even if the dynamics in the full phase space are. The recently proposed projected Markov models (PMMs) are a formulation that provides a description of the kinetics on a low-dimensional projection without making the Markovianity assumption. However, as yet no general way of estimating PMMs from data has been available. Here, we show that the observed dynamics of a PMM can be exactly described by an observable operator model (OOM) and derive a PMM estimator based on the OOM learning
KONVERGENSI ESTIMATOR DALAM MODEL MIXTURE BERBASIS MISSING DATA
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N Dwidayati
2014-06-01
Full Text Available Abstrak __________________________________________________________________________________________ Model mixture dapat mengestimasi proporsi pasien yang sembuh (cured dan fungsi survival pasien tak sembuh (uncured. Pada kajian ini, model mixture dikembangkan untuk analisis cure rate berbasis missing data. Ada beberapa metode yang dapat digunakan untuk analisis missing data. Salah satu metode yang dapat digunakan adalah Algoritma EM, Metode ini didasarkan pada 2 (dua langkah, yaitu: (1 Expectation Step dan (2 Maximization Step. Algoritma EM merupakan pendekatan iterasi untuk mempelajari model dari data dengan nilai hilang melalui 4 (empat langkah, yaitu(1 pilih himpunan inisial dari parameter untuk sebuah model, (2 tentukan nilai ekspektasi untuk data hilang, (3 buat induksi parameter model baru dari gabungan nilai ekspekstasi dan data asli, dan (4 jika parameter tidak converged, ulangi langkah 2 menggunakan model baru. Berdasar kajian yang dilakukan dapat ditunjukkan bahwa pada algoritma EM, log-likelihood untuk missing data mengalami kenaikan setelah dilakukan setiap iterasi dari algoritmanya. Dengan demikian berdasar algoritma EM, barisan likelihood konvergen jika likelihood terbatas ke bawah. Abstract __________________________________________________________________________________________ Model mixture can estimate proportion of recovering patient and function of patient survival do not recover. At this study, model mixture developed to analyse cure rate bases on missing data. There are some method which applicable to analyse missing data. One of method which can be applied is Algoritma EM, This method based on 2 ( two step, that is: ( 1 Expectation Step and ( 2 Maximization Step. EM Algorithm is approach of iteration to study model from data with value loses through 4 ( four step, yaitu(1 select;chooses initial gathering from parameter for a model, ( 2 determines expectation value for data to lose, ( 3 induce newfangled parameter
Nonlinear State Estimation and Modeling of a Helicopter UAV
Barczyk, Martin
Experimentally-validated nonlinear flight control of a helicopter UAV has two necessary conditions: an estimate of the vehicle’s states from noisy multirate output measurements, and a nonlinear dynamics model with minimum complexity, physically controllable inputs and experimentally identified parameter values. This thesis addresses both these objectives for the Applied Nonlinear Controls Lab (ANCL)'s helicopter UAV project. A magnetometer-plus-GPS aided Inertial Navigation System (INS) for outdoor flight as well as an Attitude and Heading Reference System (AHRS) for indoor testing are designed, implemented and experimentally validated employing an Extended Kalman Filter (EKF), using a novel calibration technique for the magnetometer aiding sensor added to remove the limitations of an earlier GPS-only aiding design. Next the recently-developed nonlinear observer design methodology of invariant observers is adapted to the aided INS and AHRS examples, employing a rotation matrix representation for the state manifold to obtain designs amenable to global stability analysis, obtaining a direct nonlinear design for gains of the AHRS observer, modifying the previously-proposed Invariant EKF systematic method for computing gains, and culminating in simulation and experimental validation of the observers. Lastly a nonlinear control-oriented model of the helicopter UAV is derived from first principles, using a rigid-body dynamics formulation augmented with models of the on-board subsystems: main rotor forces and blade flapping dynamics, the Bell-Hiller system and flybar flapping dynamics, tail rotor forces, tail gyro unit, engine and rotor speed, servo operation, fuselage drag, and tail stabilizer forces. The parameter values in the resulting models are identified experimentally. Using these the model is further simplified to be tractable for model-based control design.
IMPROVEMENT OF MATHEMATICAL MODELS FOR ESTIMATION OF TRAIN DYNAMICS
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L. V. Ursulyak
2017-12-01
Full Text Available Purpose. Using scientific publications the paper analyzes the mathematical models developed in Ukraine, CIS countries and abroad for theoretical studies of train dynamics and also shows the urgency of their further improvement. Methodology. Information base of the research was official full-text and abstract databases, scientific works of domestic and foreign scientists, professional periodicals, materials of scientific and practical conferences, methodological materials of ministries and departments. Analysis of publications on existing mathematical models used to solve a wide range of problems associated with the train dynamics study shows the expediency of their application. Findings. The results of these studies were used in: 1 design of new types of draft gears and air distributors; 2 development of methods for controlling the movement of conventional and connected trains; 3 creation of appropriate process flow diagrams; 4 development of energy-saving methods of train driving; 5 revision of the Construction Codes and Regulations (SNiP ΙΙ-39.76; 6 when selecting the parameters of the autonomous automatic control system, created in DNURT, for an auxiliary locomotive that is part of a connected train; 7 when creating computer simulators for the training of locomotive drivers; 8 assessment of the vehicle dynamic indices characterizing traffic safety. Scientists around the world conduct numerical experiments related to estimation of train dynamics using mathematical models that need to be constantly improved. Originality. The authors presented the main theoretical postulates that allowed them to develop the existing mathematical models for solving problems related to the train dynamics. The analysis of scientific articles published in Ukraine, CIS countries and abroad allows us to determine the most relevant areas of application of mathematical models. Practicalvalue. The practical value of the results obtained lies in the scientific validity
Wiedinmyer, C.; Akagi, S. K.; Yokelson, R. J.; Emmons, L. K.; Al-Saadi, J. A.; Orlando, J. J.; Soja, A. J.
2010-12-01
The Fire INventory from NCAR version 1.0 (FINNv1) provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include biofuel use and trash burning. Emission factors used in the calculations have been updated with recent data, particularly for the non-methane organic compounds (NMOC). The resulting global annual NMOC emission estimates are as much as a factor of 5 greater than some prior estimates. Chemical speciation profiles, necessary to allocate the total NMOC emission estimates to lumped species for use by chemical transport models, are provided for three widely used chemical mechanisms: SAPRC99, GEOS-CHEM, and MOZART-4. Using these profiles, FINNv1 also provides global estimates of key organic compounds, including formaldehyde and methanol. The uncertainty in the FINNv1 emission estimates are about a factor of two; but, the estimates agree closely with other global inventories of biomass burning emissions for CO, CO2, and other species with less variable emission factors. FINNv1 emission estimates have been developed specifically for modeling atmospheric chemistry and air quality in a consistent framework at scales from local to global. The product is unique because of the high temporal and spatial resolution, global coverage, and the number of species estimated. FINNv1 can be used for both hindcast and forecast or near-real time model applications and the results are being critically evaluated with models and observations whenever possible.
Comparisons of Crosswind Velocity Profile Estimates Used in Fast-Time Wake Vortex Prediction Models
Pruis, Mathew J.; Delisi, Donald P.; Ahmad, Nashat N.
2011-01-01
Five methods for estimating crosswind profiles used in fast-time wake vortex prediction models are compared in this study. Previous investigations have shown that temporal and spatial variations in the crosswind vertical profile have a large impact on the transport and time evolution of the trailing vortex pair. The most important crosswind parameters are the magnitude of the crosswind and the gradient in the crosswind shear. It is known that pulsed and continuous wave lidar measurements can provide good estimates of the wind profile in the vicinity of airports. In this study comparisons are made between estimates of the crosswind profiles from a priori information on the trajectory of the vortex pair as well as crosswind profiles derived from different sensors and a regional numerical weather prediction model.
Assessment of vapor pressure estimation methods for secondary organic aerosol modeling
Camredon, Marie; Aumont, Bernard
Vapor pressure ( Pvap) is a fundamental property controlling the gas-particle partitioning of organic species. Therefore this pure substance property is a critical parameter for modeling the formation of secondary organic aerosols (SOA). Structure-property relationships are needed to estimate Pvap because (i) very few experimental data for Pvap are available for semi-volatile organics and (ii) the number of contributors to SOA is extremely large. The Lee and Kesler method, a modified form of the Mackay equation, the Myrdal and Yalkowsky method and the UNIFAC- pLo method are commonly used to estimate Pvap in gas-particle partitioning models. The objectives of this study are (i) to assess the accuracy of these four methods on a large experimental database selected to be representative of SOA contributors and (ii) to compare the estimates provided by the various methods for compounds detected in the aerosol phase.
Leon-Perez, Jose M; Notelaers, Guy; Arenas, Alicia; Munduate, Lourdes; Medina, Francisco J
2014-05-01
Research findings underline the negative effects of exposure to bullying behaviors and document the detrimental health effects of being a victim of workplace bullying. While no one disputes its negative consequences, debate continues about the magnitude of this phenomenon since very different prevalence rates of workplace bullying have been reported. Methodological aspects may explain these findings. Our contribution to this debate integrates behavioral and self-labeling estimation methods of workplace bullying into a measurement model that constitutes a bullying typology. Results in the present sample (n = 1,619) revealed that six different groups can be distinguished according to the nature and intensity of reported bullying behaviors. These clusters portray different paths for the workplace bullying process, where negative work-related and person-degrading behaviors are strongly intertwined. The analysis of the external validity showed that integrating previous estimation methods into a single measurement latent class model provides a reliable estimation method of workplace bullying, which may overcome previous flaws.
Gu, Yuanyuan; Norman, Richard; Viney, Rosalie
2014-09-01
Using discrete choice experiments (DCEs) to estimate health state utility values has become an important alternative to the conventional methods of Time Trade-Off and Standard Gamble. Studies using DCEs have typically used the conditional logit to estimate the underlying utility function. The conditional logit is known for several limitations. In this paper, we propose two types of models based on the mixed logit: one using preference space and the other using quality-adjusted life year (QALY) space, a concept adapted from the willingness-to-pay literature. These methods are applied to a dataset collected using the EQ-5D. The results showcase the advantages of using QALY space and demonstrate that the preferred QALY space model provides lower estimates of the utility values than the conditional logit, with the divergence increasing with worsening health states. Copyright © 2014 John Wiley & Sons, Ltd.
International Nuclear Information System (INIS)
Whelan, Gene; Pelton, Mitch A.
2007-01-01
One of the most important concerns for regulatory agencies is the concept of reproducibility (i.e., reproducibility means credibility) of an assessment. One aspect of reproducibility deals with tampering of the assessment. In other words, when multiple groups are engaged in an assessment, it is important to lock down the problem that is to be solved and/or to restrict the models that are to be used to solve the problem. The objective of this effort is to provide the U.S. Nuclear Regulatory Commission (NRC) with a means to limit user access to models and to provide a mechanism to constrain the conceptual site models (CSMs) when appropriate. The purpose is to provide the user (i.e., NRC) with the ability to ''lock down'' the CSM (i.e., picture containing linked icons), restrict access to certain models, or both.
Johannes Breidenbach; Clara Antón-Fernández; Hans Petersson; Ronald E. McRoberts; Rasmus Astrup
2014-01-01
National Forest Inventories (NFIs) provide estimates of forest parameters for national and regional scales. Many key variables of interest, such as biomass and timber volume, cannot be measured directly in the field. Instead, models are used to predict those variables from measurements of other field variables. Therefore, the uncertainty or variability of NFI estimates...
International Nuclear Information System (INIS)
Reimus, P.W.; Sevigny, N.L.; Schutz, M.E.; Heller, R.A.
1986-12-01
The MONITOR model is a FORTRAN 77 based computer code that provides parametric life-cycle cost estimates for a monitored retrievable storage (MRS) facility. MONITOR is very flexible in that it can estimate the costs of an MRS facility operating under almost any conceivable nuclear waste logistics scenario. The model can also accommodate input data of varying degrees of complexity and detail (ranging from very simple to more complex) which makes it ideal for use in the MRS program, where new designs and new cost data are frequently offered for consideration. MONITOR can be run as an independent program, or it can be interfaced with the Waste System Transportation and Economic Simulation (WASTES) model, a program that simulates the movement of waste through a complete nuclear waste disposal system. The WASTES model drives the MONITOR model by providing it with the annual quantities of waste that are received, stored, and shipped at the MRS facility. Three runs of MONITOR are documented in this report. Two of the runs are for Version 1 of the MONITOR code. A simulation which uses the costs developed by the Ralph M. Parsons Company in the 2A (backup) version of the MRS cost estimate. In one of these runs MONITOR was run as an independent model, and in the other run MONITOR was run using an input file generated by the WASTES model. The two runs correspond to identical cases, and the fact that they gave identical results verified that the code performed the same calculations in both modes of operation. The third run was made for Version 2 of the MONITOR code. A simulation which uses the costs developed by the Ralph M. Parsons Company in the 2B (integral) version of the MRS cost estimate. This run was made with MONITOR being run as an independent model. The results of several cases have been verified by hand calculations
Retrospective forecast of ETAS model with daily parameters estimate
Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang
2016-04-01
We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.
Rebillat, Marc; Schoukens, Maarten
2018-05-01
Linearity is a common assumption for many real-life systems, but in many cases the nonlinear behavior of systems cannot be ignored and must be modeled and estimated. Among the various existing classes of nonlinear models, Parallel Hammerstein Models (PHM) are interesting as they are at the same time easy to interpret as well as to estimate. One way to estimate PHM relies on the fact that the estimation problem is linear in the parameters and thus that classical least squares (LS) estimation algorithms can be used. In that area, this article introduces a regularized LS estimation algorithm inspired on some of the recently developed regularized impulse response estimation techniques. Another mean to estimate PHM consists in using parametric or non-parametric exponential sine sweeps (ESS) based methods. These methods (LS and ESS) are founded on radically different mathematical backgrounds but are expected to tackle the same issue. A methodology is proposed here to compare them with respect to (i) their accuracy, (ii) their computational cost, and (iii) their robustness to noise. Tests are performed on simulated systems for several values of methods respective parameters and of signal to noise ratio. Results show that, for a given set of data points, the ESS method is less demanding in computational resources than the LS method but that it is also less accurate. Furthermore, the LS method needs parameters to be set in advance whereas the ESS method is not subject to conditioning issues and can be fully non-parametric. In summary, for a given set of data points, ESS method can provide a first, automatic, and quick overview of a nonlinear system than can guide more computationally demanding and precise methods, such as the regularized LS one proposed here.
Directory of Open Access Journals (Sweden)
Shengyu eJiang
2016-02-01
Full Text Available Likert types of rating scales in which a respondent chooses a response from an ordered set of response options are used to measure a wide variety of psychological, educational, and medical outcome variables. The most appropriate item response theory model for analyzing and scoring these instruments when they provide scores on multiple scales is the multidimensional graded response model (MGRM. A simulation study was conducted to investigate the variables that might affect item parameter recovery for the MGRM. Data were generated based on different sample sizes, test lengths, and scale intercorrelations. Parameter estimates were obtained through the flexiMIRT software. The quality of parameter recovery was assessed by the correlation between true and estimated parameters as well as bias and root- mean-square-error. Results indicated that for the vast majority of cases studied a sample size of N = 500 provided accurate parameter estimates, except for tests with 240 items when 1,000 examinees were necessary to obtain accurate parameter estimates. Increasing sample size beyond N = 1,000 did not increase the accuracy of MGRM parameter estimates.
A model for estimating the potency and survival of human gametes in vivo.
Weinberg, C R; Wilcox, A J
1995-06-01
Sperm and ova are sensitive to numerous toxicants in animal studies; however, human vulnerability has been far more difficult to assess, due in part to a lack of methods for measuring the viable survival of human gametes in vivo. We present a parametric model for fertility, which assumes that the viable lifetime of the ovum is fixed while that of sperm is exponentially distributed. By reducing the number of parameters that must be estimated, compared to a previous approach, the model leads to improved tests for differences in sperm and egg survival between exposed and unexposed couples. Since it assumes that batches of sperm introduced on different days present independent competing "risks" (of fertilization) to the ovum, the model also provides for estimation of the age distribution, in days, of the sperm which actually fertilized the ova. This allows us to consider whether older sperm are more likely to produce defective embryos. We apply this model to data from a group of women who were intensively studied, beginning when they discontinued contraception in order to start a pregnancy. Participants kept daily records of intercourse. Daily urine specimens allowed the day of ovulation to be estimated and conceptions to be identified, based on assays of excreted hormones. Applying the parametric model to these data, the estimated mean viable lifetime for sperm is 1.4 days, while the lifetime of the ovum appears to be less than a day.(ABSTRACT TRUNCATED AT 250 WORDS)
Chin, Wen Cheong; Lee, Min Cherng; Yap, Grace Lee Ching
2016-01-01
High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai-Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.
Herdağdelen, Amaç; Marelli, Marco
2017-05-01
Corpus-based word frequencies are one of the most important predictors in language processing tasks. Frequencies based on conversational corpora (such as movie subtitles) are shown to better capture the variance in lexical decision tasks compared to traditional corpora. In this study, we show that frequencies computed from social media are currently the best frequency-based estimators of lexical decision reaction times (up to 3.6% increase in explained variance). The results are robust (observed for Twitter- and Facebook-based frequencies on American English and British English datasets) and are still substantial when we control for corpus size. © 2016 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.
Model Based Analysis of the Variance Estimators for the Combined ...
African Journals Online (AJOL)
In this paper we study the variance estimators for the combined ratio estimator under an appropriate asymptotic framework. An alternative bias-robust variance estimator, different from that suggested by Valliant (1987), is derived. Several variance estimators are compared in an empirical study using a real population.
A Context-Aware Model to Provide Positioning in Disaster Relief Scenarios
Directory of Open Access Journals (Sweden)
Daniel Moreno
2015-09-01
Full Text Available The effectiveness of the work performed during disaster relief efforts is highly dependent on the coordination of activities conducted by the first responders deployed in the affected area. Such coordination, in turn, depends on an appropriate management of geo-referenced information. Therefore, enabling first responders to count on positioning capabilities during these activities is vital to increase the effectiveness of the response process. The positioning methods used in this scenario must assume a lack of infrastructure-based communication and electrical energy, which usually characterizes affected areas. Although positioning systems such as the Global Positioning System (GPS have been shown to be useful, we cannot assume that all devices deployed in the area (or most of them will have positioning capabilities by themselves. Typically, many first responders carry devices that are not capable of performing positioning on their own, but that require such a service. In order to help increase the positioning capability of first responders in disaster-affected areas, this paper presents a context-aware positioning model that allows mobile devices to estimate their position based on information gathered from their surroundings. The performance of the proposed model was evaluated using simulations, and the obtained results show that mobile devices without positioning capabilities were able to use the model to estimate their position. Moreover, the accuracy of the positioning model has been shown to be suitable for conducting most first response activities.
Williamson, David H; Ceccarelli, Daniela M; Evans, Richard D; Hill, Jos K; Russ, Garry R
2014-01-01
No-take marine reserves (NTMRs) are increasingly being established to conserve or restore biodiversity and to enhance the sustainability of fisheries. Although effectively designed and protected NTMR networks can yield conservation and fishery benefits, reserve effects often fail to manifest in systems where there are high levels of non-compliance by fishers (poaching). Obtaining reliable estimates of NTMR non-compliance can be expensive and logistically challenging, particularly in areas with limited or non-existent resources for conducting surveillance and enforcement. Here we assess the utility of density estimates and re-accumulation rates of derelict (lost and abandoned) fishing line as a proxy for fishing effort and NTMR non-compliance on fringing coral reefs in three island groups of the Great Barrier Reef Marine Park (GBRMP), Australia. Densities of derelict fishing line were consistently lower on reefs within old (>20 year) NTMRs than on non-NTMR reefs (significantly in the Palm and Whitsunday Islands), whereas line densities did not differ significantly between reefs in new NTMRs (5 years of protection) and non-NTMR reefs. A manipulative experiment in which derelict fishing lines were removed from a subset of the monitoring sites demonstrated that lines re-accumulated on NTMR reefs at approximately one third (32.4%) of the rate observed on non-NTMR reefs over a thirty-two month period. Although these inshore NTMRs have long been considered some of the best protected within the GBRMP, evidence presented here suggests that the level of non-compliance with NTMR regulations is higher than previously assumed.
Directory of Open Access Journals (Sweden)
David H Williamson
Full Text Available No-take marine reserves (NTMRs are increasingly being established to conserve or restore biodiversity and to enhance the sustainability of fisheries. Although effectively designed and protected NTMR networks can yield conservation and fishery benefits, reserve effects often fail to manifest in systems where there are high levels of non-compliance by fishers (poaching. Obtaining reliable estimates of NTMR non-compliance can be expensive and logistically challenging, particularly in areas with limited or non-existent resources for conducting surveillance and enforcement. Here we assess the utility of density estimates and re-accumulation rates of derelict (lost and abandoned fishing line as a proxy for fishing effort and NTMR non-compliance on fringing coral reefs in three island groups of the Great Barrier Reef Marine Park (GBRMP, Australia. Densities of derelict fishing line were consistently lower on reefs within old (>20 year NTMRs than on non-NTMR reefs (significantly in the Palm and Whitsunday Islands, whereas line densities did not differ significantly between reefs in new NTMRs (5 years of protection and non-NTMR reefs. A manipulative experiment in which derelict fishing lines were removed from a subset of the monitoring sites demonstrated that lines re-accumulated on NTMR reefs at approximately one third (32.4% of the rate observed on non-NTMR reefs over a thirty-two month period. Although these inshore NTMRs have long been considered some of the best protected within the GBRMP, evidence presented here suggests that the level of non-compliance with NTMR regulations is higher than previously assumed.
Sinclair, Shane; Hack, Thomas F; Raffin-Bouchal, Shelley; McClement, Susan; Stajduhar, Kelli; Singh, Pavneet; Hagen, Neil A; Sinnarajah, Aynharan; Chochinov, Harvey Max
2018-03-14
Healthcare providers are considered the primary conduit of compassion in healthcare. Although most healthcare providers desire to provide compassion, and patients and families expect to receive it, an evidence-based understanding of the construct and its associated dimensions from the perspective of healthcare providers is needed. The aim of this study was to investigate healthcare providers' perspectives and experiences of compassion in order to generate an empirically derived, clinically informed model. Data were collected via focus groups with frontline healthcare providers and interviews with peer-nominated exemplary compassionate healthcare providers. Data were independently and collectively analysed by the research team in accordance with Straussian grounded theory. 57 healthcare providers were recruited from urban and rural palliative care services spanning hospice, home care, hospital-based consult teams, and a dedicated inpatient unit within Alberta, Canada. Five categories and 13 associated themes were identified, illustrated in the Healthcare Provider Compassion Model depicting the dimensions of compassion and their relationship to one another. Compassion was conceptualised as-a virtuous and intentional response to know a person, to discern their needs and ameliorate their suffering through relational understanding and action. An empirical foundation of healthcare providers' perspectives on providing compassionate care was generated. While the dimensions of the Healthcare Provider Compassion Model were congruent with the previously developed Patient Model, further insight into compassion is now evident. The Healthcare Provider Compassion Model provides a model to guide clinical practice and research focused on developing interventions, measures and resources to improve it. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly
Lee, Soojeong; Rajan, Sreeraman; Jeon, Gwanggil; Chang, Joon-Hyuk; Dajani, Hilmi R; Groza, Voicu Z
2017-06-01
Blood pressure (BP) is one of the most important vital indicators and plays a key role in determining the cardiovascular activity of patients. This paper proposes a hybrid approach consisting of nonparametric bootstrap (NPB) and machine learning techniques to obtain the characteristic ratios (CR) used in the blood pressure estimation algorithm to improve the accuracy of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates and obtain confidence intervals (CI). The NPB technique is used to circumvent the requirement for large sample set for obtaining the CI. A mixture of Gaussian densities is assumed for the CRs and Gaussian mixture model (GMM) is chosen to estimate the SBP and DBP ratios. The K-means clustering technique is used to obtain the mixture order of the Gaussian densities. The proposed approach achieves grade "A" under British Society of Hypertension testing protocol and is superior to the conventional approach based on maximum amplitude algorithm (MAA) that uses fixed CR ratios. The proposed approach also yields a lower mean error (ME) and the standard deviation of the error (SDE) in the estimates when compared to the conventional MAA method. In addition, CIs obtained through the proposed hybrid approach are also narrower with a lower SDE. The proposed approach combining the NPB technique with the GMM provides a methodology to derive individualized characteristic ratio. The results exhibit that the proposed approach enhances the accuracy of SBP and DBP estimation and provides narrower confidence intervals for the estimates. Copyright © 2015 Elsevier Ltd. All rights reserved.
Estimating model parameters in nonautonomous chaotic systems using synchronization
International Nuclear Information System (INIS)
Yang, Xiaoli; Xu, Wei; Sun, Zhongkui
2007-01-01
In this Letter, a technique is addressed for estimating unknown model parameters of multivariate, in particular, nonautonomous chaotic systems from time series of state variables. This technique uses an adaptive strategy for tracking unknown parameters in addition to a linear feedback coupling for synchronizing systems, and then some general conditions, by means of the periodic version of the LaSalle invariance principle for differential equations, are analytically derived to ensure precise evaluation of unknown parameters and identical synchronization between the concerned experimental system and its corresponding receiver one. Exemplifies are presented by employing a parametrically excited 4D new oscillator and an additionally excited Ueda oscillator. The results of computer simulations reveal that the technique not only can quickly track the desired parameter values but also can rapidly respond to changes in operating parameters. In addition, the technique can be favorably robust against the effect of noise when the experimental system is corrupted by bounded disturbance and the normalized absolute error of parameter estimation grows almost linearly with the cutoff value of noise strength in simulation
Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models
Ting, Chee-Ming
2017-12-06
We consider the challenges in estimating state-related changes in brain connectivity networks with a large number of nodes. Existing studies use sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms K-means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to resting-state fMRI data, our method successfully identifies modular organization in resting-state networks in consistency with other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states.
The model for estimation production cost of embroidery handicraft
Nofierni; Sriwana, IK; Septriani, Y.
2017-12-01
Embroidery industry is one of type of micro industry that produce embroidery handicraft. These industries are emerging in some rural areas of Indonesia. Embroidery clothing are produce such as scarves and clothes that show cultural value of certain region. The owner of an enterprise must calculate the cost of production before making a decision on how many products are received from the customer. A calculation approach to production cost analysis is needed to consider the feasibility of each order coming. This study is proposed to design the expert system (ES) in order to improve production management in the embroidery industry. The model will design used Fuzzy inference system as a model to estimate production cost. Research conducted based on survey and knowledge acquisitions from stakeholder of supply chain embroidery handicraft industry at Bukittinggi, West Sumatera, Indonesia. This paper will use fuzzy input where the quality, the complexity of the design and the working hours required and the result of the model are useful to manage production cost on embroidery production.
Estimating Agricultural Losses using Flood Modeling for Rural Area
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Muhadi Nur Atirah
2017-01-01
Full Text Available Flooding is the most significant natural hazard in Malaysia in terms of population affected, frequency, flood extent, flood duration and social economic damage. Flooding causes loss of lives, injuries, property damage and leave some economic damage to the country especially when it occurs in a rural area where the main income is dependent on agricultural area. This study focused on flooding in oil palm plantations, rubber plantations and fruits and vegetables area. InfoWorks ICM was used to develop a flood model to study the impact of flooding and to mitigate the floods using a retention pond. Later, Geographical Information System (GIS together with the flood model were used for the analysis on flood damage assessment and management of flood risk. The estimated total damage for three different flood event; 10 ARI, 50 ARI and 100 ARI involved millions of ringgits. In reducing the flood impact along the Selangor River, retention pond was suggested, modeled and tested. By constructing retention pond, flood extents in agricultural area were reduced significantly by 60.49% for 10 ARI, 45.39% for 50 ARI and 46.54% for 100 ARI.
Bayesian parameter estimation for stochastic models of biological cell migration
Dieterich, Peter; Preuss, Roland
2013-08-01
Cell migration plays an essential role under many physiological and patho-physiological conditions. It is of major importance during embryonic development and wound healing. In contrast, it also generates negative effects during inflammation processes, the transmigration of tumors or the formation of metastases. Thus, a reliable quantification and characterization of cell paths could give insight into the dynamics of these processes. Typically stochastic models are applied where parameters are extracted by fitting models to the so-called mean square displacement of the observed cell group. We show that this approach has several disadvantages and problems. Therefore, we propose a simple procedure directly relying on the positions of the cell's trajectory and the covariance matrix of the positions. It is shown that the covariance is identical with the spatial aging correlation function for the supposed linear Gaussian models of Brownian motion with drift and fractional Brownian motion. The technique is applied and illustrated with simulated data showing a reliable parameter estimation from single cell paths.
Bindschadler, Michael; Modgil, Dimple; Branch, Kelley R; La Riviere, Patrick J; Alessio, Adam M
2014-04-07
Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)(-1), cardiac output = 3, 5, 8 L min(-1)). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This
Bindschadler, Michael; Modgil, Dimple; Branch, Kelley R.; La Riviere, Patrick J.; Alessio, Adam M.
2014-04-01
Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)-1, cardiac output = 3, 5, 8 L min-1). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that
House thermal model parameter estimation method for Model Predictive Control applications
van Leeuwen, Richard Pieter; de Wit, J.B.; Fink, J.; Smit, Gerardus Johannes Maria
In this paper we investigate thermal network models with different model orders applied to various Dutch low-energy house types with high and low interior thermal mass and containing floor heating. Parameter estimations are performed by using data from TRNSYS simulations. The paper discusses results
Capacitance Online Estimation Based on Adaptive Model Observer
Directory of Open Access Journals (Sweden)
Cen Zhaohui
2016-01-01
Full Text Available As a basic component in electrical and electronic devices, capacitors are very popular in electrical circuits. Conventional capacitors such as electrotype capacitors are easy to degradation, aging and fatigue due to long‐time running and outer damages such as mechanical and electrical stresses. In this paper, a novel online capacitance measurement/estimation approach is proposed. Firstly, an Adaptive Model Observer (AMO is designed based on the capacitor's circuit equations. Secondly, the AMO’s stability and convergence are analysed and discussed. Finally, Capacitors with different capacitance and different initial voltages in a buck converter topology are tested and validated. Simulation results demonstrate the effectiveness and superiority of our proposed approach.
An improved model for estimating pesticide emissions for agricultural LCA
DEFF Research Database (Denmark)
Dijkman, Teunis Johannes; Birkved, Morten; Hauschild, Michael Zwicky
2011-01-01
Credible quantification of chemical emissions in the inventory phase of Life Cycle Assessment (LCA) is crucial since chemicals are the dominating cause of the human and ecotoxicity-related environmental impacts in Life Cycle Impact Assessment (LCIA). When applying LCA for assessment of agricultural...... products, off-target pesticide emissions need to be quantified as accurately as possible because of the considerable toxicity effects associated with chemicals designed to have a high impact on biological organisms like for example insects or weed plants. PestLCI was developed to estimate the fractions....... To overcome these limitations, a reworked and updated version of PestLCI is presented here. The new model includes 16 European climate types and 6 mean European soil characteristic profiles covering all dominant European soil types to widen the geographical scope and to allow contemporary (varying site...
Directory of Open Access Journals (Sweden)
Ali P. Yunus
2016-04-01
Full Text Available Sea-level rise (SLR from global warming may have severe consequences for coastal cities, particularly when combined with predicted increases in the strength of tidal surges. Predicting the regional impact of SLR flooding is strongly dependent on the modelling approach and accuracy of topographic data. Here, the areas under risk of sea water flooding for London boroughs were quantified based on the projected SLR scenarios reported in Intergovernmental Panel on Climate Change (IPCC fifth assessment report (AR5 and UK climatic projections 2009 (UKCP09 using a tidally-adjusted bathtub modelling approach. Medium- to very high-resolution digital elevation models (DEMs are used to evaluate inundation extents as well as uncertainties. Depending on the SLR scenario and DEMs used, it is estimated that 3%–8% of the area of Greater London could be inundated by 2100. The boroughs with the largest areas at risk of flooding are Newham, Southwark, and Greenwich. The differences in inundation areas estimated from a digital terrain model and a digital surface model are much greater than the root mean square error differences observed between the two data types, which may be attributed to processing levels. Flood models from SRTM data underestimate the inundation extent, so their results may not be reliable for constructing flood risk maps. This analysis provides a broad-scale estimate of the potential consequences of SLR and uncertainties in the DEM-based bathtub type flood inundation modelling for London boroughs.
Belfield, Clive; Bailey, Thomas
2017-01-01
Recently, studies have adopted fixed effects modeling to identify the returns to college. This method has the advantage over ordinary least squares estimates in that unobservable, individual-level characteristics that may bias the estimated returns are differenced out. But the method requires extensive longitudinal data and involves complex…
Estimating the diets of animals using stable isotopes and a comprehensive Bayesian mixing model.
Directory of Open Access Journals (Sweden)
John B Hopkins
Full Text Available Using stable isotope mixing models (SIMMs as a tool to investigate the foraging ecology of animals is gaining popularity among researchers. As a result, statistical methods are rapidly evolving and numerous models have been produced to estimate the diets of animals--each with their benefits and their limitations. Deciding which SIMM to use is contingent on factors such as the consumer of interest, its food sources, sample size, the familiarity a user has with a particular framework for statistical analysis, or the level of inference the researcher desires to make (e.g., population- or individual-level. In this paper, we provide a review of commonly used SIMM models and describe a comprehensive SIMM that includes all features commonly used in SIMM analysis and two new features. We used data collected in Yosemite National Park to demonstrate IsotopeR's ability to estimate dietary parameters. We then examined the importance of each feature in the model and compared our results to inferences from commonly used SIMMs. IsotopeR's user interface (in R will provide researchers a user-friendly tool for SIMM analysis. The model is also applicable for use in paleontology, archaeology, and forensic studies as well as estimating pollution inputs.
Holland-Letz, Tim; Gunkel, Nikolas; Amtmann, Eberhard; Kopp-Schneider, Annette
2017-11-27
In toxicology and related areas, interaction effects between two substances are commonly expressed through a combination index [Formula: see text] evaluated separately at different effect levels and mixture ratios. Often, these indices are combined into a graphical representation, the isobologram. Instead of estimating the combination indices at the experimental mixture ratios only, we propose a simple parametric model for estimating the underlying interaction function. We integrate this approach into a joint model where both the parameters of the dose-response functions of the singular substances and the interaction parameters can be estimated simultaneously. As an additional benefit, this concept allows to determine optimal statistical designs for combination studies optimizing the estimation of the interaction function as a whole. From an optimal design perspective, finding the interaction parameters generally corresponds to a [Formula: see text]-optimality resp. [Formula: see text]-optimality design problem, while estimation of all underlying dose response parameters corresponds to a [Formula: see text]-optimality design problem. We show how optimal designs can be obtained in either case as well as how combination designs providing reasonable performance in regard to both criteria can be determined by putting a constraint on the efficiency in regard to one of the criteria and optimizing for the other. As all designs require prior information about model parameter values, which may be unreliable in practice, the effect of misspecifications is investigated as well.
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.
Multi-scale occupancy estimation and modelling using multiple detection methods
Nichols, James D.; Bailey, Larissa L.; O'Connell, Allan F.; Talancy, Neil W.; Grant, Evan H. Campbell; Gilbert, Andrew T.; Annand, Elizabeth M.; Husband, Thomas P.; Hines, James E.
2008-01-01
Occupancy estimation and modelling based on detection–nondetection data provide an effective way of exploring change in a species’ distribution across time and space in cases where the species is not always detected with certainty. Today, many monitoring programmes target multiple species, or life stages within a species, requiring the use of multiple detection methods. When multiple methods or devices are used at the same sample sites, animals can be detected by more than one method.We develop occupancy models for multiple detection methods that permit simultaneous use of data from all methods for inference about method-specific detection probabilities. Moreover, the approach permits estimation of occupancy at two spatial scales: the larger scale corresponds to species’ use of a sample unit, whereas the smaller scale corresponds to presence of the species at the local sample station or site.We apply the models to data collected on two different vertebrate species: striped skunks Mephitis mephitis and red salamanders Pseudotriton ruber. For striped skunks, large-scale occupancy estimates were consistent between two sampling seasons. Small-scale occupancy probabilities were slightly lower in the late winter/spring when skunks tend to conserve energy, and movements are limited to males in search of females for breeding. There was strong evidence of method-specific detection probabilities for skunks. As anticipated, large- and small-scale occupancy areas completely overlapped for red salamanders. The analyses provided weak evidence of method-specific detection probabilities for this species.Synthesis and applications. Increasingly, many studies are utilizing multiple detection methods at sampling locations. The modelling approach presented here makes efficient use of detections from multiple methods to estimate occupancy probabilities at two spatial scales and to compare detection probabilities associated with different detection methods. The models can be
A model for estimating the minimum number of offspring to sample in studies of reproductive success.
Anderson, Joseph H; Ward, Eric J; Carlson, Stephanie M
2011-01-01
Molecular parentage permits studies of selection and evolution in fecund species with cryptic mating systems, such as fish, amphibians, and insects. However, there exists no method for estimating the number of offspring that must be assigned parentage to achieve robust estimates of reproductive success when only a fraction of offspring can be sampled. We constructed a 2-stage model that first estimated the mean (μ) and variance (v) in reproductive success from published studies on salmonid fishes and then sampled offspring from reproductive success distributions simulated from the μ and v estimates. Results provided strong support for modeling salmonid reproductive success via the negative binomial distribution and suggested that few offspring samples are needed to reject the null hypothesis of uniform offspring production. However, the sampled reproductive success distributions deviated significantly (χ(2) goodness-of-fit test p value reproductive success distribution at rates often >0.05 and as high as 0.24, even when hundreds of offspring were assigned parentage. In general, reproductive success patterns were less accurate when offspring were sampled from cohorts with larger numbers of parents and greater variance in reproductive success. Our model can be reparameterized with data from other species and will aid researchers in planning reproductive success studies by providing explicit sampling targets required to accurately assess reproductive success.
Roemer, R B; Booth, D; Bhavsar, A A; Walter, G H; Terry, L I
2012-12-21
A mathematical model based on conservation of energy has been developed and used to simulate the temperature responses of cones of the Australian cycads Macrozamia lucida and Macrozamia. macleayi during their daily thermogenic cycle. These cones generate diel midday thermogenic temperature increases as large as 12 °C above ambient during their approximately two week pollination period. The cone temperature response model is shown to accurately predict the cones' temperatures over multiple days as based on simulations of experimental results from 28 thermogenic events from 3 different cones, each simulated for either 9 or 10 sequential days. The verified model is then used as the foundation of a new, parameter estimation based technique (termed inverse calorimetry) that estimates the cones' daily metabolic heating rates from temperature measurements alone. The inverse calorimetry technique's predictions of the major features of the cones' thermogenic metabolism compare favorably with the estimates from conventional respirometry (indirect calorimetry). Because the new technique uses only temperature measurements, and does not require measurements of oxygen consumption, it provides a simple, inexpensive and portable complement to conventional respirometry for estimating metabolic heating rates. It thus provides an additional tool to facilitate field and laboratory investigations of the bio-physics of thermogenic plants. Copyright © 2012 Elsevier Ltd. All rights reserved.
Social models provide a norm of appropriate food intake for young women.
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Lenny R Vartanian
Full Text Available It is often assumed that social models influence people's eating behavior by providing a norm of appropriate food intake, but this hypothesis has not been directly tested. In three experiments, female participants were exposed to a low-intake model, a high-intake model, or no model (control condition. Experiments 1 and 2 used a remote-confederate manipulation and were conducted in the context of a cookie taste test. Experiment 3 used a live confederate and was conducted in the context of a task during which participants were given incidental access to food. Participants also rated the extent to which their food intake was influenced by a variety of factors (e.g., hunger, taste, how much others ate. In all three experiments, participants in the low-intake conditions ate less than did participants in the high-intake conditions, and also reported a lower perceived norm of appropriate intake. Furthermore, perceived norms of appropriate intake mediated the effects of the social model on participants' food intake. Despite the observed effects of the social models, participants were much more likely to indicate that their food intake was influenced by taste and hunger than by the behavior of the social models. Thus, social models appear to influence food intake by providing a norm of appropriate eating behavior, but people may be unaware of the influence of a social model on their behavior.
Wiedinmyer, C.; Akagi, S. K.; Yokelson, R. J.; Emmons, L. K.; Al-Saadi, J. A.; Orlando, J. J.; Soja, A. J.
2011-07-01
The Fire INventory from NCAR version 1.0 (FINNv1) provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include biofuel use and trash burning. Emission factors used in the calculations have been updated with recent data, particularly for the non-methane organic compounds (NMOC). The resulting global annual NMOC emission estimates are as much as a factor of 5 greater than some prior estimates. Chemical speciation profiles, necessary to allocate the total NMOC emission estimates to lumped species for use by chemical transport models, are provided for three widely used chemical mechanisms: SAPRC99, GEOS-CHEM, and MOZART-4. Using these profiles, FINNv1 also provides global estimates of key organic compounds, including formaldehyde and methanol. Uncertainties in the emissions estimates arise from several of the method steps. The use of fire hot spots, assumed area burned, land cover maps, biomass consumption estimates, and emission factors all introduce error into the model estimates. The uncertainty in the FINNv1 emission estimates are about a factor of two; but, the global estimates agree reasonably well with other global inventories of biomass burning emissions for CO, CO2, and other species with less variable emission factors. FINNv1 emission estimates have been developed specifically for modeling atmospheric chemistry and air quality in a consistent framework at scales from local to global. The product is unique because of the high temporal and spatial resolution, global coverage, and the number of species estimated. FINNv1 can be used for both hindcast and forecast or near-real time model applications and the results are being critically evaluated with models and observations whenever possible.
Directory of Open Access Journals (Sweden)
C. Wiedinmyer
2011-07-01
Full Text Available The Fire INventory from NCAR version 1.0 (FINNv1 provides daily, 1 km resolution, global estimates of the trace gas and particle emissions from open burning of biomass, which includes wildfire, agricultural fires, and prescribed burning and does not include biofuel use and trash burning. Emission factors used in the calculations have been updated with recent data, particularly for the non-methane organic compounds (NMOC. The resulting global annual NMOC emission estimates are as much as a factor of 5 greater than some prior estimates. Chemical speciation profiles, necessary to allocate the total NMOC emission estimates to lumped species for use by chemical transport models, are provided for three widely used chemical mechanisms: SAPRC99, GEOS-CHEM, and MOZART-4. Using these profiles, FINNv1 also provides global estimates of key organic compounds, including formaldehyde and methanol. Uncertainties in the emissions estimates arise from several of the method steps. The use of fire hot spots, assumed area burned, land cover maps, biomass consumption estimates, and emission factors all introduce error into the model estimates. The uncertainty in the FINNv1 emission estimates are about a factor of two; but, the global estimates agree reasonably well with other global inventories of biomass burning emissions for CO, CO_{2}, and other species with less variable emission factors. FINNv1 emission estimates have been developed specifically for modeling atmospheric chemistry and air quality in a consistent framework at scales from local to global. The product is unique because of the high temporal and spatial resolution, global coverage, and the number of species estimated. FINNv1 can be used for both hindcast and forecast or near-real time model applications and the results are being critically evaluated with models and observations whenever possible.
The model-based estimates of important cancer risk factors and screening behaviors are obtained by combining the responses to the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS).
Value-added strategy models to provide quality services in senior health business.
Yang, Ya-Ting; Lin, Neng-Pai; Su, Shyi; Chen, Ya-Mei; Chang, Yao-Mao; Handa, Yujiro; Khan, Hafsah Arshed Ali; Elsa Hsu, Yi-Hsin
2017-06-20
The rapid population aging is now a global issue. The increase in the elderly population will impact the health care industry and health enterprises; various senior needs will promote the growth of the senior health industry. Most senior health studies are focused on the demand side and scarcely on supply. Our study selected quality enterprises focused on aging health and analyzed different strategies to provide excellent quality services to senior health enterprises. We selected 33 quality senior health enterprises in Taiwan and investigated their excellent quality services strategies by face-to-face semi-structured in-depth interviews with CEO and managers of each enterprise in 2013. A total of 33 senior health enterprises in Taiwan. Overall, 65 CEOs and managers of 33 enterprises were interviewed individually. None. Core values and vision, organization structure, quality services provided, strategies for quality services. This study's results indicated four type of value-added strategy models adopted by senior enterprises to offer quality services: (i) residential care and co-residence model, (ii) home care and living in place model, (iii) community e-business experience model and (iv) virtual and physical portable device model. The common part in these four strategy models is that the services provided are elderly centered. These models offer virtual and physical integrations, and also offer total solutions for the elderly and their caregivers. Through investigation of successful strategy models for providing quality services to seniors, we identified opportunities to develop innovative service models and successful characteristics, also policy implications were summarized. The observations from this study will serve as a primary evidenced base for enterprises developing their senior market and, also for promoting the value co-creation possibility through dialogue between customers and those that deliver service. © The Author 2017. Published by Oxford
The HINTS is designed to produce reliable estimates at the national and regional levels. GIS maps using HINTS data have been used to provide a visual representation of possible geographic relationships in HINTS cancer-related variables.
Performance Estimation of Networked Business Models: Case Study on a Finnish eHealth Service Project
Marikka Heikkilä; Sam Solaimani; Aki Soudunsaari; Mila Hakanen; Leni Kuivaniemi; Mari Suoranta
2014-01-01
Purpose: The objective of this paper is to propose and demonstrate a framework for estimating performance in a networked business model. Design/methodology/approach: Our approach is design science, utilising action research in studying a case of four independent firms in Health & Wellbeing sector aiming to jointly provide a new service for business and private customers. The duration of the research study is 3 years. Findings: We propose that a balanced set of performance indicators...
An examination of sources of sensitivity of consumer surplus estimates in travel cost models.
Blaine, Thomas W; Lichtkoppler, Frank R; Bader, Timothy J; Hartman, Travis J; Lucente, Joseph E
2015-03-15
We examine sensitivity of estimates of recreation demand using the Travel Cost Method (TCM) to four factors. Three of the four have been routinely and widely discussed in the TCM literature: a) Poisson verses negative binomial regression; b) application of Englin correction to account for endogenous stratification; c) truncation of the data set to eliminate outliers. A fourth issue we address has not been widely modeled: the potential effect on recreation demand of the interaction between income and travel cost. We provide a straightforward comparison of all four factors, analyzing the impact of each on regression parameters and consumer surplus estimates. Truncation has a modest effect on estimates obtained from the Poisson models but a radical effect on the estimates obtained by way of the negative binomial. Inclusion of an income-travel cost interaction term generally produces a more conservative but not a statistically significantly different estimate of consumer surplus in both Poisson and negative binomial models. It also generates broader confidence intervals. Application of truncation, the Englin correction and the income-travel cost interaction produced the most conservative estimates of consumer surplus and eliminated the statistical difference between the Poisson and the negative binomial. Use of the income-travel cost interaction term reveals that for visitors who face relatively low travel costs, the relationship between income and travel demand is negative, while it is positive for those who face high travel costs. This provides an explanation of the ambiguities on the findings regarding the role of income widely observed in the TCM literature. Our results suggest that policies that reduce access to publicly owned resources inordinately impact local low income recreationists and are contrary to environmental justice. Copyright © 2014 Elsevier Ltd. All rights reserved.
Khoshravesh, Mojtaba; Sefidkouhi, Mohammad Ali Gholami; Valipour, Mohammad
2017-07-01
The proper evaluation of evapotranspiration is essential in food security investigation, farm management, pollution detection, irrigation scheduling, nutrient flows, carbon balance as well as hydrologic modeling, especially in arid environments. To achieve sustainable development and to ensure water supply, especially in arid environments, irrigation experts need tools to estimate reference evapotranspiration on a large scale. In this study, the monthly reference evapotranspiration was estimated by three different regression models including the multivariate fractional polynomial (MFP), robust regression, and Bayesian regression in Ardestan, Esfahan, and Kashan. The results were compared with Food and Agriculture Organization (FAO)-Penman-Monteith (FAO-PM) to select the best model. The results show that at a monthly scale, all models provided a closer agreement with the calculated values for FAO-PM ( R 2 > 0.95 and RMSE < 12.07 mm month-1). However, the MFP model gives better estimates than the other two models for estimating reference evapotranspiration at all stations.
Directory of Open Access Journals (Sweden)
Kovačić Nataša
2015-11-01
Full Text Available The paper addresses the effect of external integration (EI with transport suppliers on the efficiency of travel agencies in the tourism sector supply chains. The main aim is the comparison of different estimation methods used in the structural equation modeling (SEM, applied to discover possible relationships between EIs and efficiencies. The latter are calculated by the means of data envelopment analysis (DEA. While designing the structural equation model, the exploratory and confirmatory factor analyses are also used as preliminary statistical procedures. For the estimation of parameters of SEM model, three different methods are explained, analyzed and compared: maximum likelihood (ML method, Bayesian Markov Chain Monte Carlo (BMCMC method, and unweighted least squares (ULS method. The study reveals that all estimation methods calculate comparable estimated parameters. The results also give an evidence of good model fit performance. Besides, the research confirms that the amplified external integration with transport providers leads to increased efficiency of travel agencies, which might be a very interesting finding for the operational management.
Directory of Open Access Journals (Sweden)
THANH TUNG KHUAT
2017-05-01
Full Text Available Artificial Bee Colony inspired by the foraging behaviour of honey bees is a novel meta-heuristic optimization algorithm in the community of swarm intelligence algorithms. Nevertheless, it is still insufficient in the speed of convergence and the quality of solutions. This paper proposes an approach in order to tackle these downsides by combining the positive aspects of TeachingLearning based optimization and Artificial Bee Colony. The performance of the proposed method is assessed on the software effort estimation problem, which is the complex and important issue in the project management. Software developers often carry out the software estimation in the early stages of the software development life cycle to derive the required cost and schedule for a project. There are a large number of methods for effort estimation in which COCOMO II is one of the most widely used models. However, this model has some restricts because its parameters have not been optimized yet. In this work, therefore, we will present the approach to overcome this limitation of COCOMO II model. The experiments have been conducted on NASA software project dataset and the obtained results indicated that the improvement of parameters provided better estimation capabilities compared to the original COCOMO II model.
A Complex of Business Process Management Models for a Service-Providing IT Company
Yatsenko Roman M.; Balykov Oleksii H.
2017-01-01
The article presents an analysis of a complex of business process management models that are designed to improve the performance of service-providing IT companies. This class of enterprises was selected because of their significant contribution to the Ukrainian economy: third place in the structure of exports, significant budget revenues, high development dynamics, and prospects in the global marketplace. The selected complex of models is designed as a sequence of stages that must be accompli...
ξ common cause failure model and method for defense effectiveness estimation
International Nuclear Information System (INIS)
Li Zhaohuan
1991-08-01
Two issues have been dealt. One is to develop an event based parametric model called ξ-CCF model. Its parameters are expressed in the fraction of the progressive multiplicities of failure events. By these expressions, the contribution of each multiple failure can be presented more clearly. It can help to select defense tactics against common cause failures. The other is to provide a method which is based on the operational experience and engineering judgement to estimate the effectiveness of defense tactics. It is expressed in terms of reduction matrix for a given tactics on a specific plant in the event by event form. The application of practical example shows that the model in cooperation with the method can simply estimate the effectiveness of defense tactics. It can be easily used by the operators and its application may be extended
International Nuclear Information System (INIS)
Barnett, C.S.
1992-01-01
The double contingency principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative and intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive for a search for a quantitative and probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies, as functions of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess the effectiveness of the DCP. (Author)
A New Empirical Model to Estimate Landfill Gas Pollution
Directory of Open Access Journals (Sweden)
Hamidreza Kamalan
2016-07-01
Full Text Available Background: Landfills are the most important producers of methane as human source. So, prediction of landfill gas generation is by far the most important concern of scientists, decision makers, and landfill owners as well as health authorities. Almost all the currently used models are based on Monod equation first order decay rate which is experimental while the main purpose of this research is to develop a numerical model. Methods: A real scale pilot landfill with 4500 tons of municipal solid waste has been designed, constructed, and operated for two years. Required measurements have been done to provide proper data on greenhouse gases emitted by the landfill and monitor its status such as internal temperature, leachate content, and its settlement during two years. Afterwards, weighted residual method has been used to develop the numerical model. Then, the newly mathematical method has been verified with data from another landfill. Results: Measurements showed that the minimum and maximum percentages of methane among landfill gas were 22.3 and 46.1%, respectively. These values for velocity of landfill gas are 0.3 and 0.48 meters per second, in that order. Conclusion: Since there is just 0.6 percent error in calculation as compared to real measurements from a landfill in California and most of the models used have ten percent error, this simple empirical numerical model is suggested to be utilized by scientists, decision makers, and landfill owners
Estimating Spoken Dialog System Quality with User Models
Engelbrecht, Klaus-Peter
2013-01-01
Spoken dialog systems have the potential to offer highly intuitive user interfaces, as they allow systems to be controlled using natural language. However, the complexity inherent in natural language dialogs means that careful testing of the system must be carried out from the very beginning of the design process. This book examines how user models can be used to support such early evaluations in two ways: by running simulations of dialogs, and by estimating the quality judgments of users. First, a design environment supporting the creation of dialog flows, the simulation of dialogs, and the analysis of the simulated data is proposed. How the quality of user simulations may be quantified with respect to their suitability for both formative and summative evaluation is then discussed. The remainder of the book is dedicated to the problem of predicting quality judgments of users based on interaction data. New modeling approaches are presented, which process the dialogs as sequences, and which allow knowl...
Energy Technology Data Exchange (ETDEWEB)
Lepez, V.
2002-12-01
The aim of this thesis is to build a statistical model of oil and gas fields' sizes distribution in a given sedimentary basin, for both the fields that exist in:the subsoil and those which have already been discovered. The estimation of all the parameters of the model via estimation of the density of the observations by model selection of piecewise polynomials by penalized maximum likelihood techniques enables to provide estimates of the total number of fields which are yet to be discovered, by class of size. We assume that the set of underground fields' sizes is an i.i.d. sample of unknown population with Levy-Pareto law with unknown parameter. The set of already discovered fields is a sub-sample without replacement from the previous which is 'size-biased'. The associated inclusion probabilities are to be estimated. We prove that the probability density of the observations is the product of the underlying density and of an unknown weighting function representing the sampling bias. An arbitrary partition of the sizes' interval being set (called a model), the analytical solutions of likelihood maximization enables to estimate both the parameter of the underlying Levy-Pareto law and the weighting function, which is assumed to be piecewise constant and based upon the partition. We shall add a monotonousness constraint over the latter, taking into account the fact that the bigger a field, the higher its probability of being discovered. Horvitz-Thompson-like estimators finally give the conclusion. We then allow our partitions to vary inside several classes of models and prove a model selection theorem which aims at selecting the best partition within a class, in terms of both Kuilback and Hellinger risk of the associated estimator. We conclude by simulations and various applications to real data from sedimentary basins of four continents, in order to illustrate theoretical as well as practical aspects of our model. (author)
Deslauriers, David; Rosburg, Alex J.; Chipps, Steven R.
2017-01-01
We developed a foraging model for young fishes that incorporates handling and digestion rate to estimate daily food consumption. Feeding trials were used to quantify functional feeding response, satiation, and gut evacuation rate. Once parameterized, the foraging model was then applied to evaluate effects of prey type, prey density, water temperature, and fish size on daily feeding rate by age-0 (19–70 mm) pallid sturgeon (Scaphirhynchus albus). Prey consumption was positively related to prey density (for fish >30 mm) and water temperature, but negatively related to prey size and the presence of sand substrate. Model evaluation results revealed good agreement between observed estimates of daily consumption and those predicted by the model (r2 = 0.95). Model simulations showed that fish feeding on Chironomidae or Ephemeroptera larvae were able to gain mass, whereas fish feeding solely on zooplankton lost mass under most conditions. By accounting for satiation and digestive processes in addition to handling time and prey density, the model provides realistic estimates of daily food consumption that can prove useful for evaluating rearing conditions for age-0 fishes.
Estimation models of variance components for farrowing interval in swine
Directory of Open Access Journals (Sweden)
Aderbal Cavalcante Neto
2009-02-01
Full Text Available The main objective of this study was to evaluate the importance of including maternal genetic, common litter environmental and permanent environmental effects in estimation models of variance components for the farrowing interval trait in swine. Data consisting of 1,013 farrowing intervals of Dalland (C-40 sows recorded in two herds were analyzed. Variance components were obtained by the derivative-free restricted maximum likelihood method. Eight models were tested which contained the fixed effects(contemporary group and covariables and the direct genetic additive and residual effects, and varied regarding the inclusion of the maternal genetic, common litter environmental, and/or permanent environmental random effects. The likelihood-ratio test indicated that the inclusion of these effects in the model was unnecessary, but the inclusion of the permanent environmental effect caused changes in the estimates of heritability, which varied from 0.00 to 0.03. In conclusion, the heritability values obtained indicated that this trait appears to present no genetic gain as response to selection. The common litter environmental and the maternal genetic effects did not present any influence on this trait. The permanent environmental effect, however, should be considered in the genetic models for this trait in swine, because its presence caused changes in the additive genetic variance estimates.Este trabalho teve como objetivo principal avaliar a importância da inclusão dos efeitos genético materno, comum de leitegada e de ambiente permanente no modelo de estimação de componentes de variância para a característica intervalo de parto em fêmeas suínas. Foram utilizados dados que consistiam de 1.013 observações de fêmeas Dalland (C-40, registradas em dois rebanhos. As estimativas dos componentes de variância foram realizadas pelo método da máxima verossimilhança restrita livre de derivadas. Foram testados oito modelos, que continham os efeitos
Belkhatir, Zehor
2015-11-23
This paper discusses the estimation of distributed Cerebral Blood Flow (CBF) using spatiotemporal traveling wave model. We consider a damped wave partial differential equation that describes a physiological relationship between the blood mass density and the CBF. The spatiotemporal model is reduced to a finite dimensional system using a cubic b-spline continuous Galerkin method. A Kalman Filter with Unknown Inputs without Direct Feedthrough (KF-UI-WDF) is applied on the obtained reduced differential model to estimate the source term which is the CBF scaled by a factor. Numerical results showing the performances of the adopted estimator are provided.
Performance Estimation of Networked Business Models: Case Study on a Finnish eHealth Service Project
Directory of Open Access Journals (Sweden)
Marikka Heikkilä
2014-08-01
Full Text Available Purpose: The objective of this paper is to propose and demonstrate a framework for estimating performance in a networked business model. Design/methodology/approach: Our approach is design science, utilising action research in studying a case of four independent firms in Health & Wellbeing sector aiming to jointly provide a new service for business and private customers. The duration of the research study is 3 years. Findings: We propose that a balanced set of performance indicators can be defined by paying attention to all main components of the business model, enriched with of network collaboration. The results highlight the importance of measuring all main components of the business model and also the business network partners’ view on trust, contracts and fairness. Research implications: This article contributes to the business model literature by combining business modelling with performance evaluation. The article points out that it is essential to create metrics that can be applied to evaluate and improve the business model blueprints, but it is also important to measure business collaboration aspects. Practical implications: Companies have already adopted Business model canvas or similar business model tools to innovate new business models. We suggest that companies continue their business model innovation work by agreeing on a set of performance metrics, building on the business model components model enriched with measures of network collaboration. Originality/value: This article contributes to the business model literature and praxis by combining business modelling with performance evaluation.
Ecological niche modeling to estimate the distribution of Japanese encephalitis virus in Asia.
Directory of Open Access Journals (Sweden)
Robin H Miller
Full Text Available BACKGROUND: Culex tritaeniorhynchus is the primary vector of Japanese encephalitis virus (JEV, a leading cause of encephalitis in Asia. JEV is transmitted in an enzootic cycle involving large wading birds as the reservoirs and swine as amplifying hosts. The development of a JEV vaccine reduced the number of JE cases in regions with comprehensive childhood vaccination programs, such as in Japan and the Republic of Korea. However, the lack of vaccine programs or insufficient coverage of populations in other endemic countries leaves many people susceptible to JEV. The aim of this study was to predict the distribution of Culex tritaeniorhynchus using ecological niche modeling. METHODS/PRINCIPAL FINDINGS: An ecological niche model was constructed using the Maxent program to map the areas with suitable environmental conditions for the Cx. tritaeniorhynchus vector. Program input consisted of environmental data (temperature, elevation, rainfall and known locations of vector presence resulting from an extensive literature search and records from MosquitoMap. The statistically significant Maxent model of the estimated probability of Cx. tritaeniorhynchus presence showed that the mean temperatures of the wettest quarter had the greatest impact on the model. Further, the majority of human Japanese encephalitis (JE cases were located in regions with higher estimated probability of Cx. tritaeniorhynchus presence. CONCLUSIONS/SIGNIFICANCE: Our ecological niche model of the estimated probability of Cx. tritaeniorhynchus presence provides a framework for better allocation of vector control resources, particularly in locations where JEV vaccinations are unavailable. Furthermore, this model provides estimates of vector probability that could improve vector surveillance programs and JE control efforts.
Uncertainty Estimation in Tsunami Initial Condition From Rapid Bayesian Finite Fault Modeling
Benavente, R. F.; Dettmer, J.; Cummins, P. R.; Urrutia, A.; Cienfuegos, R.
2017-12-01
It is well known that kinematic rupture models for a given earthquake can present discrepancies even when similar datasets are employed in the inversion process. While quantifying this variability can be critical when making early estimates of the earthquake and triggered tsunami impact, "most likely models" are normally used for this purpose. In this work, we quantify the uncertainty of the tsunami initial condition for the great Illapel earthquake (Mw = 8.3, 2015, Chile). We focus on utilizing data and inversion methods that are suitable to rapid source characterization yet provide meaningful and robust results. Rupture models from teleseismic body and surface waves as well as W-phase are derived and accompanied by Bayesian uncertainty estimates from linearized inversion under positivity constraints. We show that robust and consistent features about the rupture kinematics appear when working within this probabilistic framework. Moreover, by using static dislocation theory, we translate the probabilistic slip distributions into seafloor deformation which we interpret as a tsunami initial condition. After considering uncertainty, our probabilistic seafloor deformation models obtained from different data types appear consistent with each other providing meaningful results. We also show that selecting just a single "representative" solution from the ensemble of initial conditions for tsunami propagation may lead to overestimating information content in the data. Our results suggest that rapid, probabilistic rupture models can play a significant role during emergency response by providing robust information about the extent of the disaster.
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.)
Effects of uncertainty in model predictions of individual tree volume on large area volume estimates
Ronald E. McRoberts; James A. Westfall
2014-01-01
Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model...