Robust Depth-Weighted Wavelet for Nonparametric Regression Models
Institute of Scientific and Technical Information of China (English)
Lu LIN
2005-01-01
In the nonpaxametric regression models, the original regression estimators including kernel estimator, Fourier series estimator and wavelet estimator are always constructed by the weighted sum of data, and the weights depend only on the distance between the design points and estimation points. As a result these estimators are not robust to the perturbations in data. In order to avoid this problem, a new nonparametric regression model, called the depth-weighted regression model, is introduced and then the depth-weighted wavelet estimation is defined. The new estimation is robust to the perturbations in data, which attains very high breakdown value close to 1/2. On the other hand, some asymptotic behaviours such as asymptotic normality are obtained. Some simulations illustrate that the proposed wavelet estimator is more robust than the original wavelet estimator and, as a price to pay for the robustness, the new method is slightly less efficient than the original method.
A nonparametric dynamic additive regression model for longitudinal data
DEFF Research Database (Denmark)
Martinussen, Torben; Scheike, Thomas H.
2000-01-01
dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models......dynamic linear models, estimating equations, least squares, longitudinal data, nonparametric methods, partly conditional mean models, time-varying-coefficient models...
Genomic breeding value estimation using nonparametric additive regression models
Directory of Open Access Journals (Sweden)
Solberg Trygve
2009-01-01
Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
On concurvity in nonlinear and nonparametric regression models
Directory of Open Access Journals (Sweden)
Sonia Amodio
2014-12-01
Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.
Efficient robust nonparametric estimation in a semimartingale regression model
Konev, Victor
2010-01-01
The paper considers the problem of robust estimating a periodic function in a continuous time regression model with dependent disturbances given by a general square integrable semimartingale with unknown distribution. An example of such a noise is non-gaussian Ornstein-Uhlenbeck process with the L\\'evy process subordinator, which is used to model the financial Black-Scholes type markets with jumps. An adaptive model selection procedure, based on the weighted least square estimates, is proposed. Under general moment conditions on the noise distribution, sharp non-asymptotic oracle inequalities for the robust risks have been derived and the robust efficiency of the model selection procedure has been shown.
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
Karabatsos, George; Walker, Stephen G.
2013-01-01
The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…
Faraway, Julian J
2005-01-01
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...
Stahel-Donoho kernel estimation for fixed design nonparametric regression models
Institute of Scientific and Technical Information of China (English)
LIN; Lu
2006-01-01
This paper reports a robust kernel estimation for fixed design nonparametric regression models.A Stahel-Donoho kernel estimation is introduced,in which the weight functions depend on both the depths of data and the distances between the design points and the estimation points.Based on a local approximation,a computational technique is given to approximate to the incomputable depths of the errors.As a result the new estimator is computationally efficient.The proposed estimator attains a high breakdown point and has perfect asymptotic behaviors such as the asymptotic normality and convergence in the mean squared error.Unlike the depth-weighted estimator for parametric regression models,this depth-weighted nonparametric estimator has a simple variance structure and then we can compare its efficiency with the original one.Some simulations show that the new method can smooth the regression estimation and achieve some desirable balances between robustness and efficiency.
Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Directory of Open Access Journals (Sweden)
Xibin Zhang
2016-04-01
Full Text Available This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP growth rates among the organisation for economic co-operation and development (OECD and non-OECD countries.
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction
Institute of Scientific and Technical Information of China (English)
WENG Jian-cheng; HU Zhong-wei; YU Quan; REN Fu-tian
2007-01-01
A K-nearest neighbor (K-NN) based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series,collected from the floating car data (FCD) system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.
Wei, Jiawei
2011-07-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee, et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey one-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity, et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.
Wei, Jiawei; Carroll, Raymond J; Maity, Arnab
2011-07-01
We consider the problem of testing for a constant nonparametric effect in a general semi-parametric regression model when there is the potential for interaction between the parametrically and nonparametrically modeled variables. The work was originally motivated by a unique testing problem in genetic epidemiology (Chatterjee, et al., 2006) that involved a typical generalized linear model but with an additional term reminiscent of the Tukey one-degree-of-freedom formulation, and their interest was in testing for main effects of the genetic variables, while gaining statistical power by allowing for a possible interaction between genes and the environment. Later work (Maity, et al., 2009) involved the possibility of modeling the environmental variable nonparametrically, but they focused on whether there was a parametric main effect for the genetic variables. In this paper, we consider the complementary problem, where the interest is in testing for the main effect of the nonparametrically modeled environmental variable. We derive a generalized likelihood ratio test for this hypothesis, show how to implement it, and provide evidence that our method can improve statistical power when compared to standard partially linear models with main effects only. We use the method for the primary purpose of analyzing data from a case-control study of colorectal adenoma.
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
BOOTSTRAP WAVELET IN THE NONPARAMETRIC REGRESSION MODEL WITH WEAKLY DEPENDENT PROCESSES
Institute of Scientific and Technical Information of China (English)
林路; 张润楚
2004-01-01
This paper introduces a method of bootstrap wavelet estimation in a nonparametric regression model with weakly dependent processes for both fixed and random designs. The asymptotic bounds for the bias and variance of the bootstrap wavelet estimators are given in the fixed design model. The conditional normality for a modified version of the bootstrap wavelet estimators is obtained in the fixed model. The consistency for the bootstrap wavelet estimator is also proved in the random design model. These results show that the bootstrap wavelet method is valid for the model with weakly dependent processes.
Nonparametric Regression with Common Shocks
Directory of Open Access Journals (Sweden)
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Passenger Flow Prediction of Subway Transfer Stations Based on Nonparametric Regression Model
Directory of Open Access Journals (Sweden)
Yujuan Sun
2014-01-01
Full Text Available Passenger flow is increasing dramatically with accomplishment of subway network system in big cities of China. As convergence nodes of subway lines, transfer stations need to assume more passengers due to amount transfer demand among different lines. Then, transfer facilities have to face great pressure such as pedestrian congestion or other abnormal situations. In order to avoid pedestrian congestion or warn the management before it occurs, it is very necessary to predict the transfer passenger flow to forecast pedestrian congestions. Thus, based on nonparametric regression theory, a transfer passenger flow prediction model was proposed. In order to test and illustrate the prediction model, data of transfer passenger flow for one month in XIDAN transfer station were used to calibrate and validate the model. By comparing with Kalman filter model and support vector machine regression model, the results show that the nonparametric regression model has the advantages of high accuracy and strong transplant ability and could predict transfer passenger flow accurately for different intervals.
Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne
2012-12-01
In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Semi-parametric regression: Efficiency gains from modeling the nonparametric part
Yu, Kyusang; Park, Byeong U; 10.3150/10-BEJ296
2011-01-01
It is widely admitted that structured nonparametric modeling that circumvents the curse of dimensionality is important in nonparametric estimation. In this paper we show that the same holds for semi-parametric estimation. We argue that estimation of the parametric component of a semi-parametric model can be improved essentially when more structure is put into the nonparametric part of the model. We illustrate this for the partially linear model, and investigate efficiency gains when the nonparametric part of the model has an additive structure. We present the semi-parametric Fisher information bound for estimating the parametric part of the partially linear additive model and provide semi-parametric efficient estimators for which we use a smooth backfitting technique to deal with the additive nonparametric part. We also present the finite sample performances of the proposed estimators and analyze Boston housing data as an illustration.
Quantal Response: Nonparametric Modeling
2017-01-01
spline N−spline Fig. 3 Logistic regression 7 Approved for public release; distribution is unlimited. 5. Nonparametric QR Models Nonparametric linear ...stimulus and probability of response. The Generalized Linear Model approach does not make use of the limit distribution but allows arbitrary functional...7. Conclusions and Recommendations 18 8. References 19 Appendix A. The Linear Model 21 Appendix B. The Generalized Linear Model 33 Appendix C. B
Panel data specifications in nonparametric kernel regression
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
parametric panel data estimators to analyse the production technology of Polish crop farms. The results of our nonparametric kernel regressions generally differ from the estimates of the parametric models but they only slightly depend on the choice of the kernel functions. Based on economic reasoning, we...
Nonparametric instrumental regression with non-convex constraints
Grasmair, M.; Scherzer, O.; Vanhems, A.
2013-03-01
This paper considers the nonparametric regression model with an additive error that is dependent on the explanatory variables. As is common in empirical studies in epidemiology and economics, it also supposes that valid instrumental variables are observed. A classical example in microeconomics considers the consumer demand function as a function of the price of goods and the income, both variables often considered as endogenous. In this framework, the economic theory also imposes shape restrictions on the demand function, such as integrability conditions. Motivated by this illustration in microeconomics, we study an estimator of a nonparametric constrained regression function using instrumental variables by means of Tikhonov regularization. We derive rates of convergence for the regularized model both in a deterministic and stochastic setting under the assumption that the true regression function satisfies a projected source condition including, because of the non-convexity of the imposed constraints, an additional smallness condition.
Asymptotic theory of nonparametric regression estimates with censored data
Institute of Scientific and Technical Information of China (English)
施沛德; 王海燕; 张利华
2000-01-01
For regression analysis, some useful Information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literat黵e, but the optimal rates of global convergence have not been obtained yet. Because of the possible Information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression f unction based on right-censored response data, and proves, under some regularity condi-tions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtai
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
considered. It is shown that adaptive estimation in conditional parametric models can be performed by combining the well known methods of local polynomial regression and recursive least squares with exponential forgetting. The approach used for estimation in conditional parametric models also highlights how....... For this purpose non-parametric methods together with additive models are suggested. Also, a new approach specifically designed to detect non-linearities is introduced. Confidence intervals are constructed by use of bootstrapping. As a link between non-parametric and parametric methods a paper dealing with neural...... the focus is on combinations of parametric and non-parametric methods of regression. This combination can be in terms of additive models where e.g. one or more non-parametric term is added to a linear regression model. It can also be in terms of conditional parametric models where the coefficients...
Nonparametric Regression Estimation for Multivariate Null Recurrent Processes
Directory of Open Access Journals (Sweden)
Biqing Cai
2015-04-01
Full Text Available This paper discusses nonparametric kernel regression with the regressor being a \\(d\\-dimensional \\(\\beta\\-null recurrent process in presence of conditional heteroscedasticity. We show that the mean function estimator is consistent with convergence rate \\(\\sqrt{n(Th^{d}}\\, where \\(n(T\\ is the number of regenerations for a \\(\\beta\\-null recurrent process and the limiting distribution (with proper normalization is normal. Furthermore, we show that the two-step estimator for the volatility function is consistent. The finite sample performance of the estimate is quite reasonable when the leave-one-out cross validation method is used for bandwidth selection. We apply the proposed method to study the relationship of Federal funds rate with 3-month and 5-year T-bill rates and discover the existence of nonlinearity of the relationship. Furthermore, the in-sample and out-of-sample performance of the nonparametric model is far better than the linear model.
Wishart, Justin Rory
2011-01-01
In this paper, a lower bound is determined in the minimax sense for change point estimators of the first derivative of a regression function in the fractional white noise model. Similar minimax results presented previously in the area focus on change points in the derivatives of a regression function in the white noise model or consider estimation of the regression function in the presence of correlated errors.
Nonparametric regression with martingale increment errors
Delattre, Sylvain
2010-01-01
We consider the problem of adaptive estimation of the regression function in a framework where we replace ergodicity assumptions (such as independence or mixing) by another structural assumption on the model. Namely, we propose adaptive upper bounds for kernel estimators with data-driven bandwidth (Lepski's selection rule) in a regression model where the noise is an increment of martingale. It includes, as very particular cases, the usual i.i.d. regression and auto-regressive models. The cornerstone tool for this study is a new result for self-normalized martingales, called ``stability'', which is of independent interest. In a first part, we only use the martingale increment structure of the noise. We give an adaptive upper bound using a random rate, that involves the occupation time near the estimation point. Thanks to this approach, the theoretical study of the statistical procedure is disconnected from usual ergodicity properties like mixing. Then, in a second part, we make a link with the usual minimax th...
Testing for a constant coefficient of variation in nonparametric regression
Dette, Holger; Marchlewski, Mareen; Wagener, Jens
2010-01-01
In the common nonparametric regression model Y_i=m(X_i)+sigma(X_i)epsilon_i we consider the problem of testing the hypothesis that the coefficient of the scale and location function is constant. The test is based on a comparison of the observations Y_i=\\hat{sigma}(X_i) with their mean by a smoothed empirical process, where \\hat{sigma} denotes the local linear estimate of the scale function. We show weak convergence of a centered version of this process to a Gaussian process under the null ...
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Asymptotic theory of nonparametric regression estimates with censored data
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
For regression analysis, some useful information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literature, but the optimal rates of global convergence have not been obtained yet. Because of the possible information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression function based on right_censored response data, and proves, under some regularity conditions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtain the asymptotic optimality of AIC, AICC, GCV, Cp and FPE criteria in the process of selecting the parameters.
Comparing parametric and nonparametric regression methods for panel data
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb-Douglas and......We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs...... rejects both the Cobb-Douglas and the Translog functional form, while a recently developed nonparametric kernel regression method with a fully nonparametric panel data specification delivers plausible results. On average, the nonparametric regression results are similar to results that are obtained from...
Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study
Directory of Open Access Journals (Sweden)
Anestis Antoniadis
2001-06-01
Full Text Available Wavelet analysis has been found to be a powerful tool for the nonparametric estimation of spatially-variable objects. We discuss in detail wavelet methods in nonparametric regression, where the data are modelled as observations of a signal contaminated with additive Gaussian noise, and provide an extensive review of the vast literature of wavelet shrinkage and wavelet thresholding estimators developed to denoise such data. These estimators arise from a wide range of classical and empirical Bayes methods treating either individual or blocks of wavelet coefficients. We compare various estimators in an extensive simulation study on a variety of sample sizes, test functions, signal-to-noise ratios and wavelet filters. Because there is no single criterion that can adequately summarise the behaviour of an estimator, we use various criteria to measure performance in finite sample situations. Insight into the performance of these estimators is obtained from graphical outputs and numerical tables. In order to provide some hints of how these estimators should be used to analyse real data sets, a detailed practical step-by-step illustration of a wavelet denoising analysis on electrical consumption is provided. Matlab codes are provided so that all figures and tables in this paper can be reproduced.
Comparing parametric and nonparametric regression methods for panel data
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We investigate and compare the suitability of parametric and non-parametric stochastic regression methods for analysing production technologies and the optimal firm size. Our theoretical analysis shows that the most commonly used functional forms in empirical production analysis, Cobb......-Douglas and Translog, are unsuitable for analysing the optimal firm size. We show that the Translog functional form implies an implausible linear relationship between the (logarithmic) firm size and the elasticity of scale, where the slope is artificially related to the substitutability between the inputs....... The practical applicability of the parametric and non-parametric regression methods is scrutinised and compared by an empirical example: we analyse the production technology and investigate the optimal size of Polish crop farms based on a firm-level balanced panel data set. A nonparametric specification test...
Coverage Accuracy of Confidence Intervals in Nonparametric Regression
Institute of Scientific and Technical Information of China (English)
Song-xi Chen; Yong-song Qin
2003-01-01
Point-wise confidence intervals for a nonparametric regression function with random design points are considered. The confidence intervals are those based on the traditional normal approximation and the empirical likelihood. Their coverage accuracy is assessed by developing the Edgeworth expansions for the coverage probabilities. It is shown that the empirical likelihood confidence intervals are Bartlett correctable.
Right-Censored Nonparametric Regression: A Comparative Simulation Study
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Dursun Aydın
2016-11-01
Full Text Available This paper introduces the operating of the selection criteria for right-censored nonparametric regression using smoothing spline. In order to transform the response variable into a variable that contains the right-censorship, we used the KaplanMeier weights proposed by [1], and [2]. The major problem in smoothing spline method is to determine a smoothing parameter to obtain nonparametric estimates of the regression function. In this study, the mentioned parameter is chosen based on censored data by means of the criteria such as improved Akaike information criterion (AICc, Bayesian (or Schwarz information criterion (BIC and generalized crossvalidation (GCV. For this purpose, a Monte-Carlo simulation study is carried out to illustrate which selection criterion gives the best estimation for censored data.
Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Tahoces, Pablo G; Lado, María J
2009-01-30
In many biomedical applications, interest lies in being able to distinguish between two possible states of a given response variable, depending on the values of certain continuous predictors. If the number of predictors, p, is high, or if there is redundancy among them, it then becomes important to decide on the selection of the best subset of predictors that will be able to obtain the models with greatest discrimination capacity. With this aim in mind, logistic generalized additive models were considered and receiver operating characteristic (ROC) curves were applied in order to determine and compare the discriminatory capacity of such models. This study sought to develop bootstrap-based tests that allow for the following to be ascertained: (a) the optimal number q < or = p of predictors; and (b) the model or models including q predictors, which display the largest AUC (area under the ROC curve). A simulation study was conducted to verify the behaviour of these tests. Finally, the proposed method was applied to a computer-aided diagnostic system dedicated to early detection of breast cancer. Copyright (c) 2008 John Wiley & Sons, Ltd.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...... function. However, the a priori specification of a functional form involves the risk of choosing one that is not similar to the “true” but unknown relationship between the regressors and the dependent variable. This problem, known as parametric misspecification, can result in biased parameter estimates...... and nonparametric estimations of production functions in order to evaluate the optimal firm size. The second paper discusses the use of parametric and nonparametric regression methods to estimate panel data regression models. The third paper analyses production risk, price uncertainty, and farmers' risk preferences...
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...... for complex networks can be derived and point out relevant literature....
Spline Nonparametric Regression Analysis of Stress-Strain Curve of Confined Concrete
Directory of Open Access Journals (Sweden)
Tavio Tavio
2008-01-01
Full Text Available Due to enormous uncertainties in confinement models associated with the maximum compressive strength and ductility of concrete confined by rectilinear ties, the implementation of spline nonparametric regression analysis is proposed herein as an alternative approach. The statistical evaluation is carried out based on 128 large-scale column specimens of either normal-or high-strength concrete tested under uniaxial compression. The main advantage of this kind of analysis is that it can be applied when the trend of relation between predictor and response variables are not obvious. The error in the analysis can, therefore, be minimized so that it does not depend on the assumption of a particular shape of the curve. This provides higher flexibility in the application. The results of the statistical analysis indicates that the stress-strain curves of confined concrete obtained from the spline nonparametric regression analysis proves to be in good agreement with the experimental curves available in literatures
Institute of Scientific and Technical Information of China (English)
LINGNeng-xiang; DUXue-qiao
2005-01-01
In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.
Institute of Scientific and Technical Information of China (English)
赵文芝; 田铮; 夏志明
2009-01-01
A wavelet method of detection and estimation of change points in nonparametric regression models under random design is proposed.The confidence bound of our test is derived by using the test statistics based on empirical wavelet coefficients as obtained by wavelet transformation of the data which is observed with noise.Moreover,the consistence of the test is proved while the rate of convergence is given.The method turns out to be effective after being tested on simulated examples and applied to IBM stock market data.
López Fontán, J L; Costa, J; Ruso, J M; Prieto, G; Sarmiento, F
2004-02-01
The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found.
Energy Technology Data Exchange (ETDEWEB)
Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)
2004-02-01
The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)
Distributed Nonparametric and Semiparametric Regression on SPARK for Big Data Forecasting
Directory of Open Access Journals (Sweden)
Jelena Fiosina
2017-01-01
Full Text Available Forecasting in big datasets is a common but complicated task, which cannot be executed using the well-known parametric linear regression. However, nonparametric and semiparametric methods, which enable forecasting by building nonlinear data models, are computationally intensive and lack sufficient scalability to cope with big datasets to extract successful results in a reasonable time. We present distributed parallel versions of some nonparametric and semiparametric regression models. We used MapReduce paradigm and describe the algorithms in terms of SPARK data structures to parallelize the calculations. The forecasting accuracy of the proposed algorithms is compared with the linear regression model, which is the only forecasting model currently having parallel distributed realization within the SPARK framework to address big data problems. The advantages of the parallelization of the algorithm are also provided. We validate our models conducting various numerical experiments: evaluating the goodness of fit, analyzing how increasing dataset size influences time consumption, and analyzing time consumption by varying the degree of parallelism (number of workers in the distributed realization.
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Directory of Open Access Journals (Sweden)
Saerom Park
Full Text Available Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
Park, Saerom; Lee, Jaewook; Son, Youngdoo
2016-01-01
Market impact cost is the most significant portion of implicit transaction costs that can reduce the overall transaction cost, although it cannot be measured directly. In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact cost accurately and to provide the predictive model that is versatile in the number of variables. We collected a large amount of real single transaction data of US stock market from Bloomberg Terminal and generated three independent input variables. As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. Although these models encounter certain difficulties in separating the permanent and temporary cost directly, nonparametric machine learning models can be good alternatives in reducing transaction costs by considerably improving in prediction performance.
Bayesian nonparametric duration model with censorship
Directory of Open Access Journals (Sweden)
Joseph Hakizamungu
2007-10-01
Full Text Available This paper is concerned with nonparametric i.i.d. durations models censored observations and we establish by a simple and unified approach the general structure of a bayesian nonparametric estimator for a survival function S. For Dirichlet prior distributions, we describe completely the structure of the posterior distribution of the survival function. These results are essentially supported by prior and posterior independence properties.
Ryu, Duchwan
2010-09-28
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves. © 2010, The International Biometric Society.
Ryu, Duchwan; Li, Erning; Mallick, Bani K
2011-06-01
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.
随机右删失非参数回归模型的影响分析%Influence Analysis of Non-parametric Regression Model with Random Right Censorship
Institute of Scientific and Technical Information of China (English)
王淑玲; 冯予; 刘刚
2012-01-01
In this paper, the primary model is transformed to non-parametric regression model; Then, local influence is discussed and concise influence matrix is obtained; At last, example is given to illustrate our results.%将随机删失非参数固定设计回归模型转化为非参数回归模型进行研究；然后对此模型作了局部影响分析,得到计算影响矩阵及最大影响曲率方向的简洁公式；最后通过实例分析,验证了分析方法的有效性.
A Censored Nonparametric Software Reliability Model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
This paper analyses the effct of censoring on the estimation of failure rate, and presents a framework of a censored nonparametric software reliability model. The model is based on nonparametric testing of failure rate monotonically decreasing and weighted kernel failure rate estimation under the constraint of failure rate monotonically decreasing. Not only does the model have the advantages of little assumptions and weak constraints, but also the residual defects number of the software system can be estimated. The numerical experiment and real data analysis show that the model performs well with censored data.
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major......This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural...... currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis....
Correlated Non-Parametric Latent Feature Models
Doshi-Velez, Finale
2012-01-01
We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.
Nonparametric correlation models for portfolio allocation
DEFF Research Database (Denmark)
Aslanidis, Nektarios; Casas, Isabel
2013-01-01
This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural...... breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major...
LSTA, Rawane Samb
2010-01-01
This thesis deals with the nonparametric estimation of density f of the regression error term E of the model Y=m(X)+E, assuming its independence with the covariate X. The difficulty linked to this study is the fact that the regression error E is not observed. In a such setup, it would be unwise, for estimating f, to use a conditional approach based upon the probability distribution function of Y given X. Indeed, this approach is affected by the curse of dimensionality, so that the resulting estimator of the residual term E would have considerably a slow rate of convergence if the dimension of X is very high. Two approaches are proposed in this thesis to avoid the curse of dimensionality. The first approach uses the estimated residuals, while the second integrates a nonparametric conditional density estimator of Y given X. If proceeding so can circumvent the curse of dimensionality, a challenging issue is to evaluate the impact of the estimated residuals on the final estimator of the density f. We will also at...
Nonparametric Least Squares Estimation of a Multivariate Convex Regression Function
Seijo, Emilio
2010-01-01
This paper deals with the consistency of the least squares estimator of a convex regression function when the predictor is multidimensional. We characterize and discuss the computation of such an estimator via the solution of certain quadratic and linear programs. Mild sufficient conditions for the consistency of this estimator and its subdifferentials in fixed and stochastic design regression settings are provided. We also consider a regression function which is known to be convex and componentwise nonincreasing and discuss the characterization, computation and consistency of its least squares estimator.
A Bayesian nonparametric meta-analysis model.
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G
2015-03-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall effect size, such models may be adequate, but for prediction, they surely are not if the effect-size distribution exhibits non-normal behavior. To address this issue, we propose a Bayesian nonparametric meta-analysis model, which can describe a wider range of effect-size distributions, including unimodal symmetric distributions, as well as skewed and more multimodal distributions. We demonstrate our model through the analysis of real meta-analytic data arising from behavioral-genetic research. We compare the predictive performance of the Bayesian nonparametric model against various conventional and more modern normal fixed-effects and random-effects models.
Multivariate nonparametric regression and visualization with R and applications to finance
Klemelä, Jussi
2014-01-01
A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generatingmechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functio
Directory of Open Access Journals (Sweden)
D. Das
2014-04-01
Full Text Available Climate projections simulated by Global Climate Models (GCM are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often precludes their application towards accurately assessing the effects of climate change on finer regional scale phenomena. Downscaling of climate variables from coarser to finer regional scales using statistical methods are often performed for regional climate projections. Statistical downscaling (SD is based on the understanding that the regional climate is influenced by two factors – the large scale climatic state and the regional or local features. A transfer function approach of SD involves learning a regression model which relates these features (predictors to a climatic variable of interest (predictand based on the past observations. However, often a single regression model is not sufficient to describe complex dynamic relationships between the predictors and predictand. We focus on the covariate selection part of the transfer function approach and propose a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP, for simultaneous clustering and discovery of covariates within the clusters while automatically finding the number of clusters. Sparse linear models are parsimonious and hence relatively more generalizable than non-sparse alternatives, and lends to domain relevant interpretation. Applications to synthetic data demonstrate the value of the new approach and preliminary results related to feature selection for statistical downscaling shows our method can lead to new insights.
The Use of Nonparametric Kernel Regression Methods in Econometric Production Analysis
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard
This PhD thesis addresses one of the fundamental problems in applied econometric analysis, namely the econometric estimation of regression functions. The conventional approach to regression analysis is the parametric approach, which requires the researcher to specify the form of the regression...... to avoid this problem. The main objective is to investigate the applicability of the nonparametric kernel regression method in applied production analysis. The focus of the empirical analyses included in this thesis is the agricultural sector in Poland. Data on Polish farms are used to investigate...... practically and politically relevant problems and to illustrate how nonparametric regression methods can be used in applied microeconomic production analysis both in panel data and cross-section data settings. The thesis consists of four papers. The first paper addresses problems of parametric...
Heteroscedasticity checks for regression models
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.
Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
Measuring the influence of networks on transaction costs using a non-parametric regression technique
DEFF Research Database (Denmark)
Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.
. We empirically analyse the effect of networks on productivity using a cross-validated local linear non-parametric regression technique and a data set of 384 farms in Poland. Our empirical study generally supports our hypothesis that networks affect productivity. Large and dense trading networks...
A nonparametric and diversified portfolio model
Shirazi, Yasaman Izadparast; Sabiruzzaman, Md.; Hamzah, Nor Aishah
2014-07-01
Traditional portfolio models, like mean-variance (MV) suffer from estimation error and lack of diversity. Alternatives, like mean-entropy (ME) or mean-variance-entropy (MVE) portfolio models focus independently on the issue of either a proper risk measure or the diversity. In this paper, we propose an asset allocation model that compromise between risk of historical data and future uncertainty. In the new model, entropy is presented as a nonparametric risk measure as well as an index of diversity. Our empirical evaluation with a variety of performance measures shows that this model has better out-of-sample performances and lower portfolio turnover than its competitors.
Estimation of Subpixel Snow-Covered Area by Nonparametric Regression Splines
Kuter, S.; Akyürek, Z.; Weber, G.-W.
2016-10-01
Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than th
Dai, Wenlin
2017-09-01
Difference-based methods do not require estimating the mean function in nonparametric regression and are therefore popular in practice. In this paper, we propose a unified framework for variance estimation that combines the linear regression method with the higher-order difference estimators systematically. The unified framework has greatly enriched the existing literature on variance estimation that includes most existing estimators as special cases. More importantly, the unified framework has also provided a smart way to solve the challenging difference sequence selection problem that remains a long-standing controversial issue in nonparametric regression for several decades. Using both theory and simulations, we recommend to use the ordinary difference sequence in the unified framework, no matter if the sample size is small or if the signal-to-noise ratio is large. Finally, to cater for the demands of the application, we have developed a unified R package, named VarED, that integrates the existing difference-based estimators and the unified estimators in nonparametric regression and have made it freely available in the R statistical program http://cran.r-project.org/web/packages/.
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access to no...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Parametric or nonparametric? A parametricness index for model selection
Liu, Wei; 10.1214/11-AOS899
2012-01-01
In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike's information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to detect the situation that a specific model selection problem is in. In this work, we differentiate the two scenarios theoretically under some conditions. We develop a measure, parametricness index (PI), to assess whether a model selected by a potentially consistent procedure can be practically treated as the true model, which also hints on AIC or BIC is better suited for the data for the goal of estimating the regression function. A consequence is that by switching between AIC and BIC based on the PI, the resulting regression estimator is si...
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
Subpixel Snow Cover Mapping from MODIS Data by Nonparametric Regression Splines
Akyurek, Z.; Kuter, S.; Weber, G. W.
2016-12-01
Spatial extent of snow cover is often considered as one of the key parameters in climatological, hydrological and ecological modeling due to its energy storage, high reflectance in the visible and NIR regions of the electromagnetic spectrum, significant heat capacity and insulating properties. A significant challenge in snow mapping by remote sensing (RS) is the trade-off between the temporal and spatial resolution of satellite imageries. In order to tackle this issue, machine learning-based subpixel snow mapping methods, like Artificial Neural Networks (ANNs), from low or moderate resolution images have been proposed. Multivariate Adaptive Regression Splines (MARS) is a nonparametric regression tool that can build flexible models for high dimensional and complex nonlinear data. Although MARS is not often employed in RS, it has various successful implementations such as estimation of vertical total electron content in ionosphere, atmospheric correction and classification of satellite images. This study is the first attempt in RS to evaluate the applicability of MARS for subpixel snow cover mapping from MODIS data. Total 16 MODIS-Landsat ETM+ image pairs taken over European Alps between March 2000 and April 2003 were used in the study. MODIS top-of-atmospheric reflectance, NDSI, NDVI and land cover classes were used as predictor variables. Cloud-covered, cloud shadow, water and bad-quality pixels were excluded from further analysis by a spatial mask. MARS models were trained and validated by using reference fractional snow cover (FSC) maps generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also developed. The mutual comparison of obtained MARS and ANN models was accomplished on independent test areas. The MARS model performed better than the ANN model with an average RMSE of 0.1288 over the independent test areas; whereas the average RMSE of the ANN model
Bayesian nonparametric estimation and consistency of mixed multinomial logit choice models
De Blasi, Pierpaolo; Lau, John W; 10.3150/09-BEJ233
2011-01-01
This paper develops nonparametric estimation for discrete choice models based on the mixed multinomial logit (MMNL) model. It has been shown that MMNL models encompass all discrete choice models derived under the assumption of random utility maximization, subject to the identification of an unknown distribution $G$. Noting the mixture model description of the MMNL, we employ a Bayesian nonparametric approach, using nonparametric priors on the unknown mixing distribution $G$, to estimate choice probabilities. We provide an important theoretical support for the use of the proposed methodology by investigating consistency of the posterior distribution for a general nonparametric prior on the mixing distribution. Consistency is defined according to an $L_1$-type distance on the space of choice probabilities and is achieved by extending to a regression model framework a recent approach to strong consistency based on the summability of square roots of prior probabilities. Moving to estimation, slightly different te...
Revisiting the Distance Duality Relation using a non-parametric regression method
Rana, Akshay; Jain, Deepak; Mahajan, Shobhit; Mukherjee, Amitabha
2016-07-01
The interdependence of luminosity distance, DL and angular diameter distance, DA given by the distance duality relation (DDR) is very significant in observational cosmology. It is very closely tied with the temperature-redshift relation of Cosmic Microwave Background (CMB) radiation. Any deviation from η(z)≡ DL/DA (1+z)2 =1 indicates a possible emergence of new physics. Our aim in this work is to check the consistency of these relations using a non-parametric regression method namely, LOESS with SIMEX. This technique avoids dependency on the cosmological model and works with a minimal set of assumptions. Further, to analyze the efficiency of the methodology, we simulate a dataset of 020 points of η (z) data based on a phenomenological model η(z)= (1+z)epsilon. The error on the simulated data points is obtained by using the temperature of CMB radiation at various redshifts. For testing the distance duality relation, we use the JLA SNe Ia data for luminosity distances, while the angular diameter distances are obtained from radio galaxies datasets. Since the DDR is linked with CMB temperature-redshift relation, therefore we also use the CMB temperature data to reconstruct η (z). It is important to note that with CMB data, we are able to study the evolution of DDR upto a very high redshift z = 2.418. In this analysis, we find no evidence of deviation from η=1 within a 1σ region in the entire redshift range used in this analysis (0 < z <= 2.418).
The Infinite Hierarchical Factor Regression Model
Rai, Piyush
2009-01-01
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.
Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
Davenport, Clemontina A; Maity, Arnab; Wu, Yichao
2015-04-01
Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models
Fan, Jianqing; Song, Rui
2011-01-01
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under the nonparametric additive models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, an iterative nonparametric independence screening (INIS) is also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data a...
DPpackage: Bayesian Semi- and Nonparametric Modeling in R
Directory of Open Access Journals (Sweden)
Alejandro Jara
2011-04-01
Full Text Available Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.
Zhu, Feng; Feng, Weiyue; Wang, Huajian; Huang, Shaosen; Lv, Yisong; Chen, Yong
2013-01-01
X-ray spectral imaging provides quantitative imaging of trace elements in biological sample with high sensitivity. We propose a novel algorithm to promote the signal-to-noise ratio (SNR) of X-ray spectral images that have low photon counts. Firstly, we estimate the image data area that belongs to the homogeneous parts through confidence interval testing. Then, we apply the Poisson regression through its maximum likelihood estimation on this area to estimate the true photon counts from the Poisson noise corrupted data. Unlike other denoising methods based on regression analysis, we use the bootstrap resampling methods to ensure the accuracy of regression estimation. Finally, we use a robust local nonparametric regression method to estimate the baseline and subsequently subtract it from the X-ray spectral data to further improve the SNR of the data. Experiments on several real samples show that the proposed method performs better than some state-of-the-art approaches to ensure accuracy and precision for quantit...
Forecasting of Households Consumption Expenditure with Nonparametric Regression: The Case of Turkey
Directory of Open Access Journals (Sweden)
Aydin Noyan
2016-11-01
Full Text Available The relationship between household income and expenditure is important for understanding how the shape of the economic dynamics of the households. In this study, the relationship between household consumption expenditure and household disposable income were analyzed by Locally Weighted Scatterplot Smoothing Regression which is a nonparametric method using R programming. This study aimed to determine relationship between variables directly, unlike making any assumptions are commonly used as in the conventional parametric regression. According to the findings, effect on expenditure with increasing of income and household size together increased rapidly at first, and then speed of increase decreased. This increase can be explained by having greater compulsory consumption expenditure relatively in small households. Besides, expenditure is relatively higher in middle and high income levels according to low income level. However, the change in expenditure is limited in middle and is the most limited in high income levels when household size changes.
Carroll, Raymond J.
2011-03-01
In many applications we can expect that, or are interested to know if, a density function or a regression curve satisfies some specific shape constraints. For example, when the explanatory variable, X, represents the value taken by a treatment or dosage, the conditional mean of the response, Y , is often anticipated to be a monotone function of X. Indeed, if this regression mean is not monotone (in the appropriate direction) then the medical or commercial value of the treatment is likely to be significantly curtailed, at least for values of X that lie beyond the point at which monotonicity fails. In the case of a density, common shape constraints include log-concavity and unimodality. If we can correctly guess the shape of a curve, then nonparametric estimators can be improved by taking this information into account. Addressing such problems requires a method for testing the hypothesis that the curve of interest satisfies a shape constraint, and, if the conclusion of the test is positive, a technique for estimating the curve subject to the constraint. Nonparametric methodology for solving these problems already exists, but only in cases where the covariates are observed precisely. However in many problems, data can only be observed with measurement errors, and the methods employed in the error-free case typically do not carry over to this error context. In this paper we develop a novel approach to hypothesis testing and function estimation under shape constraints, which is valid in the context of measurement errors. Our method is based on tilting an estimator of the density or the regression mean until it satisfies the shape constraint, and we take as our test statistic the distance through which it is tilted. Bootstrap methods are used to calibrate the test. The constrained curve estimators that we develop are also based on tilting, and in that context our work has points of contact with methodology in the error-free case.
Flexible survival regression modelling
DEFF Research Database (Denmark)
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...
Nonparametric Bayesian Modeling for Automated Database Schema Matching
Energy Technology Data Exchange (ETDEWEB)
Ferragut, Erik M [ORNL; Laska, Jason A [ORNL
2015-01-01
The problem of merging databases arises in many government and commercial applications. Schema matching, a common first step, identifies equivalent fields between databases. We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models.
PV power forecast using a nonparametric PV model
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
Forecasting the AC power output of a PV plant accurately is important both for plant owners and electric system operators. Two main categories of PV modeling are available: the parametric and the nonparametric. In this paper, a methodology using a nonparametric PV model is proposed, using as inputs several forecasts of meteorological variables from a Numerical Weather Forecast model, and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quant...
Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.
Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F
2013-04-01
In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.
Unitary Response Regression Models
Lipovetsky, S.
2007-01-01
The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
DEFF Research Database (Denmark)
Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H. C. A.
All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access...... to nonpublic information. Our analysis shows that information networks have an impact on the level of TAC. Many resources that are sacrificed for TAC are inputs that also enter the technical production process. As most production data do not separate between these two usages of inputs, high transaction costs...... are unveiled by reduced productivity. A cross-validated local linear non-parametric regression shows that good information networks increase the productivity of farms. A bootstrapping procedure confirms that this result is statistically significant....
Fast pixel-based optical proximity correction based on nonparametric kernel regression
Ma, Xu; Wu, Bingliang; Song, Zhiyang; Jiang, Shangliang; Li, Yanqiu
2014-10-01
Optical proximity correction (OPC) is a resolution enhancement technique extensively used in the semiconductor industry to improve the resolution and pattern fidelity of optical lithography. In pixel-based OPC (PBOPC), the layout is divided into small pixels, which are then iteratively modified until the simulated print image on the wafer matches the desired pattern. However, the increasing complexity and size of modern integrated circuits make PBOPC techniques quite computationally intensive. This paper focuses on developing a practical and efficient PBOPC algorithm based on a nonparametric kernel regression, a well-known technique in machine learning. Specifically, we estimate the OPC patterns based on the geometric characteristics of the original layout corresponding to the same region and a series of training examples. Experimental results on metal layers show that our proposed approach significantly improves the speed of a current professional PBOPC software by a factor of 2 to 3, and may further reduce the mask complexity.
Institute of Scientific and Technical Information of China (English)
赵文芝; 夏志明; 贺飞跃
2016-01-01
The two-step estimators for change point in nonparametric regression are proposed.In the first step,an initial estimator is obtained by local linear smoothing method.In the second step,the fi-nal estimator is obtained by CUSUM method on a closed neighborhood of initial estimator.It is found through a simulation study that the proposed estimator is efficient.The estimator for j ump size is also obtained.Further more,experimental results that using historical data on Nile river discharges,ex-change rate data of USD against RMB and global temperature data for the northern hemisphere show that the proposed method is also practical in applications.%针对非参数回归模型变点问题，给出了变点的两步估计方法。第一步，用局部线性方法给出变点的初始估计量；第二步，在初始估计量的邻域内，用 CUSUM方法给出变点的最终估计量，同时获得了变点跃度的估计量。蒙特卡罗随机模拟结果表明了此方法的有效性。最后以尼罗河流量数据，美元兑换人民币汇率数据以及北半球月平均气温数据为例进行分析，结果说明此方法有实际应用价值。
Heteroscedasticity checks for regression models
Institute of Scientific and Technical Information of China (English)
ZHU; Lixing
2001-01-01
［1］Carroll, R. J., Ruppert, D., Transformation and Weighting in Regression, New York: Chapman and Hall, 1988.［2］Cook, R. D., Weisberg, S., Diagnostics for heteroscedasticity in regression, Biometrika, 1988, 70: 1—10.［3］Davidian, M., Carroll, R. J., Variance function estimation, J. Amer. Statist. Assoc., 1987, 82: 1079—1091.［4］Bickel, P., Using residuals robustly I: Tests for heteroscedasticity, Ann. Statist., 1978, 6: 266—291.［5］Carroll, R. J., Ruppert, D., On robust tests for heteroscedasticity, Ann. Statist., 1981, 9: 205—209.［6］Eubank, R. L., Thomas, W., Detecting heteroscedasticity in nonparametric regression, J. Roy. Statist. Soc., Ser. B, 1993, 55: 145—155.［7］Diblasi, A., Bowman, A., Testing for constant variance in a linear model, Statist. and Probab. Letters, 1997, 33: 95—103.［8］Dette, H., Munk, A., Testing heteoscedasticity in nonparametric regression, J. R. Statist. Soc. B, 1998, 60: 693—708.［9］Müller, H. G., Zhao, P. L., On a semi-parametric variance function model and a test for heteroscedasticity, Ann. Statist., 1995, 23: 946—967.［10］Stute, W., Manteiga, G., Quindimil, M. P., Bootstrap approximations in model checks for regression, J. Amer. Statist. Asso., 1998, 93: 141—149.［11］Stute, W., Thies, G., Zhu, L. X., Model checks for regression: An innovation approach, Ann. Statist., 1998, 26: 1916—1939.［12］Shorack, G. R., Wellner, J. A., Empirical Processes with Applications to Statistics, New York: Wiley, 1986.［13］Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Statist., 1979, 7: 1—26.［14］Wu, C. F. J., Jackknife, bootstrap and other re-sampling methods in regression analysis, Ann. Statist., 1986, 14: 1261—1295.［15］H rdle, W., Mammen, E., Comparing non-parametric versus parametric regression fits, Ann. Statist., 1993, 21: 1926—1947.［16］Liu, R. Y., Bootstrap procedures under some non-i.i.d. models, Ann. Statist., 1988, 16: 1696—1708.［17
A Level Set Analysis and A Nonparametric Regression on S&P 500 Daily Return
Directory of Open Access Journals (Sweden)
Yipeng Yang
2016-02-01
Full Text Available In this paper, a level set analysis is proposed which aims to analyze the S&P 500 return with a certain magnitude. It is found that the process of large jumps/drops of return tend to have negative serial correlation, and volatility clustering phenomenon can be easily seen. Then, a nonparametric analysis is performed and new patterns are discovered. An ARCH model is constructed based on the patterns we discovered and it is capable of manifesting the volatility skew in option pricing. A comparison of our model with the GARCH(1,1 model is carried out. The explanation of the validity on our model through prospect theory is provided, and, as a novelty, we linked the volatility skew phenomenon to the prospect theory in behavioral finance.
Nonparametric Bayes modeling for case control studies with many predictors.
Zhou, Jing; Herring, Amy H; Bhattacharya, Anirban; Olshan, Andrew F; Dunson, David B
2016-03-01
It is common in biomedical research to run case-control studies involving high-dimensional predictors, with the main goal being detection of the sparse subset of predictors having a significant association with disease. Usual analyses rely on independent screening, considering each predictor one at a time, or in some cases on logistic regression assuming no interactions. We propose a fundamentally different approach based on a nonparametric Bayesian low rank tensor factorization model for the retrospective likelihood. Our model allows a very flexible structure in characterizing the distribution of multivariate variables as unknown and without any linear assumptions as in logistic regression. Predictors are excluded only if they have no impact on disease risk, either directly or through interactions with other predictors. Hence, we obtain an omnibus approach for screening for important predictors. Computation relies on an efficient Gibbs sampler. The methods are shown to have high power and low false discovery rates in simulation studies, and we consider an application to an epidemiology study of birth defects.
2017-01-01
Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result. PMID:28133490
Directory of Open Access Journals (Sweden)
Yue Fan
2017-01-01
Full Text Available Gene regulatory networks (GRNs play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.
Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects
Qian, Minghui; Hu, Ridong; Chen, Jianwei
2016-01-01
Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…
A non-parametric model for the cosmic velocity field
Branchini, E; Teodoro, L; Frenk, CS; Schmoldt, [No Value; Efstathiou, G; White, SDM; Saunders, W; Sutherland, W; Rowan-Robinson, M; Keeble, O; Tadros, H; Maddox, S; Oliver, S
1999-01-01
We present a self-consistent non-parametric model of the local cosmic velocity field derived from the distribution of IRAS galaxies in the PSCz redshift survey. The survey has been analysed using two independent methods, both based on the assumptions of gravitational instability and linear biasing.
Economic decision making and the application of nonparametric prediction models
Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.
2008-01-01
Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.
Using a nonparametric PV model to forecast AC power output of PV plants
Almeida, Marcelo Pinho; Perpiñan Lamigueiro, Oscar; Narvarte Fernández, Luis
2015-01-01
In this paper, a methodology using a nonparametric model is used to forecast AC power output of PV plants using as inputs several forecasts of meteorological variables from a Numerical Weather Prediction (NWP) model and actual AC power measurements of PV plants. The methodology was built upon the R environment and uses Quantile Regression Forests as machine learning tool to forecast the AC power with a confidence interval. Real data from five PV plants was used to validate the methodology, an...
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models.
Fan, Jianqing; Feng, Yang; Song, Rui
2011-06-01
A variable screening procedure via correlation learning was proposed in Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening is called NIS, a specific member of the sure independence screening. Several closely related variable screening procedures are proposed. Under general nonparametric models, it is shown that under some mild technical conditions, the proposed independence screening methods enjoy a sure screening property. The extent to which the dimensionality can be reduced by independence screening is also explicitly quantified. As a methodological extension, a data-driven thresholding and an iterative nonparametric independence screening (INIS) are also proposed to enhance the finite sample performance for fitting sparse additive models. The simulation results and a real data analysis demonstrate that the proposed procedure works well with moderate sample size and large dimension and performs better than competing methods.
A Bayesian Nonparametric Meta-Analysis Model
Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.
2015-01-01
In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…
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......A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Non-Parametric Model Drift Detection
2016-07-01
Analysis Division Information Directorate This report is published in the interest of scientific and technical...took place on datasets made up of text documents. The difference between datasets used to estimate potential error (drop in accuracy) that the model...Assistant, Extraction of executable rules from regulatory text 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 19a
TWO REGRESSION CREDIBILITY MODELS
Directory of Open Access Journals (Sweden)
Constanţa-Nicoleta BODEA
2010-03-01
Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter θ. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. To illustrate the solution with the properties mentioned above, we shall need the well-known representation theorem for a special class of matrices, the properties of the trace for a square matrix, the scalar product of two vectors, the norm with respect to a positive definite matrix given in advance and the complicated mathematical properties of conditional expectations and of conditional covariances.
Parametric and Non-Parametric System Modelling
DEFF Research Database (Denmark)
Nielsen, Henrik Aalborg
1999-01-01
other aspects, the properties of a method for parameter estimation in stochastic differential equations is considered within the field of heat dynamics of buildings. In the second paper a lack-of-fit test for stochastic differential equations is presented. The test can be applied to both linear and non-linear...... networks is included. In this paper, neural networks are used for predicting the electricity production of a wind farm. The results are compared with results obtained using an adaptively estimated ARX-model. Finally, two papers on stochastic differential equations are included. In the first paper, among...... stochastic differential equations. Some applications are presented in the papers. In the summary report references are made to a number of other applications. Resumé på dansk: Nærværende afhandling består af ti artikler publiceret i perioden 1996-1999 samt et sammendrag og en perspektivering heraf. I...
Directory of Open Access Journals (Sweden)
Mustafa Koroglu
2016-02-01
Full Text Available This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown functional coefficients by a local linear regression approach. Some Monte Carlo simulation results are reported to assess the finite sample performance of our proposed estimators. We then apply the proposed model to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries’ openness to trade, respectively.
非参数判别模型%Nonparametric discriminant model
Institute of Scientific and Technical Information of China (English)
谢斌锋; 梁飞豹
2011-01-01
提出了一类新的判别分析方法,主要思想是将非参数回归模型推广到判别分析中,形成相应的非参数判别模型.通过实例与传统判别法相比较,表明非参数判别法具有更广泛的适用性和较高的回代正确率.%In this paper, the author puts forth a new class of discriminant method, which the main idea is applied non- parametric regression model to discriminant analysis and forms the corresponding nonparametric discriminant model. Compared with the traditional discriminant methods by citing an example, the nonparametric discriminant method has more comprehensive adaptability and higher correct rate of back subsitution.
Du, Li; Turner, Jay
2015-10-01
A long term air quality study is being conducted in Roxana, Illinois, USA, at the fenceline of a petroleum refinery. Measurements include 1-in-6 day 24-hour integrated ambient fine particulate matter (PM2.5) speciation following the Chemical Speciation Network (CSN) sampling and analysis protocols. Lanthanoid elements, some of which are tracers of fluidized-bed catalytic cracker (FCC) emissions, are also measured by inductively coupled plasma-mass spectrometry (ICP-MS) after extraction from PM2.5 using hot block-assisted acid digestion. Lanthanoid recoveries of 80-90% were obtained for two ambient particulate matter standard reference materials (NIST SRM 1648a and 2783). Ambient PM2.5 La patterns could be explained by a two-source model representing resuspended soil and FCC emissions with enhanced La/Ce ratios when impacted by the refinery. Nonparametric wind regression demonstrates that when the monitoring station was upwind of the refinery the mean La/Ce ratio is consistent with soil and when the monitoring station is downwind of the refinery the mean ratio is more than four times higher for bearings that corresponds to maximum impacts. Source apportionment modeling using EPA UNMIX and EPA PMF could not reliably apportion PM2.5 mass to the FCC emissions. However, the weight of evidence is that such contributions are small with no large episodes observed for the 164 samples analyzed. This study demonstrates the applicability of a hot block-assisted digestion protocol for the extraction of lanthanoid elements as well as insights obtained from long-term monitoring data including wind direction-based analyses.
Stochastic Earthquake Rupture Modeling Using Nonparametric Co-Regionalization
Lee, Kyungbook; Song, Seok Goo
2016-10-01
Accurate predictions of the intensity and variability of ground motions are essential in simulation-based seismic hazard assessment. Advanced simulation-based ground motion prediction methods have been proposed to complement the empirical approach, which suffers from the lack of observed ground motion data, especially in the near-source region for large events. It is important to quantify the variability of the earthquake rupture process for future events and to produce a number of rupture scenario models to capture the variability in simulation-based ground motion predictions. In this study, we improved the previously developed stochastic earthquake rupture modeling method by applying the nonparametric co-regionalization, which was proposed in geostatistics, to the correlation models estimated from dynamically derived earthquake rupture models. The nonparametric approach adopted in this study is computationally efficient and, therefore, enables us to simulate numerous rupture scenarios, including large events (M > 7.0). It also gives us an opportunity to check the shape of true input correlation models in stochastic modeling after being deformed for permissibility. We expect that this type of modeling will improve our ability to simulate a wide range of rupture scenario models and thereby predict ground motions and perform seismic hazard assessment more accurately.
Montiel, Ariadna; Sendra, Irene; Escamilla-Rivera, Celia; Salzano, Vincenzo
2014-01-01
In this work we present a nonparametric approach, which works on minimal assumptions, to reconstruct the cosmic expansion of the Universe. We propose to combine a locally weighted scatterplot smoothing method and a simulation-extrapolation method. The first one (Loess) is a nonparametric approach that allows to obtain smoothed curves with no prior knowledge of the functional relationship between variables nor of the cosmological quantities. The second one (Simex) takes into account the effect of measurement errors on a variable via a simulation process. For the reconstructions we use as raw data the Union2.1 Type Ia Supernovae compilation, as well as recent Hubble parameter measurements. This work aims to illustrate the approach, which turns out to be a self-sufficient technique in the sense we do not have to choose anything by hand. We examine the details of the method, among them the amount of observational data needed to perform the locally weighted fit which will define the robustness of our reconstructio...
a Multivariate Downscaling Model for Nonparametric Simulation of Daily Flows
Molina, J. M.; Ramirez, J. A.; Raff, D. A.
2011-12-01
A multivariate, stochastic nonparametric framework for stepwise disaggregation of seasonal runoff volumes to daily streamflow is presented. The downscaling process is conditional on volumes of spring runoff and large-scale ocean-atmosphere teleconnections and includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. The non-parametric and assumption-free character of the framework allows consideration of the random nature and nonlinearities of daily flows, which parametric models are unable to account for adequately. This paper examines statistical links between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a periodicity analysis of climate patterns to detect coherences of their cyclic behavior in the frequency domain. We explore the use of such relationships and selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices, NPGO, SOI and PDO, respectively) in the proposed data-driven framework by means of a combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. In addition, we present a new and simple algorithm, based on nonparametric resampling, that overcomes the common limitation of lack of preservation of historical correlation between daily flows across months. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and produces synthetic series that are statistically indistinguishable from the observations. We present evidence showing that both
Change-point estimation for censored regression model
Institute of Scientific and Technical Information of China (English)
Zhan-feng WANG; Yao-hua WU; Lin-cheng ZHAO
2007-01-01
In this paper, we consider the change-point estimation in the censored regression model assuming that there exists one change point. A nonparametric estimate of the change-point is proposed and is shown to be strongly consistent. Furthermore, its convergence rate is also obtained.
Generative Temporal Modelling of Neuroimaging - Decomposition and Nonparametric Testing
DEFF Research Database (Denmark)
Hald, Ditte Høvenhoff
The goal of this thesis is to explore two improvements for functional magnetic resonance imaging (fMRI) analysis; namely our proposed decomposition method and an extension to the non-parametric testing framework. Analysis of fMRI allows researchers to investigate the functional processes...... of the brain, and provides insight into neuronal coupling during mental processes or tasks. The decomposition method is a Gaussian process-based independent components analysis (GPICA), which incorporates a temporal dependency in the sources. A hierarchical model specification is used, featuring both...
Bayesian nonparametric centered random effects models with variable selection.
Yang, Mingan
2013-03-01
In a linear mixed effects model, it is common practice to assume that the random effects follow a parametric distribution such as a normal distribution with mean zero. However, in the case of variable selection, substantial violation of the normality assumption can potentially impact the subset selection and result in poor interpretation and even incorrect results. In nonparametric random effects models, the random effects generally have a nonzero mean, which causes an identifiability problem for the fixed effects that are paired with the random effects. In this article, we focus on a Bayesian method for variable selection. We characterize the subject-specific random effects nonparametrically with a Dirichlet process and resolve the bias simultaneously. In particular, we propose flexible modeling of the conditional distribution of the random effects with changes across the predictor space. The approach is implemented using a stochastic search Gibbs sampler to identify subsets of fixed effects and random effects to be included in the model. Simulations are provided to evaluate and compare the performance of our approach to the existing ones. We then apply the new approach to a real data example, cross-country and interlaboratory rodent uterotrophic bioassay.
Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.
Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z
2017-03-01
A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.
ASYMPTOTIC EFFICIENT ESTIMATION IN SEMIPARAMETRIC NONLINEAR REGRESSION MODELS
Institute of Scientific and Technical Information of China (English)
ZhuZhongyi; WeiBocheng
1999-01-01
In this paper, the estimation method based on the “generalized profile likelihood” for the conditionally parametric models in the paper given by Severini and Wong (1992) is extendedto fixed design semiparametrie nonlinear regression models. For these semiparametrie nonlinear regression models,the resulting estimator of parametric component of the model is shown to beasymptotically efficient and the strong convergence rate of nonparametric component is investigated. Many results (for example Chen (1988) ,Gao & Zhao (1993), Rice (1986) et al. ) are extended to fixed design semiparametric nonlinear regression models.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P
2016-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models, and then infer their parameters from data. When the desired structure is composed of modules or "communities", a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: 1. Deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, that not only remove limitations that seriously degrade the inference on large networks, but also reveal s...
Song, Dong; Wang, Zhuo; Marmarelis, Vasilis Z; Berger, Theodore W
2009-02-01
This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson-Volterra models are obtained herein from broadband experimental input-output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input-output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.
Sumantari, Y. D.; Slamet, I.; Sugiyanto
2017-06-01
Semiparametric regression is a statistical analysis method that consists of parametric and nonparametric regression. There are various approach techniques in nonparametric regression. One of the approach techniques is spline. Central Java is one of the most densely populated province in Indonesia. Population density in this province can be modeled by semiparametric regression because it consists of parametric and nonparametric component. Therefore, the purpose of this paper is to determine the factors that in uence population density in Central Java using the semiparametric spline regression model. The result shows that the factors which in uence population density in Central Java is Family Planning (FP) active participants and district minimum wage.
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.
Bayesian nonparametric meta-analysis using Polya tree mixture models.
Branscum, Adam J; Hanson, Timothy E
2008-09-01
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
Bayesian nonparametric clustering in phylogenetics: modeling antigenic evolution in influenza.
Cybis, Gabriela B; Sinsheimer, Janet S; Bedford, Trevor; Rambaut, Andrew; Lemey, Philippe; Suchard, Marc A
2017-01-18
Influenza is responsible for up to 500,000 deaths every year, and antigenic variability represents much of its epidemiological burden. To visualize antigenic differences across many viral strains, antigenic cartography methods use multidimensional scaling on binding assay data to map influenza antigenicity onto a low-dimensional space. Analysis of such assay data ideally leads to natural clustering of influenza strains of similar antigenicity that correlate with sequence evolution. To understand the dynamics of these antigenic groups, we present a framework that jointly models genetic and antigenic evolution by combining multidimensional scaling of binding assay data, Bayesian phylogenetic machinery and nonparametric clustering methods. We propose a phylogenetic Chinese restaurant process that extends the current process to incorporate the phylogenetic dependency structure between strains in the modeling of antigenic clusters. With this method, we are able to use the genetic information to better understand the evolution of antigenicity throughout epidemics, as shown in applications of this model to H1N1 influenza. Copyright © 2017 John Wiley & Sons, Ltd.
Zhao, Zhibiao
2011-06-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.
Nonparametric Bayesian inference of the microcanonical stochastic block model
Peixoto, Tiago P.
2017-01-01
A principled approach to characterize the hidden modular structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization. We focus on a microcanonical variant of the SBM, where the structure is imposed via hard constraints, i.e., the generated networks are not allowed to violate the patterns imposed by the model. We show how this simple model variation allows simultaneously for two important improvements over more traditional inference approaches: (1) deeper Bayesian hierarchies, with noninformative priors replaced by sequences of priors and hyperpriors, which not only remove limitations that seriously degrade the inference on large networks but also reveal structures at multiple scales; (2) a very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges but also with an unlimited number of modules. We show also how this approach can be used to sample modular hierarchies from the posterior distribution, as well as to perform model selection. We discuss and analyze the differences between sampling from the posterior and simply finding the single parameter estimate that maximizes it. Furthermore, we expose a direct equivalence between our microcanonical approach and alternative derivations based on the canonical SBM.
Robust Medical Test Evaluation Using Flexible Bayesian Semiparametric Regression Models
Directory of Open Access Journals (Sweden)
Adam J. Branscum
2013-01-01
Full Text Available The application of Bayesian methods is increasing in modern epidemiology. Although parametric Bayesian analysis has penetrated the population health sciences, flexible nonparametric Bayesian methods have received less attention. A goal in nonparametric Bayesian analysis is to estimate unknown functions (e.g., density or distribution functions rather than scalar parameters (e.g., means or proportions. For instance, ROC curves are obtained from the distribution functions corresponding to continuous biomarker data taken from healthy and diseased populations. Standard parametric approaches to Bayesian analysis involve distributions with a small number of parameters, where the prior specification is relatively straight forward. In the nonparametric Bayesian case, the prior is placed on an infinite dimensional space of all distributions, which requires special methods. A popular approach to nonparametric Bayesian analysis that involves Polya tree prior distributions is described. We provide example code to illustrate how models that contain Polya tree priors can be fit using SAS software. The methods are used to evaluate the covariate-specific accuracy of the biomarker, soluble epidermal growth factor receptor, for discerning lung cancer cases from controls using a flexible ROC regression modeling framework. The application highlights the usefulness of flexible models over a standard parametric method for estimating ROC curves.
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Modified Regression Correlation Coefficient for Poisson Regression Model
Kaengthong, Nattacha; Domthong, Uthumporn
2017-09-01
This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).
Ridge Regression for Interactive Models.
Tate, Richard L.
1988-01-01
An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are favorable to…
Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts
AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.
2014-12-01
Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using
Inferential Models for Linear Regression
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Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
A generalized additive regression model for survival times
DEFF Research Database (Denmark)
Scheike, Thomas H.
2001-01-01
Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models......Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models...
A generalized additive regression model for survival times
DEFF Research Database (Denmark)
Scheike, Thomas H.
2001-01-01
Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models......Additive Aalen model; counting process; disability model; illness-death model; generalized additive models; multiple time-scales; non-parametric estimation; survival data; varying-coefficient models...
Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…
Directory of Open Access Journals (Sweden)
Rabia Ece OMAY
2013-06-01
Full Text Available In this study, relationship between gross domestic product (GDP per capita and sulfur dioxide (SO2 and particulate matter (PM10 per capita is modeled for Turkey. Nonparametric fixed effect panel data analysis is used for the modeling. The panel data covers 12 territories, in first level of Nomenclature of Territorial Units for Statistics (NUTS, for period of 1990-2001. Modeling of the relationship between GDP and SO2 and PM10 for Turkey, the non-parametric models have given good results.
Granato, Gregory E.
2006-01-01
The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and
Non-parametric Bayesian graph models reveal community structure in resting state fMRI
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Madsen, Kristoffer H.; Siebner, Hartwig Roman
2014-01-01
Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian...
An Assessment of the Nonparametric Approach for Evaluating the Fit of Item Response Models
Liang, Tie; Wells, Craig S.; Hambleton, Ronald K.
2014-01-01
As item response theory has been more widely applied, investigating the fit of a parametric model becomes an important part of the measurement process. There is a lack of promising solutions to the detection of model misfit in IRT. Douglas and Cohen introduced a general nonparametric approach, RISE (Root Integrated Squared Error), for detecting…
Korany, Mohamed A; Maher, Hadir M; Galal, Shereen M; Fahmy, Ossama T; Ragab, Marwa A A
2010-11-15
This manuscript discusses the application of chemometrics to the handling of HPLC response data using the internal standard method (ISM). This was performed on a model mixture containing terbutaline sulphate, guaiphenesin, bromhexine HCl, sodium benzoate and propylparaben as an internal standard. Derivative treatment of chromatographic response data of analyte and internal standard was followed by convolution of the resulting derivative curves using 8-points sin x(i) polynomials (discrete Fourier functions). The response of each analyte signal, its corresponding derivative and convoluted derivative data were divided by that of the internal standard to obtain the corresponding ratio data. This was found beneficial in eliminating different types of interferences. It was successfully applied to handle some of the most common chromatographic problems and non-ideal conditions, namely: overlapping chromatographic peaks and very low analyte concentrations. For example, a significant change in the correlation coefficient of sodium benzoate, in case of overlapping peaks, went from 0.9975 to 0.9998 on applying normal conventional peak area and first derivative under Fourier functions methods, respectively. Also a significant improvement in the precision and accuracy for the determination of synthetic mixtures and dosage forms in non-ideal cases was achieved. For example, in the case of overlapping peaks guaiphenesin mean recovery% and RSD% went from 91.57, 9.83 to 100.04, 0.78 on applying normal conventional peak area and first derivative under Fourier functions methods, respectively. This work also compares the application of Theil's method, a non-parametric regression method, in handling the response ratio data, with the least squares parametric regression method, which is considered the de facto standard method used for regression. Theil's method was found to be superior to the method of least squares as it assumes that errors could occur in both x- and y-directions and
Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling
Knowles, David
2010-01-01
A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indian Buffet Process (IBP) is used as a prior on G to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated datasets based on a known sparse connectivity matrix for E. Coli, and on three biological datasets of increasing complexity.
Nonparametric Independence Screening in Sparse Ultra-High Dimensional Varying Coefficient Models.
Fan, Jianqing; Ma, Yunbei; Dai, Wei
2014-01-01
The varying-coefficient model is an important class of nonparametric statistical model that allows us to examine how the effects of covariates vary with exposure variables. When the number of covariates is large, the issue of variable selection arises. In this paper, we propose and investigate marginal nonparametric screening methods to screen variables in sparse ultra-high dimensional varying-coefficient models. The proposed nonparametric independence screening (NIS) selects variables by ranking a measure of the nonparametric marginal contributions of each covariate given the exposure variable. The sure independent screening property is established under some mild technical conditions when the dimensionality is of nonpolynomial order, and the dimensionality reduction of NIS is quantified. To enhance the practical utility and finite sample performance, two data-driven iterative NIS methods are proposed for selecting thresholding parameters and variables: conditional permutation and greedy methods, resulting in Conditional-INIS and Greedy-INIS. The effectiveness and flexibility of the proposed methods are further illustrated by simulation studies and real data applications.
Shi, J Q; Wang, B; Will, E J; West, R M
2012-11-20
We propose a new semiparametric model for functional regression analysis, combining a parametric mixed-effects model with a nonparametric Gaussian process regression model, namely a mixed-effects Gaussian process functional regression model. The parametric component can provide explanatory information between the response and the covariates, whereas the nonparametric component can add nonlinearity. We can model the mean and covariance structures simultaneously, combining the information borrowed from other subjects with the information collected from each individual subject. We apply the model to dose-response curves that describe changes in the responses of subjects for differing levels of the dose of a drug or agent and have a wide application in many areas. We illustrate the method for the management of renal anaemia. An individual dose-response curve is improved when more information is included by this mechanism from the subject/patient over time, enabling a patient-specific treatment regime.
Model and Variable Selection Procedures for Semiparametric Time Series Regression
Directory of Open Access Journals (Sweden)
Risa Kato
2009-01-01
Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.
Nonparametric Econometrics: The np Package
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Tristen Hayﬁeld
2008-07-01
Full Text Available We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of signiﬁcance and consistent model speciﬁcation tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.
Nonparametric modeling of dynamic functional connectivity in fmri data
DEFF Research Database (Denmark)
Nielsen, Søren Føns Vind; Madsen, Kristoffer H.; Røge, Rasmus
2015-01-01
in Bayesian statistical modeling we use the predictive likelihood to investigate if the model can discriminate between a motor task and rest both within and across subjects. We further investigate what drives dynamic states using the model on the entire data collated across subjects and task/rest. We find...
A Bayesian non-parametric Potts model with application to pre-surgical FMRI data.
Johnson, Timothy D; Liu, Zhuqing; Bartsch, Andreas J; Nichols, Thomas E
2013-08-01
The Potts model has enjoyed much success as a prior model for image segmentation. Given the individual classes in the model, the data are typically modeled as Gaussian random variates or as random variates from some other parametric distribution. In this article, we present a non-parametric Potts model and apply it to a functional magnetic resonance imaging study for the pre-surgical assessment of peritumoral brain activation. In our model, we assume that the Z-score image from a patient can be segmented into activated, deactivated, and null classes, or states. Conditional on the class, or state, the Z-scores are assumed to come from some generic distribution which we model non-parametrically using a mixture of Dirichlet process priors within the Bayesian framework. The posterior distribution of the model parameters is estimated with a Markov chain Monte Carlo algorithm, and Bayesian decision theory is used to make the final classifications. Our Potts prior model includes two parameters, the standard spatial regularization parameter and a parameter that can be interpreted as the a priori probability that each voxel belongs to the null, or background state, conditional on the lack of spatial regularization. We assume that both of these parameters are unknown, and jointly estimate them along with other model parameters. We show through simulation studies that our model performs on par, in terms of posterior expected loss, with parametric Potts models when the parametric model is correctly specified and outperforms parametric models when the parametric model in misspecified.
Yerlikaya-Özkurt, Fatma; Askan, Aysegul; Weber, Gerhard-Wilhelm
2014-12-01
Ground Motion Prediction Equations (GMPEs) are empirical relationships which are used for determining the peak ground response at a particular distance from an earthquake source. They relate the peak ground responses as a function of earthquake source type, distance from the source, local site conditions where the data are recorded and finally the depth and magnitude of the earthquake. In this article, a new prediction algorithm, called Conic Multivariate Adaptive Regression Splines (CMARS), is employed on an available dataset for deriving a new GMPE. CMARS is based on a special continuous optimization technique, conic quadratic programming. These convex optimization problems are very well-structured, resembling linear programs and, hence, permitting the use of interior point methods. The CMARS method is performed on the strong ground motion database of Turkey. Results are compared with three other GMPEs. CMARS is found to be effective for ground motion prediction purposes.
Semiparametric Regression and Model Refining
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
This paper presents a semiparametric adjustment method suitable for general cases.Assuming that the regularizer matrix is positive definite,the calculation method is discussed and the corresponding formulae are presented.Finally,a simulated adjustment problem is constructed to explain the method given in this paper.The results from the semiparametric model and G-M model are compared.The results demonstrate that the model errors or the systematic errors of the observations can be detected correctly with the semiparametric estimate method.
Regression modeling of ground-water flow
Cooley, R.L.; Naff, R.L.
1985-01-01
Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)
[From clinical judgment to linear regression model.
Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O
2013-01-01
When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R(2)) indicates the importance of independent variables in the outcome.
Institute of Scientific and Technical Information of China (English)
2009-01-01
In this paper, we study the local asymptotic behavior of the regression spline estimator in the framework of marginal semiparametric model. Similarly to Zhu, Fung and He (2008), we give explicit expression for the asymptotic bias of regression spline estimator for nonparametric function f. Our results also show that the asymptotic bias of the regression spline estimator does not depend on the working covariance matrix, which distinguishes the regression splines from the smoothing splines and the seemingly unrelated kernel. To understand the local bias result of the regression spline estimator, we show that the regression spline estimator can be obtained iteratively by applying the standard weighted least squares regression spline estimator to pseudo-observations. At each iteration, the bias of the estimator is unchanged and only the variance is updated.
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation.
Freiman, M; Kronman, A; Esses, S J; Joskowicz, L; Sosna, J
2010-01-01
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.
Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model
Directory of Open Access Journals (Sweden)
Isaac Mugume
2016-01-01
Full Text Available Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE, the mean absolute error (MAE, mean error (ME, skewness, and the bias easy estimate (BES and nonparametric (the sign test, STM methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.
Regression Model With Elliptically Contoured Errors
Arashi, M; Tabatabaey, S M M
2012-01-01
For the regression model where the errors follow the elliptically contoured distribution (ECD), we consider the least squares (LS), restricted LS (RLS), preliminary test (PT), Stein-type shrinkage (S) and positive-rule shrinkage (PRS) estimators for the regression parameters. We compare the quadratic risks of the estimators to determine the relative dominance properties of the five estimators.
Efficient Quantile Estimation for Functional-Coefficient Partially Linear Regression Models
Institute of Scientific and Technical Information of China (English)
Zhangong ZHOU; Rong JIANG; Weimin QIAN
2011-01-01
The quantile estimation methods are proposed for functional-coefficient partially linear regression (FCPLR) model by combining nonparametric and functional-coefficient regression (FCR) model.The local linear scheme and the integrated method are used to obtain local quantile estimators of all unknown functions in the FCPLR model.These resulting estimators are asymptotically normal,but each of them has big variance.To reduce variances of these quantile estimators,the one-step backfitting technique is used to obtain the efficient quantile estimators of all unknown functions,and their asymptotic normalities are derived.Two simulated examples are carried out to illustrate the proposed estimation methodology.
Galindo-Garre, Francisca; Hidalgo, María Dolores; Guilera, Georgina; Pino, Oscar; Rojo, J Emilio; Gómez-Benito, Juana
2015-03-01
The World Health Organization Disability Assessment Schedule II (WHO-DAS II) is a multidimensional instrument developed for measuring disability. It comprises six domains (getting around, self-care, getting along with others, life activities and participation in society). The main purpose of this paper is the evaluation of the psychometric properties for each domain of the WHO-DAS II with parametric and non-parametric Item Response Theory (IRT) models. A secondary objective is to assess whether the WHO-DAS II items within each domain form a hierarchy of invariantly ordered severity indicators of disability. A sample of 352 patients with a schizophrenia spectrum disorder is used in this study. The 36 items WHO-DAS II was administered during the consultation. Partial Credit and Mokken scale models are used to study the psychometric properties of the questionnaire. The psychometric properties of the WHO-DAS II scale are satisfactory for all the domains. However, we identify a few items that do not discriminate satisfactorily between different levels of disability and cannot be invariantly ordered in the scale. In conclusion the WHO-DAS II can be used to assess overall disability in patients with schizophrenia, but some domains are too general to assess functionality in these patients because they contain items that are not applicable to this pathology.
Empirical likelihood ratio tests for multivariate regression models
Institute of Scientific and Technical Information of China (English)
WU Jianhong; ZHU Lixing
2007-01-01
This paper proposes some diagnostic tools for checking the adequacy of multivariate regression models including classical regression and time series autoregression. In statistical inference, the empirical likelihood ratio method has been well known to be a powerful tool for constructing test and confidence region. For model checking, however, the naive empirical likelihood (EL) based tests are not of Wilks' phenomenon. Hence, we make use of bias correction to construct the EL-based score tests and derive a nonparametric version of Wilks' theorem. Moreover, by the advantages of both the EL and score test method, the EL-based score tests share many desirable features as follows: They are self-scale invariant and can detect the alternatives that converge to the null at rate n-1/2, the possibly fastest rate for lack-of-fit testing; they involve weight functions, which provides us with the flexibility to choose scores for improving power performance, especially under directional alternatives. Furthermore, when the alternatives are not directional, we construct asymptotically distribution-free maximin tests for a large class of possible alternatives. A simulation study is carried out and an application for a real dataset is analyzed.
Xu, Zhiqiang
2017-02-16
Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.
A Nonparametric Approach for Assessing Goodness-of-Fit of IRT Models in a Mixed Format Test
Liang, Tie; Wells, Craig S.
2015-01-01
Investigating the fit of a parametric model plays a vital role in validating an item response theory (IRT) model. An area that has received little attention is the assessment of multiple IRT models used in a mixed-format test. The present study extends the nonparametric approach, proposed by Douglas and Cohen (2001), to assess model fit of three…
Maydeu-Olivares, Albert
2005-01-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in…
Kalicka, Renata; Pietrenko-Dabrowska, Anna
2007-03-01
In the paper MRI measurements are used for assessment of brain tissue perfusion and other features and functions of the brain (cerebral blood flow - CBF, cerebral blood volume - CBV, mean transit time - MTT). Perfusion is an important indicator of tissue viability and functioning as in pathological tissue blood flow, vascular and tissue structure are altered with respect to normal tissue. MRI enables diagnosing diseases at an early stage of their course. The parametric and non-parametric approaches to the identification of MRI models are presented and compared. The non-parametric modeling adopts gamma variate functions. The parametric three-compartmental catenary model, based on the general kinetic model, is also proposed. The parameters of the models are estimated on the basis of experimental data. The goodness of fit of the gamma variate and the three-compartmental models to the data and the accuracy of the parameter estimates are compared. Kalman filtering, smoothing the measurements, was adopted to improve the estimate accuracy of the parametric model. Parametric modeling gives a better fit and better parameter estimates than non-parametric and allows an insight into the functioning of the system. To improve the accuracy optimal experiment design related to the input signal was performed.
Using nonparametrics to specify a model to measure the value of travel time
DEFF Research Database (Denmark)
Fosgerau, Mogens
2007-01-01
Using a range of nonparametric methods, the paper examines the specification of a model to evaluate the willingness-to-pay (WTP) for travel time changes from binomial choice data from a simple time-cost trading experiment. The analysis favours a model with random WTP as the only source...... of randomness over a model with fixed WTP which is linear in time and cost and has an additive random error term. Results further indicate that the distribution of log WTP can be described as a sum of a linear index fixing the location of the log WTP distribution and an independent random variable representing...... unobserved heterogeneity. This formulation is useful for parametric modelling. The index indicates that the WTP varies systematically with income and other individual characteristics. The WTP varies also with the time difference presented in the experiment which is in contradiction of standard utility theory....
Nonparametric test of consistency between cosmological models and multiband CMB measurements
Aghamousa, Amir
2015-01-01
We present a novel approach to test the consistency of the cosmological models with multiband CMB data using a nonparametric approach. In our analysis we calibrate the REACT (Risk Estimation and Adaptation after Coordinate Transformation) confidence levels associated with distances in function space (confidence distances) based on the Monte Carlo simulations in order to test the consistency of an assumed cosmological model with observation. To show the applicability of our algorithm, we confront Planck 2013 temperature data with concordance model of cosmology considering two different Planck spectra combination. In order to have an accurate quantitative statistical measure to compare between the data and the theoretical expectations, we calibrate REACT confidence distances and perform a bias control using many realizations of the data. Our results in this work using Planck 2013 temperature data put the best fit $\\Lambda$CDM model at $95\\% (\\sim 2\\sigma)$ confidence distance from the center of the nonparametri...
Developing two non-parametric performance models for higher learning institutions
Kasim, Maznah Mat; Kashim, Rosmaini; Rahim, Rahela Abdul; Khan, Sahubar Ali Muhamed Nadhar
2016-08-01
Measuring the performance of higher learning Institutions (HLIs) is a must for these institutions to improve their excellence. This paper focuses on formation of two performance models: efficiency and effectiveness models by utilizing a non-parametric method, Data Envelopment Analysis (DEA). The proposed models are validated by measuring the performance of 16 public universities in Malaysia for year 2008. However, since data for one of the variables is unavailable, an estimate was used as a proxy to represent the real data. The results show that average efficiency and effectiveness scores were 0.817 and 0.900 respectively, while six universities were fully efficient and eight universities were fully effective. A total of six universities were both efficient and effective. It is suggested that the two proposed performance models would work as complementary methods to the existing performance appraisal method or as alternative methods in monitoring the performance of HLIs especially in Malaysia.
Forecasting turbulent modes with nonparametric diffusion models: Learning from noisy data
Berry, Tyrus; Harlim, John
2016-04-01
In this paper, we apply a recently developed nonparametric modeling approach, the "diffusion forecast", to predict the time-evolution of Fourier modes of turbulent dynamical systems. While the diffusion forecasting method assumes the availability of a noise-free training data set observing the full state space of the dynamics, in real applications we often have only partial observations which are corrupted by noise. To alleviate these practical issues, following the theory of embedology, the diffusion model is built using the delay-embedding coordinates of the data. We show that this delay embedding biases the geometry of the data in a way which extracts the most stable component of the dynamics and reduces the influence of independent additive observation noise. The resulting diffusion forecast model approximates the semigroup solutions of the generator of the underlying dynamics in the limit of large data and when the observation noise vanishes. As in any standard forecasting problem, the forecasting skill depends crucially on the accuracy of the initial conditions. We introduce a novel Bayesian method for filtering the discrete-time noisy observations which works with the diffusion forecast to determine the forecast initial densities. Numerically, we compare this nonparametric approach with standard stochastic parametric models on a wide-range of well-studied turbulent modes, including the Lorenz-96 model in weakly chaotic to fully turbulent regimes and the barotropic modes of a quasi-geostrophic model with baroclinic instabilities. We show that when the only available data is the low-dimensional set of noisy modes that are being modeled, the diffusion forecast is indeed competitive to the perfect model.
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
A new bivariate negative binomial regression model
Faroughi, Pouya; Ismail, Noriszura
2014-12-01
This paper introduces a new form of bivariate negative binomial (BNB-1) regression which can be fitted to bivariate and correlated count data with covariates. The BNB regression discussed in this study can be fitted to bivariate and overdispersed count data with positive, zero or negative correlations. The joint p.m.f. of the BNB1 distribution is derived from the product of two negative binomial marginals with a multiplicative factor parameter. Several testing methods were used to check overdispersion and goodness-of-fit of the model. Application of BNB-1 regression is illustrated on Malaysian motor insurance dataset. The results indicated that BNB-1 regression has better fit than bivariate Poisson and BNB-2 models with regards to Akaike information criterion.
A Nonparametric Bayesian Approach to Seismic Hazard Modeling Using the ETAS Framework
Ross, G.
2015-12-01
The epidemic-type aftershock sequence (ETAS) model is one of the most popular tools for modeling seismicity and quantifying risk in earthquake-prone regions. Under the ETAS model, the occurrence times of earthquakes are treated as a self-exciting Poisson process where each earthquake briefly increases the probability of subsequent earthquakes occurring soon afterwards, which captures the fact that large mainshocks tend to produce long sequences of aftershocks. A triggering kernel controls the amount by which the probability increases based on the magnitude of each earthquake, and the rate at which it then decays over time. This triggering kernel is usually chosen heuristically, to match the parametric form of the modified Omori law for aftershock decay. However recent work has questioned whether this is an appropriate choice. Since the choice of kernel has a large impact on the predictions made by the ETAS model, avoiding misspecification is crucially important. We present a novel nonparametric version of ETAS which avoids making parametric assumptions, and instead learns the correct specification from the data itself. Our approach is based on the Dirichlet process, which is a modern class of Bayesian prior distribution which allows for efficient inference over an infinite dimensional space of functions. We show how our nonparametric ETAS model can be fit to data, and present results demonstrating that the fit is greatly improved compared to the standard parametric specification. Additionally, we explain how our model can be used to perform probabilistic declustering of earthquake catalogs, to classify earthquakes as being either aftershocks or mainshocks. and to learn the causal relations between pairs of earthquakes.
Rights, Jason D; Sterba, Sonya K
2016-11-01
Multilevel data structures are common in the social sciences. Often, such nested data are analysed with multilevel models (MLMs) in which heterogeneity between clusters is modelled by continuously distributed random intercepts and/or slopes. Alternatively, the non-parametric multilevel regression mixture model (NPMM) can accommodate the same nested data structures through discrete latent class variation. The purpose of this article is to delineate analytic relationships between NPMM and MLM parameters that are useful for understanding the indirect interpretation of the NPMM as a non-parametric approximation of the MLM, with relaxed distributional assumptions. We define how seven standard and non-standard MLM specifications can be indirectly approximated by particular NPMM specifications. We provide formulas showing how the NPMM can serve as an approximation of the MLM in terms of intraclass correlation, random coefficient means and (co)variances, heteroscedasticity of residuals at level 1, and heteroscedasticity of residuals at level 2. Further, we discuss how these relationships can be useful in practice. The specific relationships are illustrated with simulated graphical demonstrations, and direct and indirect interpretations of NPMM classes are contrasted. We provide an R function to aid in implementing and visualizing an indirect interpretation of NPMM classes. An empirical example is presented and future directions are discussed. © 2016 The British Psychological Society.
A Spline Regression Model for Latent Variables
Harring, Jeffrey R.
2014-01-01
Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the…
Non-Parametric Inference in Astrophysics
Wasserman, L H; Nichol, R C; Genovese, C; Jang, W; Connolly, A J; Moore, A W; Schneider, J; Wasserman, Larry; Miller, Christopher J.; Nichol, Robert C.; Genovese, Chris; Jang, Woncheol; Connolly, Andrew J.; Moore, Andrew W.; Schneider, Jeff; group, the PICA
2001-01-01
We discuss non-parametric density estimation and regression for astrophysics problems. In particular, we show how to compute non-parametric confidence intervals for the location and size of peaks of a function. We illustrate these ideas with recent data on the Cosmic Microwave Background. We also briefly discuss non-parametric Bayesian inference.
Morton, Kenneth D., Jr.; Torrione, Peter A.; Collins, Leslie
2010-04-01
Time domain ground penetrating radar (GPR) has been shown to be a powerful sensing phenomenology for detecting buried objects such as landmines. Landmine detection with GPR data typically utilizes a feature-based pattern classification algorithm to discriminate buried landmines from other sub-surface objects. In high-fidelity GPR, the time-frequency characteristics of a landmine response should be indicative of the physical construction and material composition of the landmine and could therefore be useful for discrimination from other non-threatening sub-surface objects. In this research we propose modeling landmine time-domain responses with a nonparametric Bayesian time-series model and we perform clustering of these time-series models with a hierarchical nonparametric Bayesian model. Each time-series is modeled as a hidden Markov model (HMM) with autoregressive (AR) state densities. The proposed nonparametric Bayesian prior allows for automated learning of the number of states in the HMM as well as the AR order within each state density. This creates a flexible time-series model with complexity determined by the data. Furthermore, a hierarchical non-parametric Bayesian prior is used to group landmine responses with similar HMM model parameters, thus learning the number of distinct landmine response models within a data set. Model inference is accomplished using a fast variational mean field approximation that can be implemented for on-line learning.
Constrained regression models for optimization and forecasting
Directory of Open Access Journals (Sweden)
P.J.S. Bruwer
2003-12-01
Full Text Available Linear regression models and the interpretation of such models are investigated. In practice problems often arise with the interpretation and use of a given regression model in spite of the fact that researchers may be quite "satisfied" with the model. In this article methods are proposed which overcome these problems. This is achieved by constructing a model where the "area of experience" of the researcher is taken into account. This area of experience is represented as a convex hull of available data points. With the aid of a linear programming model it is shown how conclusions can be formed in a practical way regarding aspects such as optimal levels of decision variables and forecasting.
Non-parametric Reconstruction of Cluster Mass Distribution from Strong Lensing Modelling Abell 370
Abdel-Salam, H M; Williams, L L R
1997-01-01
We describe a new non-parametric technique for reconstructing the mass distribution in galaxy clusters with strong lensing, i.e., from multiple images of background galaxies. The observed positions and redshifts of the images are considered as rigid constraints and through the lens (ray-trace) equation they provide us with linear constraint equations. These constraints confine the mass distribution to some allowed region, which is then found by linear programming. Within this allowed region we study in detail the mass distribution with minimum mass-to-light variation; also some others, such as the smoothest mass distribution. The method is applied to the extensively studied cluster Abell 370, which hosts a giant luminous arc and several other multiply imaged background galaxies. Our mass maps are constrained by the observed positions and redshifts (spectroscopic or model-inferred by previous authors) of the giant arc and multiple image systems. The reconstructed maps obtained for A370 reveal a detailed mass d...
Wang, Yin; Jiang, Han
2014-01-01
We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy. PMID:24803950
Directory of Open Access Journals (Sweden)
Yin Wang
2014-01-01
Full Text Available We present a nonparametric shape constrained algorithm for segmentation of coronary arteries in computed tomography images within the framework of active contours. An adaptive scale selection scheme, based on the global histogram information of the image data, is employed to determine the appropriate window size for each point on the active contour, which improves the performance of the active contour model in the low contrast local image regions. The possible leakage, which cannot be identified by using intensity features alone, is reduced through the application of the proposed shape constraint, where the shape of circular sampled intensity profile is used to evaluate the likelihood of current segmentation being considered vascular structures. Experiments on both synthetic and clinical datasets have demonstrated the efficiency and robustness of the proposed method. The results on clinical datasets have shown that the proposed approach is capable of extracting more detailed coronary vessels with subvoxel accuracy.
Sieve M-estimation for semiparametric varying-coefficient partially linear regression model
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
This article considers a semiparametric varying-coefficient partially linear regression model.The semiparametric varying-coefficient partially linear regression model which is a generalization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable.A sieve M-estimation method is proposed and the asymptotic properties of the proposed estimators are discussed.Our main object is to estimate the nonparametric component and the unknown parameters simultaneously.It is easier to compute and the required computation burden is much less than the existing two-stage estimation method.Furthermore,the sieve M-estimation is robust in the presence of outliers if we choose appropriate ρ(·).Under some mild conditions,the estimators are shown to be strongly consistent;the convergence rate of the estimator for the unknown nonparametric component is obtained and the estimator for the unknown parameter is shown to be asymptotically normally distributed.Numerical experiments are carried out to investigate the performance of the proposed method.
A Skew-Normal Mixture Regression Model
Liu, Min; Lin, Tsung-I
2014-01-01
A challenge associated with traditional mixture regression models (MRMs), which rest on the assumption of normally distributed errors, is determining the number of unobserved groups. Specifically, even slight deviations from normality can lead to the detection of spurious classes. The current work aims to (a) examine how sensitive the commonly…
Modeling confounding by half-sibling regression
DEFF Research Database (Denmark)
Schölkopf, Bernhard; Hogg, David W; Wang, Dun
2016-01-01
We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as "half-sibling regression," is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both...
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Jiang, GJ
1998-01-01
This paper develops a nonparametric model of interest rate term structure dynamics based an a spot rate process that permits only positive interest rates and a market price of interest rate risk that precludes arbitrage opportunities. Both the spot rate process and the market price of interest rate
Jiang, GJ
1998-01-01
This paper develops a nonparametric model of interest rate term structure dynamics based an a spot rate process that permits only positive interest rates and a market price of interest rate risk that precludes arbitrage opportunities. Both the spot rate process and the market price of interest rate
Le Bihan, Nicolas; Margerin, Ludovic
2009-07-01
In this paper, we present a nonparametric method to estimate the heterogeneity of a random medium from the angular distribution of intensity of waves transmitted through a slab of random material. Our approach is based on the modeling of forward multiple scattering using compound Poisson processes on compact Lie groups. The estimation technique is validated through numerical simulations based on radiative transfer theory.
Bihan, Nicolas Le
2009-01-01
In this paper, we present a nonparametric method to estimate the heterogeneity of a random medium from the angular distribution of intensity transmitted through a slab of random material. Our approach is based on the modeling of forward multiple scattering using Compound Poisson Processes on compact Lie groups. The estimation technique is validated through numerical simulations based on radiative transfer theory.
Bayesian multimodel inference for geostatistical regression models.
Directory of Open Access Journals (Sweden)
Devin S Johnson
Full Text Available The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs. The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC. The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance.
非参数回归中方差变点的小波检测%Detection of Change Points in Volatility of Non-Parametric Regression by Wavelets
Institute of Scientific and Technical Information of China (English)
王景乐; 郑明
2012-01-01
This paper studies the detection and estimation of change points in volatility under nonparametric regression models.Wavelet methods are applied to construct the test statistics which can be used to detect change points in volatility.The asymptotic distributions of the test statistics are established.We also utilize the test statistics to construct the estimators for the locations and jump sizes of the change points in volatility.The asymptotic properties of these estimators are derived.Some simulation studies are conducted to assess the finite sample performance of the proposed procedures.%本文主要研究了非参数回归模型中方差函数的变点,利用小波方法构造的检验量来检测方差中的变点,建立了这些检验量的渐近分布,并且运用这些检验量构造了方差变点的位置和跳跃幅度的估计,给出了这些估计的渐近性质,并进一步通过随机模拟验证了本文方法在有限样本下的性质.
A Robbins-Monro procedure for estimation in semiparametric regression models
Bercu, Bernard
2011-01-01
This paper is devoted to the parametric estimation of a shift together with the nonparametric estimation of a regression function in a semiparametric regression model. We implement a Robbins-Monro procedure very efficient and easy to handle. On the one hand, we propose a stochastic algorithm similar to that of Robbins-Monro in order to estimate the shift parameter. A preliminary evaluation of the regression function is not necessary for estimating the shift parameter. On the other hand, we make use of a recursive Nadaraya-Watson estimator for the estimation of the regression function. This kernel estimator takes in account the previous estimation of the shift parameter. We establish the almost sure convergence for both Robbins-Monro and Nadaraya-Watson estimators. The asymptotic normality of our estimates is also provided.
An Application on Multinomial Logistic Regression Model
Directory of Open Access Journals (Sweden)
Abdalla M El-Habil
2012-03-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE This study aims to identify an application of Multinomial Logistic Regression model which is one of the important methods for categorical data analysis. This model deals with one nominal/ordinal response variable that has more than two categories, whether nominal or ordinal variable. This model has been applied in data analysis in many areas, for example health, social, behavioral, and educational.To identify the model by practical way, we used real data on physical violence against children, from a survey of Youth 2003 which was conducted by Palestinian Central Bureau of Statistics (PCBS. Segment of the population of children in the age group (10-14 years for residents in Gaza governorate, size of 66,935 had been selected, and the response variable consisted of four categories. Eighteen of explanatory variables were used for building the primary multinomial logistic regression model. Model had been tested through a set of statistical tests to ensure its appropriateness for the data. Also the model had been tested by selecting randomly of two observations of the data used to predict the position of each observation in any classified group it can be, by knowing the values of the explanatory variables used. We concluded by using the multinomial logistic regression model that we can able to define accurately the relationship between the group of explanatory variables and the response variable, identify the effect of each of the variables, and we can predict the classification of any individual case.
Regression Models for Count Data in R
Directory of Open Access Journals (Sweden)
Christian Kleiber
2008-06-01
Full Text Available The classical Poisson, geometric and negative binomial regression models for count data belong to the family of generalized linear models and are available at the core of the statistics toolbox in the R system for statistical computing. After reviewing the conceptual and computational features of these methods, a new implementation of hurdle and zero-inﬂated regression models in the functions hurdle( and zeroinfl( from the package pscl is introduced. It re-uses design and functionality of the basic R functions just as the underlying conceptual tools extend the classical models. Both hurdle and zero-inﬂated model, are able to incorporate over-dispersion and excess zeros-two problems that typically occur in count data sets in economics and the social sciences—better than their classical counterparts. Using cross-section data on the demand for medical care, it is illustrated how the classical as well as the zero-augmented models can be ﬁtted, inspected and tested in practice.
Parametric Regression Models Using Reversed Hazard Rates
Directory of Open Access Journals (Sweden)
Asokan Mulayath Variyath
2014-01-01
Full Text Available Proportional hazard regression models are widely used in survival analysis to understand and exploit the relationship between survival time and covariates. For left censored survival times, reversed hazard rate functions are more appropriate. In this paper, we develop a parametric proportional hazard rates model using an inverted Weibull distribution. The estimation and construction of confidence intervals for the parameters are discussed. We assess the performance of the proposed procedure based on a large number of Monte Carlo simulations. We illustrate the proposed method using a real case example.
Bayesian model selection in Gaussian regression
Abramovich, Felix
2009-01-01
We consider a Bayesian approach to model selection in Gaussian linear regression, where the number of predictors might be much larger than the number of observations. From a frequentist view, the proposed procedure results in the penalized least squares estimation with a complexity penalty associated with a prior on the model size. We investigate the optimality properties of the resulting estimator. We establish the oracle inequality and specify conditions on the prior that imply its asymptotic minimaxity within a wide range of sparse and dense settings for "nearly-orthogonal" and "multicollinear" designs.
Bayesian Inference of a Multivariate Regression Model
Directory of Open Access Journals (Sweden)
Marick S. Sinay
2014-01-01
Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.
General regression and representation model for classification.
Directory of Open Access Journals (Sweden)
Jianjun Qian
Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.
Sueiro, Manuel J.; Abad, Francisco J.
2011-01-01
The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…
Das, Moumita; Bhattacharya, Sourabh
2014-01-01
In this paper, using kernel convolution of order based dependent Dirichlet process (Griffin & Steel (2006)) we construct a nonstationary, nonseparable, nonparametric space-time process, which, as we show, satisfies desirable properties, and includes the stationary, separable, parametric processes as special cases. We also investigate the smoothness properties of our proposed model. Since our model entails an infinite random series, for Bayesian model fitting purpose we must either truncate th...
Nonparametric Autoregression Model on Consumer Price Index%居民消费价格指数的非参数自回归模型
Institute of Scientific and Technical Information of China (English)
代洪伟; 凌能祥
2012-01-01
The nonparametric autoregression model was established using the data of Chinese consumer pr/ce index in 2004 -2008. The OLS estimation, the orthogonal sequence estimation and spline est/mation were used to estimate the regressive function respectively. The result showed that the nonparametric model is superior to linear models and in the three estimation methods, the orthogonal sequence estimation is the best. Finally, the simulated and predicted results were eomoared with those oresented by LIU Chun - van based on ARIMA model.%利用我国2004年-2008年的居民消费价格指数数据，建立非参数自回归模型，并分别用线性最小二乘方法、正交序列方法和多项式样条方法进行了拟合和预测．结果表明，非参数模型优于线性模型；在三种估计方法中，正交序列估计方法优于其他两种方法．最后将模拟、预测的结果和刘春燕等建立的基于ARIMA模型模拟、预测的结果进行了比较．
Adaptive regression for modeling nonlinear relationships
Knafl, George J
2016-01-01
This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...
Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan
2012-01-01
Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.
Nonparametric Estimates of Gene × Environment Interaction Using Local Structural Equation Modeling
Briley, Daniel A.; Harden, K. Paige; Bates, Timothy C.; Tucker-Drob, Elliot M.
2017-01-01
Gene × Environment (G×E) interaction studies test the hypothesis that the strength of genetic influence varies across environmental contexts. Existing latent variable methods for estimating G×E interactions in twin and family data specify parametric (typically linear) functions for the interaction effect. An improper functional form may obscure the underlying shape of the interaction effect and may lead to failures to detect a significant interaction. In this article, we introduce a novel approach to the behavior genetic toolkit, local structural equation modeling (LOSEM). LOSEM is a highly flexible nonparametric approach for estimating latent interaction effects across the range of a measured moderator. This approach opens up the ability to detect and visualize new forms of G×E interaction. We illustrate the approach by using LOSEM to estimate gene × socioeconomic status (SES) interactions for six cognitive phenotypes. Rather than continuously and monotonically varying effects as has been assumed in conventional parametric approaches, LOSEM indicated substantial nonlinear shifts in genetic variance for several phenotypes. The operating characteristics of LOSEM were interrogated through simulation studies where the functional form of the interaction effect was known. LOSEM provides a conservative estimate of G×E interaction with sufficient power to detect statistically significant G×E signal with moderate sample size. We offer recommendations for the application of LOSEM and provide scripts for implementing these biometric models in Mplus and in OpenMx under R. PMID:26318287
Hierarchical linear regression models for conditional quantiles
Institute of Scientific and Technical Information of China (English)
TIAN Maozai; CHEN Gemai
2006-01-01
The quantile regression has several useful features and therefore is gradually developing into a comprehensive approach to the statistical analysis of linear and nonlinear response models,but it cannot deal effectively with the data with a hierarchical structure.In practice,the existence of such data hierarchies is neither accidental nor ignorable,it is a common phenomenon.To ignore this hierarchical data structure risks overlooking the importance of group effects,and may also render many of the traditional statistical analysis techniques used for studying data relationships invalid.On the other hand,the hierarchical models take a hierarchical data structure into account and have also many applications in statistics,ranging from overdispersion to constructing min-max estimators.However,the hierarchical models are virtually the mean regression,therefore,they cannot be used to characterize the entire conditional distribution of a dependent variable given high-dimensional covariates.Furthermore,the estimated coefficient vector (marginal effects)is sensitive to an outlier observation on the dependent variable.In this article,a new approach,which is based on the Gauss-Seidel iteration and taking a full advantage of the quantile regression and hierarchical models,is developed.On the theoretical front,we also consider the asymptotic properties of the new method,obtaining the simple conditions for an n1/2-convergence and an asymptotic normality.We also illustrate the use of the technique with the real educational data which is hierarchical and how the results can be explained.
The limiting behavior of the estimated parameters in a misspecified random field regression model
DEFF Research Database (Denmark)
Dahl, Christian Møller; Qin, Yu
convenient new uniform convergence results that we propose. This theory may have applications beyond those presented here. Our results indicate that classical statistical inference techniques, in general, works very well for random field regression models in finite samples and that these models succesfully......This paper examines the limiting properties of the estimated parameters in the random field regression model recently proposed by Hamilton (Econometrica, 2001). Though the model is parametric, it enjoys the flexibility of the nonparametric approach since it can approximate a large collection...... of nonlinear functions and it has the added advantage that there is no "curse of dimensionality."Contrary to existing literature on the asymptotic properties of the estimated parameters in random field models our results do not require that the explanatory variables are sampled on a grid. However...
Regression Models For Saffron Yields in Iran
S. H, Sanaeinejad; S. N, Hosseini
Saffron is an important crop in social and economical aspects in Khorassan Province (Northeast of Iran). In this research wetried to evaluate trends of saffron yield in recent years and to study the relationship between saffron yield and the climate change. A regression analysis was used to predict saffron yield based on 20 years of yield data in Birjand, Ghaen and Ferdows cities.Climatologically data for the same periods was provided by database of Khorassan Climatology Center. Climatologically data includedtemperature, rainfall, relative humidity and sunshine hours for ModelI, and temperature and rainfall for Model II. The results showed the coefficients of determination for Birjand, Ferdows and Ghaen for Model I were 0.69, 0.50 and 0.81 respectively. Also coefficients of determination for the same cities for model II were 0.53, 0.50 and 0.72 respectively. Multiple regression analysisindicated that among weather variables, temperature was the key parameter for variation ofsaffron yield. It was concluded that increasing temperature at spring was the main cause of declined saffron yield during recent years across the province. Finally, yield trend was predicted for the last 5 years using time series analysis.
Nonparametric tests for censored data
Bagdonavicus, Vilijandas; Nikulin, Mikhail
2013-01-01
This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most statistical software is highlighted and discussed.
Lee, L.; Helsel, D.
2007-01-01
Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.
An Evaluation of Parametric and Nonparametric Models of Fish Population Response.
Energy Technology Data Exchange (ETDEWEB)
Haas, Timothy C.; Peterson, James T.; Lee, Danny C.
1999-11-01
Predicting the distribution or status of animal populations at large scales often requires the use of broad-scale information describing landforms, climate, vegetation, etc. These data, however, often consist of mixtures of continuous and categorical covariates and nonmultiplicative interactions among covariates, complicating statistical analyses. Using data from the interior Columbia River Basin, USA, we compared four methods for predicting the distribution of seven salmonid taxa using landscape information. Subwatersheds (mean size, 7800 ha) were characterized using a set of 12 covariates describing physiography, vegetation, and current land-use. The techniques included generalized logit modeling, classification trees, a nearest neighbor technique, and a modular neural network. We evaluated model performance using out-of-sample prediction accuracy via leave-one-out cross-validation and introduce a computer-intensive Monte Carlo hypothesis testing approach for examining the statistical significance of landscape covariates with the non-parametric methods. We found the modular neural network and the nearest-neighbor techniques to be the most accurate, but were difficult to summarize in ways that provided ecological insight. The modular neural network also required the most extensive computer resources for model fitting and hypothesis testing. The generalized logit models were readily interpretable, but were the least accurate, possibly due to nonlinear relationships and nonmultiplicative interactions among covariates. Substantial overlap among the statistically significant (P<0.05) covariates for each method suggested that each is capable of detecting similar relationships between responses and covariates. Consequently, we believe that employing one or more methods may provide greater biological insight without sacrificing prediction accuracy.
Bayesian Semi- and Non-Parametric Models for Longitudinal Data with Multiple Membership Effects in R
Directory of Open Access Journals (Sweden)
Terrance Savitsky
2014-03-01
Full Text Available We introduce growcurves for R that performs analysis of repeated measures multiple membership (MM data. This data structure arises in studies under which an intervention is delivered to each subject through the subjects participation in a set of multiple elements that characterize the intervention. In our motivating study design under which subjects receive a group cognitive behavioral therapy (CBT treatment, an element is a group CBT session and each subject attends multiple sessions that, together, comprise the treatment. The sets of elements, or group CBT sessions, attended by subjects will partly overlap with some of those from other subjects to induce a dependence in their responses. The growcurves package offers two alternative sets of hierarchical models: 1. Separate terms are specified for multivariate subject and MM element random effects, where the subject effects are modeled under a Dirichlet process prior to produce a semi-parametric construction; 2. A single term is employed to model joint subject-by-MM effects. A fully non-parametric dependent Dirichlet process formulation allows exploration of differences in subject responses across different MM elements. This model allows for borrowing information among subjects who express similar longitudinal trajectories for flexible estimation. growcurves deploys estimation functions to perform posterior sampling under a suite of prior options. An accompanying set of plot functions allows the user to readily extract by-subject growth curves. The design approach intends to anticipate inferential goals with tools that fully extract information from repeated measures data. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++ code.
Inferring gene regression networks with model trees
Directory of Open Access Journals (Sweden)
Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Marmarelis, Vasilis Z; Shin, Dae C; Zhang, Yaping; Kautzky-Willer, Alexandra; Pacini, Giovanni; D'Argenio, David Z
2013-07-01
Modeling studies of the insulin-glucose relationship have mainly utilized parametric models, most notably the minimal model (MM) of glucose disappearance. This article presents results from the comparative analysis of the parametric MM and a nonparametric Laguerre based Volterra Model (LVM) applied to the analysis of insulin modified (IM) intravenous glucose tolerance test (IVGTT) data from a clinical study of gestational diabetes mellitus (GDM). An IM IVGTT study was performed 8 to 10 weeks postpartum in 125 women who were diagnosed with GDM during their pregnancy [population at risk of developing diabetes (PRD)] and in 39 control women with normal pregnancies (control subjects). The measured plasma glucose and insulin from the IM IVGTT in each group were analyzed via a population analysis approach to estimate the insulin sensitivity parameter of the parametric MM. In the nonparametric LVM analysis, the glucose and insulin data were used to calculate the first-order kernel, from which a diagnostic scalar index representing the integrated effect of insulin on glucose was derived. Both the parametric MM and nonparametric LVM describe the glucose concentration data in each group with good fidelity, with an improved measured versus predicted r² value for the LVM of 0.99 versus 0.97 for the MM analysis in the PRD. However, application of the respective diagnostic indices of the two methods does result in a different classification of 20% of the individuals in the PRD. It was found that the data based nonparametric LVM revealed additional insights about the manner in which infused insulin affects blood glucose concentration. © 2013 Diabetes Technology Society.
Quantile regression modeling for Malaysian automobile insurance premium data
Fuzi, Mohd Fadzli Mohd; Ismail, Noriszura; Jemain, Abd Aziz
2015-09-01
Quantile regression is a robust regression to outliers compared to mean regression models. Traditional mean regression models like Generalized Linear Model (GLM) are not able to capture the entire distribution of premium data. In this paper we demonstrate how a quantile regression approach can be used to model net premium data to study the effects of change in the estimates of regression parameters (rating classes) on the magnitude of response variable (pure premium). We then compare the results of quantile regression model with Gamma regression model. The results from quantile regression show that some rating classes increase as quantile increases and some decrease with decreasing quantile. Further, we found that the confidence interval of median regression (τ = O.5) is always smaller than Gamma regression in all risk factors.
Maydeu-Olivares, Albert
2005-04-01
Chernyshenko, Stark, Chan, Drasgow, and Williams (2001) investigated the fit of Samejima's logistic graded model and Levine's non-parametric MFS model to the scales of two personality questionnaires and found that the graded model did not fit well. We attribute the poor fit of the graded model to small amounts of multidimensionality present in their data. To verify this conjecture, we compare the fit of these models to the Social Problem Solving Inventory-Revised, whose scales were designed to be unidimensional. A calibration and a cross-validation sample of new observations were used. We also included the following parametric models in the comparison: Bock's nominal model, Masters' partial credit model, and Thissen and Steinberg's extension of the latter. All models were estimated using full information maximum likelihood. We also included in the comparison a normal ogive model version of Samejima's model estimated using limited information estimation. We found that for all scales Samejima's model outperformed all other parametric IRT models in both samples, regardless of the estimation method employed. The non-parametric model outperformed all parametric models in the calibration sample. However, the graded model outperformed MFS in the cross-validation sample in some of the scales. We advocate employing the graded model estimated using limited information methods in modeling Likert-type data, as these methods are more versatile than full information methods to capture the multidimensionality that is generally present in personality data.
Marginal longitudinal semiparametric regression via penalized splines
Al Kadiri, M.
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Marginal longitudinal semiparametric regression via penalized splines.
Kadiri, M Al; Carroll, R J; Wand, M P
2010-08-01
We study the marginal longitudinal nonparametric regression problem and some of its semiparametric extensions. We point out that, while several elaborate proposals for efficient estimation have been proposed, a relative simple and straightforward one, based on penalized splines, has not. After describing our approach, we then explain how Gibbs sampling and the BUGS software can be used to achieve quick and effective implementation. Illustrations are provided for nonparametric regression and additive models.
Additive Hazard Regression Models: An Application to the Natural History of Human Papillomavirus
Directory of Open Access Journals (Sweden)
Xianhong Xie
2013-01-01
Full Text Available There are several statistical methods for time-to-event analysis, among which is the Cox proportional hazards model that is most commonly used. However, when the absolute change in risk, instead of the risk ratio, is of primary interest or when the proportional hazard assumption for the Cox proportional hazards model is violated, an additive hazard regression model may be more appropriate. In this paper, we give an overview of this approach and then apply a semiparametric as well as a nonparametric additive model to a data set from a study of the natural history of human papillomavirus (HPV in HIV-positive and HIV-negative women. The results from the semiparametric model indicated on average an additional 14 oncogenic HPV infections per 100 woman-years related to CD4 count < 200 relative to HIV-negative women, and those from the nonparametric additive model showed an additional 40 oncogenic HPV infections per 100 women over 5 years of followup, while the estimated hazard ratio in the Cox model was 3.82. Although the Cox model can provide a better understanding of the exposure disease association, the additive model is often more useful for public health planning and intervention.
Nonparametric estimation in an "illness-death" model when all transition times are interval censored
DEFF Research Database (Denmark)
Frydman, Halina; Gerds, Thomas; Grøn, Randi
2013-01-01
We develop nonparametric maximum likelihood estimation for the parameters of an irreversible Markov chain on states {0,1,2} from the observations with interval censored times of 0 → 1, 0 → 2 and 1 → 2 transitions. The distinguishing aspect of the data is that, in addition to all transition times ...
Entrepreneurial intention modeling using hierarchical multiple regression
Directory of Open Access Journals (Sweden)
Marina Jeger
2014-12-01
Full Text Available The goal of this study is to identify the contribution of effectuation dimensions to the predictive power of the entrepreneurial intention model over and above that which can be accounted for by other predictors selected and confirmed in previous studies. As is often the case in social and behavioral studies, some variables are likely to be highly correlated with each other. Therefore, the relative amount of variance in the criterion variable explained by each of the predictors depends on several factors such as the order of variable entry and sample specifics. The results show the modest predictive power of two dimensions of effectuation prior to the introduction of the theory of planned behavior elements. The article highlights the main advantages of applying hierarchical regression in social sciences as well as in the specific context of entrepreneurial intention formation, and addresses some of the potential pitfalls that this type of analysis entails.
Nonparametric statistical methods using R
Kloke, John
2014-01-01
A Practical Guide to Implementing Nonparametric and Rank-Based ProceduresNonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. The authors emphasize applications and statistical computation. They illustrate the methods with many real and simulated data examples using R, including the packages Rfit and npsm.The book first gives an overview of the R language and basic statistical c
Boosted Regression Tree Models to Explain Watershed ...
Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite-nitrate (NO2-NO3), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO2-NO3 concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed-use and heavily impacted watershed
Essays on parametric and nonparametric modeling and estimation with applications to energy economics
Gao, Weiyu
My dissertation research is composed of two parts: a theoretical part on semiparametric efficient estimation and an applied part in energy economics under different dynamic settings. The essays are related in terms of their applications as well as the way in which models are constructed and estimated. In the first essay, efficient estimation of the partially linear model is studied. We work out the efficient score functions and efficiency bounds under four stochastic restrictions---independence, conditional symmetry, conditional zero mean, and partially conditional zero mean. A feasible efficient estimation method for the linear part of the model is developed based on the efficient score. A battery of specification test that allows for choosing between the alternative assumptions is provided. A Monte Carlo simulation is also conducted. The second essay presents a dynamic optimization model for a stylized oilfield resembling the largest developed light oil field in Saudi Arabia, Ghawar. We use data from different sources to estimate the oil production cost function and the revenue function. We pay particular attention to the dynamic aspect of the oil production by employing petroleum-engineering software to simulate the interaction between control variables and reservoir state variables. Optimal solutions are studied under different scenarios to account for the possible changes in the exogenous variables and the uncertainty about the forecasts. The third essay examines the effect of oil price volatility on the level of innovation displayed by the U.S. economy. A measure of innovation is calculated by decomposing an output-based Malmquist index. We also construct a nonparametric measure for oil price volatility. Technical change and oil price volatility are then placed in a VAR system with oil price and a variable indicative of monetary policy. The system is estimated and analyzed for significant relationships. We find that oil price volatility displays a significant
National Research Council Canada - National Science Library
Arbel, Julyan; King, Catherine K; Raymond, Ben; Winsley, Tristrom; Mengersen, Kerrie L
2015-01-01
...‐species toxicity tests. In this study, we apply a Bayesian nonparametric model to a soil microbial data set acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica...
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Gianola, Daniel; Wu, Xiao-Lin; Manfredi, Eduardo; Simianer, Henner
2010-10-01
A Bayesian nonparametric form of regression based on Dirichlet process priors is adapted to the analysis of quantitative traits possibly affected by cryptic forms of gene action, and to the context of SNP-assisted genomic selection, where the main objective is to predict a genomic signal on phenotype. The procedure clusters unknown genotypes into groups with distinct genetic values, but in a setting in which the number of clusters is unknown a priori, so that standard methods for finite mixture analysis do not work. The central assumption is that genetic effects follow an unknown distribution with some "baseline" family, which is a normal process in the cases considered here. A Bayesian analysis based on the Gibbs sampler produces estimates of the number of clusters, posterior means of genetic effects, a measure of credibility in the baseline distribution, as well as estimates of parameters of the latter. The procedure is illustrated with a simulation representing two populations. In the first one, there are 3 unknown QTL, with additive, dominance and epistatic effects; in the second, there are 10 QTL with additive, dominance and additive × additive epistatic effects. In the two populations, baseline parameters are inferred correctly. The Dirichlet process model infers the number of unique genetic values correctly in the first population, but it produces an understatement in the second one; here, the true number of clusters is over 900, and the model gives a posterior mean estimate of about 140, probably because more replication of genotypes is needed for correct inference. The impact on inferences of the prior distribution of a key parameter (M), and of the extent of replication, was examined via an analysis of mean body weight in 192 paternal half-sib families of broiler chickens, where each sire was genotyped for nearly 7,000 SNPs. In this small sample, it was found that inference about the number of clusters was affected by the prior distribution of M. For a
Dziak, John J.; Li, Runze; Tan, Xianming; Shiffman, Saul; Shiyko, Mariya P.
2015-01-01
Behavioral scientists increasingly collect intensive longitudinal data (ILD), in which phenomena are measured at high frequency and in real time. In many such studies, it is of interest to describe the pattern of change over time in important variables as well as the changing nature of the relationship between variables. Individuals' trajectories on variables of interest may be far from linear, and the predictive relationship between variables of interest and related covariates may also change over time in a nonlinear way. Time-varying effect models (TVEMs; see Tan, Shiyko, Li, Li, & Dierker, 2012) address these needs by allowing regression coefficients to be smooth, nonlinear functions of time rather than constants. However, it is possible that not only observed covariates but also unknown, latent variables may be related to the outcome. That is, regression coefficients may change over time and also vary for different kinds of individuals. Therefore, we describe a finite mixture version of TVEM for situations in which the population is heterogeneous and in which a single trajectory would conceal important, inter-individual differences. This extended approach, MixTVEM, combines finite mixture modeling with non- or semi-parametric regression modeling, in order to describe a complex pattern of change over time for distinct latent classes of individuals. The usefulness of the method is demonstrated in an empirical example from a smoking cessation study. We provide a versatile SAS macro and R function for fitting MixTVEMs. PMID:26390169
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
Harlander, Niklas; Rosenkranz, Tobias; Hohmann, Volker
2012-08-01
Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. The perceptual investigation was performed with fourteen hearing-impaired subjects. The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.
Energy Technology Data Exchange (ETDEWEB)
Hampf, Benjamin
2011-08-15
In this paper we present a new approach to evaluate the environmental efficiency of decision making units. We propose a model that describes a two-stage process consisting of a production and an end-of-pipe abatement stage with the environmental efficiency being determined by the efficiency of both stages. Taking the dependencies between the two stages into account, we show how nonparametric methods can be used to measure environmental efficiency and to decompose it into production and abatement efficiency. For an empirical illustration we apply our model to an analysis of U.S. power plants.
Neelakantan, S; Veng-Pedersen, P
2005-11-01
A novel numerical deconvolution method is presented that enables the estimation of drug absorption rates under time-variant disposition conditions. The method involves two components. (1) A disposition decomposition-recomposition (DDR) enabling exact changes in the unit impulse response (UIR) to be constructed based on centrally based clearance changes iteratively determined. (2) A non-parametric, end-constrained cubic spline (ECS) input response function estimated by cross-validation. The proposed DDR-ECS method compensates for disposition changes between the test and the reference administrations by using a "beta" clearance correction based on DDR analysis. The representation of the input response by the ECS method takes into consideration the complex absorption process and also ensures physiologically realistic approximations of the response. The stability of the new method to noisy data was evaluated by comprehensive simulations that considered different UIRs, various input functions, clearance changes and a novel scaling of the input function that includes the "flip-flop" absorption phenomena. The simulated input response was also analysed by two other methods and all three methods were compared for their relative performances. The DDR-ECS method provides better estimation of the input profile under significant clearance changes but tends to overestimate the input when there were only small changes in the clearance.
Model performance analysis and model validation in logistic regression
Directory of Open Access Journals (Sweden)
Rosa Arboretti Giancristofaro
2007-10-01
Full Text Available In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study.
Testing Equality of Nonparametric Functions in Two Partially Linear Models%检验两个部分线性模型中非参函数相等
Institute of Scientific and Technical Information of China (English)
施三支; 宋立新; 杨华
2008-01-01
We propose the test statistic to check whether the nonparametric func-tions in two partially linear models are equality or not in this paper. We estimate the nonparametric function both in null hypothesis and the alternative by the local linear method, where we ignore the parametric components, and then estimate the parameters by the two stage method. The test statistic is derived, and it is shown to be asymptotically normal under the null hypothesis.
Focused information criterion and model averaging based on weighted composite quantile regression
Xu, Ganggang
2013-08-13
We study the focused information criterion and frequentist model averaging and their application to post-model-selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non-parametric functions approximated by polynomial splines, we show that, under certain conditions, the asymptotic distribution of the frequentist model averaging WCQR-estimator of a focused parameter is a non-linear mixture of normal distributions. This asymptotic distribution is used to construct confidence intervals that achieve the nominal coverage probability. With properly chosen weights, the focused information criterion based WCQR estimators are not only robust to outliers and non-normal residuals but also can achieve efficiency close to the maximum likelihood estimator, without assuming the true error distribution. Simulation studies and a real data analysis are used to illustrate the effectiveness of the proposed procedure. © 2013 Board of the Foundation of the Scandinavian Journal of Statistics..
Post-L1-Penalized Estimators in High-Dimensional Linear Regression Models
Belloni, Alexandre
2010-01-01
In this paper we study the post-penalized estimator which applies ordinary, unpenalized linear regression to the model selected by the first step penalized estimators, typically the LASSO. We show that post-LASSO can perform as well or nearly as well as the LASSO in terms of the rate of convergence. We show that this performance occurs even if the LASSO-based model selection "fails", in the sense of missing some components of the "true" regression model. Furthermore, post-LASSO can perform strictly better than LASSO, in the sense of a strictly faster rate of convergence, if the LASSO-based model selection correctly includes all components of the "true" model as a subset and enough sparsity is obtained. Of course, in the extreme case, when LASSO perfectly selects the true model, the past-LASSO estimator becomes the oracle estimator. We show that the results hold in both parametric and non-parametric models; and by the "true" model we mean the best $s$-dimensional approximation to the true regression model, whe...
SMOOTH TRANSITION LOGISTIC REGRESSION MODEL TREE
RODRIGO PINTO MOREIRA
2008-01-01
Este trabalho tem como objetivo principal adaptar o modelo STR-Tree, o qual é a combinação de um modelo Smooth Transition Regression com Classification and Regression Tree (CART), a fim de utilizá-lo em Classificação. Para isto algumas alterações foram realizadas em sua forma estrutural e na estimação. Devido ao fato de estarmos fazendo classificação de variáveis dependentes binárias, se faz necessária a utilização das técnicas empregadas em Regressão Logística, dessa forma a estimação dos pa...
DEFF Research Database (Denmark)
A methodology is presented that combines modelling based on first principles and data based modelling into a modelling cycle that facilitates fast decision-making based on statistical methods. A strong feature of this methodology is that given a first principles model along with process data, the......, the corresponding modelling cycle model of the given system for a given purpose. A computer-aided tool, which integrates the elements of the modelling cycle, is also presented, and an example is given of modelling a fed-batch bioreactor....
Energy Technology Data Exchange (ETDEWEB)
Marques, T.C.; Cruz Junior, G.; Vinhal, C. [Universidade Federal de Goias (UFG), Goiania, GO (Brazil). Escola de Engenharia Eletrica e de Computacao], Emails: thyago@eeec.ufg.br, gcruz@eeec.ufg.br, vinhal@eeec.ufg.br
2009-07-01
The goal of this paper is to present a methodology to carry out the seasonal stream flow forecasting using database of average monthly inflows of one Brazilian hydroelectric plant located at Grande, Tocantins, Paranaiba, Sao Francisco and Iguacu river's. The model is based on the Adaptive Network Based Fuzzy Inference System (ANFIS), the non-parametric model. The performance of this model was compared with a periodic autoregressive model, the parametric model. The results show that the forecasting errors of the non-parametric model considered are significantly lower than the parametric model. (author)
Ohkubo, Jun
2011-12-01
A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.
Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara
2017-01-01
In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.
Model selection in kernel ridge regression
DEFF Research Database (Denmark)
Exterkate, Peter
2013-01-01
Kernel ridge regression is a technique to perform ridge regression with a potentially infinite number of nonlinear transformations of the independent variables as regressors. This method is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts....... The influence of the choice of kernel and the setting of tuning parameters on forecast accuracy is investigated. Several popular kernels are reviewed, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. The latter two kernels are interpreted in terms of their smoothing properties......, and the tuning parameters associated to all these kernels are related to smoothness measures of the prediction function and to the signal-to-noise ratio. Based on these interpretations, guidelines are provided for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study...
A Dirty Model for Multiple Sparse Regression
Jalali, Ali; Sanghavi, Sujay
2011-01-01
Sparse linear regression -- finding an unknown vector from linear measurements -- is now known to be possible with fewer samples than variables, via methods like the LASSO. We consider the multiple sparse linear regression problem, where several related vectors -- with partially shared support sets -- have to be recovered. A natural question in this setting is whether one can use the sharing to further decrease the overall number of samples required. A line of recent research has studied the use of \\ell_1/\\ell_q norm block-regularizations with q>1 for such problems; however these could actually perform worse in sample complexity -- vis a vis solving each problem separately ignoring sharing -- depending on the level of sharing. We present a new method for multiple sparse linear regression that can leverage support and parameter overlap when it exists, but not pay a penalty when it does not. A very simple idea: we decompose the parameters into two components and regularize these differently. We show both theore...
Vale, A
2007-01-01
We revisit the longstanding question of whether first brightest cluster galaxies are statistically drawn from the same distribution as other cluster galaxies or are "special", using the new non-parametric, empirically based model presented in Vale&Ostriker (2006) for associating galaxy luminosity with halo/subhalo masses. We introduce scatter in galaxy luminosity at fixed halo mass into this model, building a conditional luminosity function (CLF) by considering two possible models: a simple lognormal and a model based on the distribution of concentration in haloes of a given mass. We show that this model naturally allows an identification of halo/subhalo systems with groups and clusters of galaxies, giving rise to a clear central/satellite galaxy distinction. We then use these results to build up the dependence of brightest cluster galaxy (BCG) magnitudes on cluster luminosity, focusing on two statistical indicators, the dispersion in BCG magnitude and the magnitude difference between first and second bri...
Cirillo, Pasquale; Muliere, Pietro
2010-01-01
In this paper we propose a new nonparametric approach to interacting failing systems (FS), that is systems whose probability of failure is not negligible in a fixed time horizon, a typical example being firms and financial bonds. The main purpose when studying a FS is to calculate the probability of default and the distribution of the number of failures that may occur during the observation period. A model used to study a failing system is defined default model. In particular, we present a general recursive model constructed by the means of inter- acting urns. After introducing the theoretical model and its properties we show a first application to credit risk modeling, showing how to assess the idiosyncratic probability of default of an obligor and the joint probability of failure of a set of obligors in a portfolio of risks, that are divided into reliability classes.
Logistic Regression Model on Antenna Control Unit Autotracking Mode
2015-10-20
412TW-PA-15240 Logistic Regression Model on Antenna Control Unit Autotracking Mode DANIEL T. LAIRD AIR FORCE TEST CENTER EDWARDS AFB, CA...OCT 15 4. TITLE AND SUBTITLE Logistic Regression Model on Antenna Control Unit Autotracking Mode 5a. CONTRACT NUMBER 5b. GRANT...alternative-hypothesis. This paper will present an Antenna Auto- tracking model using Logistic Regression modeling. This paper presents an example of
On The Robustness of z=0-1 Galaxy Size Measurements Through Model and Non-Parametric Fits
Mosleh, Moein; Franx, Marijn
2013-01-01
We present the size-stellar mass relations of nearby (z=0.01-0.02) SDSS galaxies, for samples selected by color, morphology, Sersic index n, and specific star formation rate. Several commonly-employed size measurement techniques are used, including single Sersic fits, two-component Sersic models and a non-parametric method. Through simple simulations we show that the non-parametric and two-component Sersic methods provide the most robust effective radius measurements, while those based on single Sersic profiles are often overestimates, especially for massive red/early-type galaxies. Using our robust sizes, we show that for all sub-samples, the mass-size relations are shallow at low stellar masses and steepen above ~3-4 x 10^{10}\\Msun. The mass-size relations for galaxies classified as late-type, low-n, and star-forming are consistent with each other, while blue galaxies follow a somewhat steeper relation. The mass-size relations of early-type, high-n, red, and quiescent galaxies all agree with each other but ...
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.
Multiple Retrieval Models and Regression Models for Prior Art Search
Lopez, Patrice
2009-01-01
This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach presents three main characteristics: 1. The usage of multiple retrieval models (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend.
Relative risk regression models with inverse polynomials.
Ning, Yang; Woodward, Mark
2013-08-30
The proportional hazards model assumes that the log hazard ratio is a linear function of parameters. In the current paper, we model the log relative risk as an inverse polynomial, which is particularly suitable for modeling bounded and asymmetric functions. The parameters estimated by maximizing the partial likelihood are consistent and asymptotically normal. The advantages of the inverse polynomial model over the ordinary polynomial model and the fractional polynomial model for fitting various asymmetric log relative risk functions are shown by simulation. The utility of the method is further supported by analyzing two real data sets, addressing the specific question of the location of the minimum risk threshold.
Model Selection in Kernel Ridge Regression
DEFF Research Database (Denmark)
Exterkate, Peter
Kernel ridge regression is gaining popularity as a data-rich nonlinear forecasting tool, which is applicable in many different contexts. This paper investigates the influence of the choice of kernel and the setting of tuning parameters on forecast accuracy. We review several popular kernels......, including polynomial kernels, the Gaussian kernel, and the Sinc kernel. We interpret the latter two kernels in terms of their smoothing properties, and we relate the tuning parameters associated to all these kernels to smoothness measures of the prediction function and to the signal-to-noise ratio. Based...... on these interpretations, we provide guidelines for selecting the tuning parameters from small grids using cross-validation. A Monte Carlo study confirms the practical usefulness of these rules of thumb. Finally, the flexible and smooth functional forms provided by the Gaussian and Sinc kernels makes them widely...
Combining logistic regression and neural networks to create predictive models.
Spackman, K. A.
1992-01-01
Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building wit...
Directory of Open Access Journals (Sweden)
Hong-Juan Li
2013-04-01
Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
Stochastic Approximation Methods for Latent Regression Item Response Models
von Davier, Matthias; Sinharay, Sandip
2010-01-01
This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…
Symbolic regression of generative network models
Menezes, Telmo
2014-01-01
Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied "out of the box" to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world netwo...
A semiparametric Wald statistic for testing logistic regression models based on case-control data
Institute of Scientific and Technical Information of China (English)
2008-01-01
We propose a semiparametric Wald statistic to test the validity of logistic regression models based on case-control data. The test statistic is constructed using a semiparametric ROC curve estimator and a nonparametric ROC curve estimator. The statistic has an asymptotic chi-squared distribution and is an alternative to the Kolmogorov-Smirnov-type statistic proposed by Qin and Zhang in 1997, the chi-squared-type statistic proposed by Zhang in 1999 and the information matrix test statistic proposed by Zhang in 2001. The statistic is easy to compute in the sense that it requires none of the following methods: using a bootstrap method to find its critical values, partitioning the sample data or inverting a high-dimensional matrix. We present some results on simulation and on analysis of two real examples. Moreover, we discuss how to extend our statistic to a family of statistics and how to construct its Kolmogorov-Smirnov counterpart.
Rothwell, James J; Futter, Martyn N; Dise, Nancy B
2008-11-01
Often, there is a non-linear relationship between atmospheric dissolved inorganic nitrogen (DIN) input and DIN leaching that is poorly captured by existing models. We present the first application of the non-parametric classification and regression tree approach to evaluate the key environmental drivers controlling DIN leaching from European forests. DIN leaching was classified as low (15kg N ha(-1) year(-1)) at 215 sites across Europe. The analysis identified throughfall NO(3)(-) deposition, acid deposition, hydrology, soil type, the carbon content of the soil, and the legacy of historic N deposition as the dominant drivers of DIN leaching for these forests. Ninety four percent of sites were successfully classified into the appropriate leaching category. This approach shows promise for understanding complex ecosystem responses to a wide range of anthropogenic stressors as well as an improved method for identifying risk and targeting pollution mitigation strategies in forest ecosystems.
Convergence Rates of Wavelet Estimators in Semiparametric Regression Models Under NA Samples
Institute of Scientific and Technical Information of China (English)
Hongchang HU; Li WU
2012-01-01
Consider the following heteroscedastic semiparametric regression model:yi =XTiβ + g(ti) + σiei, 1 ＜ i ≤ n,where {Xi,1 ＜ i ＜ n} are random design points,errors {ei,1 ＜ i ＜ n} are negatively associated (NA) random variables,σ2i =h(ui),and {ui} and {ti} are two nonrandom sequences on [0,1].Some wavelet estimators of the parametric component β,the nonparametric component g(t) and the variance function h(u) are given.Under some general conditions,the strong convergence rate of these wavelet estimators is O(n -1/3 log n).Hence our results are extensions of those results on independent random error settings.
Vargas, M.; Crossa, J.; Eeuwijk, van F.A.; Ramirez, M.E.; Sayre, K.
1999-01-01
Partial least squares (PLS) and factorial regression (FR) are statistical models that incorporate external environmental and/or cultivar variables for studying and interpreting genotype × environment interaction (GEl). The Additive Main effect and Multiplicative Interaction (AMMI) model uses only th
Semi- and Nonparametric ARCH Processes
Directory of Open Access Journals (Sweden)
Oliver B. Linton
2011-01-01
Full Text Available ARCH/GARCH modelling has been successfully applied in empirical finance for many years. This paper surveys the semiparametric and nonparametric methods in univariate and multivariate ARCH/GARCH models. First, we introduce some specific semiparametric models and investigate the semiparametric and nonparametrics estimation techniques applied to: the error density, the functional form of the volatility function, the relationship between mean and variance, long memory processes, locally stationary processes, continuous time processes and multivariate models. The second part of the paper is about the general properties of such processes, including stationary conditions, ergodic conditions and mixing conditions. The last part is on the estimation methods in ARCH/GARCH processes.
Corporate prediction models, ratios or regression analysis?
Bijnen, E.J.; Wijn, M.F.C.M.
1994-01-01
The models developed in the literature with respect to the prediction of a company s failure are based on ratios. It has been shown before that these models should be rejected on theoretical grounds. Our study of industrial companies in the Netherlands shows that the ratios which are used in
Semiparametric regression during 2003–2007
Ruppert, David
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation
Kekatos, Vassilis
2011-01-01
Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but unfortunately cannot be met by least-squares based or kernel regression methods. To this end, compressed sampling (CS) approaches, already successful in linear regression settings, can offer a viable alternative. The viability of CS for sparse Volterra and polynomial models is the core theme of this work. A common sparse regression task is initially posed for the two models. Building on (weighted) Lasso-based schemes, an adaptive RLS-type algorithm is developed for sparse polynomial regressions. The identifiability of polynomial models is critically challenged by dimensionality. However, following the CS principle, when these models are sparse, they could be recovered by far fewer measurements. ...
Mixed Frequency Data Sampling Regression Models: The R Package midasr
Directory of Open Access Journals (Sweden)
Eric Ghysels
2016-08-01
Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.
Impact of multicollinearity on small sample hydrologic regression models
Kroll, Charles N.; Song, Peter
2013-06-01
Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.
Support vector regression model for complex target RCS predicting
Institute of Scientific and Technical Information of China (English)
Wang Gu; Chen Weishi; Miao Jungang
2009-01-01
The electromagnetic scattering computation has developed rapidly for many years; some computing problems for complex and coated targets cannot be solved by using the existing theory and computing models. A computing model based on data is established for making up the insufficiency of theoretic models. Based on the "support vector regression method", which is formulated on the principle of minimizing a structural risk, a data model to predicate the unknown radar cross section of some appointed targets is given. Comparison between the actual data and the results of this predicting model based on support vector regression method proved that the support vector regression method is workable and with a comparative precision.
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any
Jiang, GJ; Knight, JL
1997-01-01
In this paper, we propose a nonparametric identification and estimation procedure for an Ito diffusion process based on discrete sampling observations. The nonparametric kernel estimator for the diffusion function developed in this paper deals with general Ito diffusion processes and avoids any func
Energy Technology Data Exchange (ETDEWEB)
Lozano, Sebastian; Gutierrez, Ester [University of Seville, E.S.I., Department of Industrial Management, Camino de los Descubrimientos, s/n, 41092 Sevilla (Spain)
2008-07-15
In this paper, a non-parametric approach based in Data Envelopment Analysis (DEA) is proposed as an alternative to the Kaya identity (a.k.a ImPACT). This Frontier Method identifies and extends existing best practices. Population and GDP are considered as input and output, respectively. Both primary energy consumption and Greenhouse Gas (GHG) emissions are considered as undesirable outputs. Several Linear Programming models are formulated with different aims, namely: (a) determine efficiency levels, (b) estimate maximum GDP compatible with given levels of population, energy intensity and carbonization intensity, and (c) estimate the minimum level of GHG emissions compatible with given levels of population, GDP, energy intensity or carbonization index. The United States of America case is used as illustration of the proposed approach. (author)
Rank-preserving regression: a more robust rank regression model against outliers.
Chen, Tian; Kowalski, Jeanne; Chen, Rui; Wu, Pan; Zhang, Hui; Feng, Changyong; Tu, Xin M
2016-08-30
Mean-based semi-parametric regression models such as the popular generalized estimating equations are widely used to improve robustness of inference over parametric models. Unfortunately, such models are quite sensitive to outlying observations. The Wilcoxon-score-based rank regression (RR) provides more robust estimates over generalized estimating equations against outliers. However, the RR and its extensions do not sufficiently address missing data arising in longitudinal studies. In this paper, we propose a new approach to address outliers under a different framework based on the functional response models. This functional-response-model-based alternative not only addresses limitations of the RR and its extensions for longitudinal data, but, with its rank-preserving property, even provides more robust estimates than these alternatives. The proposed approach is illustrated with both real and simulated data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Nonlinear and Non Normal Regression Models in Physiological Research
1984-01-01
Applications of nonlinear and non normal regression models are in increasing order for appropriate interpretation of complex phenomenon of biomedical sciences. This paper reviews critically some applications of these models physiological research.
A contingency table approach to nonparametric testing
Rayner, JCW
2000-01-01
Most texts on nonparametric techniques concentrate on location and linear-linear (correlation) tests, with less emphasis on dispersion effects and linear-quadratic tests. Tests for higher moment effects are virtually ignored. Using a fresh approach, A Contingency Table Approach to Nonparametric Testing unifies and extends the popular, standard tests by linking them to tests based on models for data that can be presented in contingency tables.This approach unifies popular nonparametric statistical inference and makes the traditional, most commonly performed nonparametric analyses much more comp
Identification of Influential Points in a Linear Regression Model
Directory of Open Access Journals (Sweden)
Jan Grosz
2011-03-01
Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models
Naeini, Mahdi Pakdaman; Cooper, Gregory F.
2017-01-01
Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called ensemble of near isotonic regression (ENIR). The method can be considered as an extension of BBQ [20], a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression (IsoRegC) [27]. ENIR is designed to address the key limitation of IsoRegC which is the monotonicity assumption of the predictions. Similar to BBQ, the method post-processes the output of a binary classifier to obtain calibrated probabilities. Thus it can be used with many existing classification models to generate accurate probabilistic predictions. We demonstrate the performance of ENIR on synthetic and real datasets for commonly applied binary classification models. Experimental results show that the method outperforms several common binary classifier calibration methods. In particular on the real data, ENIR commonly performs statistically significantly better than the other methods, and never worse. It is able to improve the calibration power of classifiers, while retaining their discrimination power. The method is also computationally tractable for large scale datasets, as it is O(N log N) time, where N is the number of samples.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2010-01-01
Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente
Adaptive Regression and Classification Models with Applications in Insurance
Directory of Open Access Journals (Sweden)
Jekabsons Gints
2014-07-01
Full Text Available Nowadays, in the insurance industry the use of predictive modeling by means of regression and classification techniques is becoming increasingly important and popular. The success of an insurance company largely depends on the ability to perform such tasks as credibility estimation, determination of insurance premiums, estimation of probability of claim, detecting insurance fraud, managing insurance risk. This paper discusses regression and classification modeling for such types of prediction problems using the method of Adaptive Basis Function Construction
Geometric Properties of AR（q） Nonlinear Regression Models
Institute of Scientific and Technical Information of China (English)
LIUYing-ar; WEIBo-cheng
2004-01-01
This paper is devoted to a study of geometric properties of AR(q) nonlinear regression models. We present geometric frameworks for regression parameter space and autoregression parameter space respectively based on the weighted inner product by fisher information matrix. Several geometric properties related to statistical curvatures are given for the models. The results of this paper extended the work of Bates & Watts(1980,1988)[1.2] and Seber & Wild (1989)[3].
Wavelet regression model in forecasting crude oil price
Hamid, Mohd Helmie; Shabri, Ani
2017-05-01
This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.
Regression Model Optimization for the Analysis of Experimental Data
Ulbrich, N.
2009-01-01
A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.
Alternative regression models to assess increase in childhood BMI
Directory of Open Access Journals (Sweden)
Mansmann Ulrich
2008-09-01
Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.
High-dimensional regression with unknown variance
Giraud, Christophe; Verzelen, Nicolas
2011-01-01
We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasize is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the Lasso esti- mator and some references are collected for some more general models, including multivariate regression and nonparametric regression.
Credit Scoring Model Hybridizing Artificial Intelligence with Logistic Regression
Directory of Open Access Journals (Sweden)
Han Lu
2013-01-01
Full Text Available Today the most commonly used techniques for credit scoring are artificial intelligence and statistics. In this paper, we started a new way to use these two kinds of models. Through logistic regression filters the variables with a high degree of correlation, artificial intelligence models reduce complexity and accelerate convergence, while these models hybridizing logistic regression have better explanations in statistically significance, thus improve the effect of artificial intelligence models. With experiments on German data set, we find an interesting phenomenon defined as ‘Dimensional interference’ with support vector machine and from cross validation it can be seen that the new method gives a lot of help with credit scoring.
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
Kulmala, A; Tenhunen, M
2012-11-07
The signal of the dosimetric detector is generally dependent on the shape and size of the sensitive volume of the detector. In order to optimize the performance of the detector and reliability of the output signal the effect of the detector size should be corrected or, at least, taken into account. The response of the detector can be modelled using the convolution theorem that connects the system input (actual dose), output (measured result) and the effect of the detector (response function) by a linear convolution operator. We have developed the super-resolution and non-parametric deconvolution method for determination of the cylinder symmetric ionization chamber radial response function. We have demonstrated that the presented deconvolution method is able to determine the radial response for the Roos parallel plate ionization chamber with a better than 0.5 mm correspondence with the physical measures of the chamber. In addition, the performance of the method was proved by the excellent agreement between the output factors of the stereotactic conical collimators (4-20 mm diameter) measured by the Roos chamber, where the detector size is larger than the measured field, and the reference detector (diode). The presented deconvolution method has a potential in providing reference data for more accurate physical models of the ionization chamber as well as for improving and enhancing the performance of the detectors in specific dosimetric problems.
Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza
2014-10-01
The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of
Group Lasso for high dimensional sparse quantile regression models
Kato, Kengo
2011-01-01
This paper studies the statistical properties of the group Lasso estimator for high dimensional sparse quantile regression models where the number of explanatory variables (or the number of groups of explanatory variables) is possibly much larger than the sample size while the number of variables in "active" groups is sufficiently small. We establish a non-asymptotic bound on the $\\ell_{2}$-estimation error of the estimator. This bound explains situations under which the group Lasso estimator is potentially superior/inferior to the $\\ell_{1}$-penalized quantile regression estimator in terms of the estimation error. We also propose a data-dependent choice of the tuning parameter to make the method more practical, by extending the original proposal of Belloni and Chernozhukov (2011) for the $\\ell_{1}$-penalized quantile regression estimator. As an application, we analyze high dimensional additive quantile regression models. We show that under a set of primitive regularity conditions, the group Lasso estimator c...
Development of a Modified Kernel Regression Model for a Robust Signal Reconstruction
Energy Technology Data Exchange (ETDEWEB)
Ahmed, Ibrahim; Heo, Gyunyoung [Kyung Hee University, Yongin (Korea, Republic of)
2016-10-15
The demand for robust and resilient performance has led to the use of online-monitoring techniques to monitor the process parameters and signal validation. On-line monitoring and signal validation techniques are the two important terminologies in process and equipment monitoring. These techniques are automated methods of monitoring instrument performance while the plant is operating. To implementing these techniques, several empirical models are used. One of these models is nonparametric regression model, otherwise known as kernel regression (KR). Unlike parametric models, KR is an algorithmic estimation procedure which assumes no significant parameters, and it needs no training process after its development when new observations are prepared; which is good for a system characteristic of changing due to ageing phenomenon. Although KR is used and performed excellently when applied to steady state or normal operating data, it has limitation in time-varying data that has several repetition of the same signal, especially if those signals are used to infer the other signals. The convectional KR has limitation in correctly estimating the dependent variable when time-varying data with repeated values are used to estimate the dependent variable especially in signal validation and monitoring. Therefore, we presented here in this work a modified KR that can resolve this issue which can also be feasible in time domain. Data are first transformed prior to the Euclidian distance evaluation considering their slopes/changes with respect to time. The performance of the developed model is evaluated and compared with that of conventional KR using both the lab experimental data and the real time data from CNS provided by KAERI. The result shows that the proposed developed model, having demonstrated high performance accuracy than that of conventional KR, is capable of resolving the identified limitation with convectional KR. We also discovered that there is still need to further
Joint regression analysis and AMMI model applied to oat improvement
Oliveira, A.; Oliveira, T. A.; Mejza, S.
2012-09-01
In our work we present an application of some biometrical methods useful in genotype stability evaluation, namely AMMI model, Joint Regression Analysis (JRA) and multiple comparison tests. A genotype stability analysis of oat (Avena Sativa L.) grain yield was carried out using data of the Portuguese Plant Breeding Board, sample of the 22 different genotypes during the years 2002, 2003 and 2004 in six locations. In Ferreira et al. (2006) the authors state the relevance of the regression models and of the Additive Main Effects and Multiplicative Interactions (AMMI) model, to study and to estimate phenotypic stability effects. As computational techniques we use the Zigzag algorithm to estimate the regression coefficients and the agricolae-package available in R software for AMMI model analysis.
Buffalos milk yield analysis using random regression models
Directory of Open Access Journals (Sweden)
A.S. Schierholt
2010-02-01
Full Text Available Data comprising 1,719 milk yield records from 357 females (predominantly Murrah breed, daughters of 110 sires, with births from 1974 to 2004, obtained from the Programa de Melhoramento Genético de Bubalinos (PROMEBUL and from records of EMBRAPA Amazônia Oriental - EAO herd, located in Belém, Pará, Brazil, were used to compare random regression models for estimating variance components and predicting breeding values of the sires. The data were analyzed by different models using the Legendre’s polynomial functions from second to fourth orders. The random regression models included the effects of herd-year, month of parity date of the control; regression coefficients for age of females (in order to describe the fixed part of the lactation curve and random regression coefficients related to the direct genetic and permanent environment effects. The comparisons among the models were based on the Akaike Infromation Criterion. The random effects regression model using third order Legendre’s polynomials with four classes of the environmental effect were the one that best described the additive genetic variation in milk yield. The heritability estimates varied from 0.08 to 0.40. The genetic correlation between milk yields in younger ages was close to the unit, but in older ages it was low.
Optimization of Regression Models of Experimental Data Using Confirmation Points
Ulbrich, N.
2010-01-01
A new search metric is discussed that may be used to better assess the predictive capability of different math term combinations during the optimization of a regression model of experimental data. The new search metric can be determined for each tested math term combination if the given experimental data set is split into two subsets. The first subset consists of data points that are only used to determine the coefficients of the regression model. The second subset consists of confirmation points that are exclusively used to test the regression model. The new search metric value is assigned after comparing two values that describe the quality of the fit of each subset. The first value is the standard deviation of the PRESS residuals of the data points. The second value is the standard deviation of the response residuals of the confirmation points. The greater of the two values is used as the new search metric value. This choice guarantees that both standard deviations are always less or equal to the value that is used during the optimization. Experimental data from the calibration of a wind tunnel strain-gage balance is used to illustrate the application of the new search metric. The new search metric ultimately generates an optimized regression model that was already tested at regression model independent confirmation points before it is ever used to predict an unknown response from a set of regressors.
Directory of Open Access Journals (Sweden)
José L. Valencia
2015-11-01
Full Text Available Rainfall, one of the most important climate variables, is commonly studied due to its great heterogeneity, which occasionally causes negative economic, social, and environmental consequences. Modeling the spatial distributions of rainfall patterns over watersheds has become a major challenge for water resources management. Multifractal analysis can be used to reproduce the scale invariance and intermittency of rainfall processes. To identify which factors are the most influential on the variability of multifractal parameters and, consequently, on the spatial distribution of rainfall patterns for different time scales in this study, universal multifractal (UM analysis—C1, α, and γs UM parameters—was combined with non-parametric statistical techniques that allow spatial-temporal comparisons of distributions by gradients. The proposed combined approach was applied to a daily rainfall dataset of 132 time-series from 1931 to 2009, homogeneously spatially-distributed across a 25 km × 25 km grid covering the Ebro River Basin. A homogeneous increase in C1 over the watershed and a decrease in α mainly in the western regions, were detected, suggesting an increase in the frequency of dry periods at different scales and an increase in the occurrence of rainfall process variability over the last decades.
DEFF Research Database (Denmark)
Scheike, Thomas Harder
2002-01-01
We use the additive risk model of Aalen (Aalen, 1980) as a model for the rate of a counting process. Rather than specifying the intensity, that is the instantaneous probability of an event conditional on the entire history of the relevant covariates and counting processes, we present a model...... for the rate function, i.e., the instantaneous probability of an event conditional on only a selected set of covariates. When the rate function for the counting process is of Aalen form we show that the usual Aalen estimator can be used and gives almost unbiased estimates. The usual martingale based variance...... estimator is incorrect and an alternative estimator should be used. We also consider the semi-parametric version of the Aalen model as a rate model (McKeague and Sasieni, 1994) and show that the standard errors that are computed based on an assumption of intensities are incorrect and give a different...
Geographically Weighted Logistic Regression Applied to Credit Scoring Models
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Pedro Henrique Melo Albuquerque
Full Text Available Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC, granted to clients residing in the Distrito Federal (DF, to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters, with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least squares...
Systematic evaluation of land use regression models for NO₂
Wang, M.|info:eu-repo/dai/nl/345480279; Beelen, R.M.J.|info:eu-repo/dai/nl/30483100X; Eeftens, M.R.|info:eu-repo/dai/nl/315028300; Meliefste, C.; Hoek, G.|info:eu-repo/dai/nl/069553475; Brunekreef, B.|info:eu-repo/dai/nl/067548180
2012-01-01
Land use regression (LUR) models have become popular to explain the spatial variation of air pollution concentrations. Independent evaluation is important. We developed LUR models for nitrogen dioxide (NO(2)) using measurements conducted at 144 sampling sites in The Netherlands. Sites were randomly
FUNCTIONAL-COEFFICIENT REGRESSION MODEL AND ITS ESTIMATION
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In this paper,a class of functional-coefficient regression models is proposed and an estimation procedure based on the locally weighted least equares is suggested. This class of models,with the proposed estimation method,is a powerful means for exploratory data analysis.
Güneralp, İnci; Filippi, Anthony M.; Randall, Jarom
2014-12-01
Floodplain forests serve a critical function in the global carbon cycle because floodplains constitute an important carbon sink compared with other terrestrial ecosystems. Forests on dynamic floodplain landscapes, such as those created by river meandering processes, are characterized by uneven-aged trees and exhibit high spatial variability, reflecting the influence of interacting fluvial, hydrological, and ecological processes. Detailed and accurate mapping of aboveground biomass (AGB) on floodplain landscapes characterized by uneven-aged forests is critical for improving estimates of floodplain-forest carbon pools, which is useful for greenhouse gas (GHG) life cycle assessment. It would also help improve our process understanding of biomorphodynamics of river-floodplain systems, as well as planning and monitoring of conservation, restoration, and management of riverine ecosystems. Using stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), and Cubist, we remotely estimate AGB of a bottomland hardwood forest on a meander bend of a dynamic lowland river. As predictors, we use 30-m and 10-m multispectral image bands (Landsat 7 ETM+ and SPOT 5, respectively) and ancillary data. Our findings show that SGB and MARS significantly outperform Cubist, which is used for U.S. national-scale forest biomass mapping. Across all data-experiments and algorithms, at 10-m spatial resolution, SGB yields the best estimates (RMSE = 22.49 tonnes/ha; coefficient of determination (R2) = 0.96) when geomorphometric data are also included. On the other hand, at 30-m spatial resolution, MARS yields the best estimates (RMSE = 29.2 tonnes/ha; R2 = 0.94) when image-derived data are also included. By enabling more accurate AGB mapping of floodplains characterized by uneven-aged forests, SGB and MARS provide an avenue for improving operational estimates of AGB and carbon at local, regional/continental, and global scales.
Fitting Additive Binomial Regression Models with the R Package blm
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Stephanie Kovalchik
2013-09-01
Full Text Available The R package blm provides functions for fitting a family of additive regression models to binary data. The included models are the binomial linear model, in which all covariates have additive effects, and the linear-expit (lexpit model, which allows some covariates to have additive effects and other covariates to have logisitc effects. Additive binomial regression is a model of event probability, and the coefficients of linear terms estimate covariate-adjusted risk differences. Thus, in contrast to logistic regression, additive binomial regression puts focus on absolute risk and risk differences. In this paper, we give an overview of the methodology we have developed to fit the binomial linear and lexpit models to binary outcomes from cohort and population-based case-control studies. We illustrate the blm packages methods for additive model estimation, diagnostics, and inference with risk association analyses of a bladder cancer nested case-control study in the NIH-AARP Diet and Health Study.
msSurv: An R Package for Nonparametric Estimation of Multistate Models
Directory of Open Access Journals (Sweden)
Nicole Ferguson
2012-09-01
Full Text Available We present an R package, msSurv, to calculate the marginal (that is, not conditional on any covariates state occupation probabilities, the state entry and exit time distributions, and the marginal integrated transition hazard for a general, possibly non-Markov, multistate system under left-truncation and right censoring. For a Markov model, msSurv also calculates and returns the transition probability matrix between any two states. Dependent censoring is handled via modeling the censoring hazard through observable covariates. Pointwise confidence intervals for the above mentioned quantities are obtained and returned for independent censoring from closed-form variance estimators and for dependent censoring using the bootstrap.
Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.
Chatzis, Sotirios P; Andreou, Andreas S
2015-11-01
Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.
Woudt, Edwin; de Boer, Pieter-Tjerk; van Ommeren, Jan C.W.
2007-01-01
Previous work on state-dependent adaptive importance sampling techniques for the simulation of rare events in Markovian queueing models used either no smoothing or a parametric smoothing technique, which was known to be non-optimal. In this paper, we introduce the use of kernel smoothing in this con
Moraes, L.E.; Kebreab, E.; Strathe, A.B.; France, J.; Dijkstra, J.; Casper, D.; Fadel, J.G.
2014-01-01
Linear and non-linear models have been extensively utilised for the estimation of net and metabolisable energy requirements and for the estimation of the efficiencies of utilising dietary energy for maintenance and tissue gain. In growing animals, biological principles imply that energy retention ra
Sugarcane Land Classification with Satellite Imagery using Logistic Regression Model
Henry, F.; Herwindiati, D. E.; Mulyono, S.; Hendryli, J.
2017-03-01
This paper discusses the classification of sugarcane plantation area from Landsat-8 satellite imagery. The classification process uses binary logistic regression method with time series data of normalized difference vegetation index as input. The process is divided into two steps: training and classification. The purpose of training step is to identify the best parameter of the regression model using gradient descent algorithm. The best fit of the model can be utilized to classify sugarcane and non-sugarcane area. The experiment shows high accuracy and successfully maps the sugarcane plantation area which obtained best result of Cohen’s Kappa value 0.7833 (strong) with 89.167% accuracy.
The art of regression modeling in road safety
Hauer, Ezra
2015-01-01
This unique book explains how to fashion useful regression models from commonly available data to erect models essential for evidence-based road safety management and research. Composed from techniques and best practices presented over many years of lectures and workshops, The Art of Regression Modeling in Road Safety illustrates that fruitful modeling cannot be done without substantive knowledge about the modeled phenomenon. Class-tested in courses and workshops across North America, the book is ideal for professionals, researchers, university professors, and graduate students with an interest in, or responsibilities related to, road safety. This book also: · Presents for the first time a powerful analytical tool for road safety researchers and practitioners · Includes problems and solutions in each chapter as well as data and spreadsheets for running models and PowerPoint presentation slides · Features pedagogy well-suited for graduate courses and workshops including problems, solutions, and PowerPoint p...
Forecasting ozone concentrations in the east of Croatia using nonparametric Neural Network Models
Kovač-Andrić, Elvira; Sheta, Alaa; Faris, Hossam; Gajdošik, Martina Šrajer
2016-07-01
Ozone is one of the most significant secondary pollutants with numerous negative effects on human health and environment including plants and vegetation. Therefore, more effort is made recently by governments and associations to predict ozone concentrations which could help in establishing better plans and regulation for environment protection. In this study, we use two Artificial Neural Network based approaches (MPL and RBF) to develop, for the first time, accurate ozone prediction models, one for urban and another one for rural area in the eastern part of Croatia. The evaluation of actual against the predicted ozone concentrations revealed that MLP and RBF models are very competitive for the training and testing data in the case of Kopački Rit area whereas in the case of Osijek city, MLP shows better evaluation results with 9% improvement in the correlation coefficient. Furthermore, subsequent feature selection process has improved the prediction power of RBF network.
Forecasting ozone concentrations in the east of Croatia using nonparametric Neural Network Models
Indian Academy of Sciences (India)
Elvira Kovac-Andric; Alaa Sheta; Hossam Faris; Martina Srajer Gajdosik
2016-07-01
Ozone is one of the most significant secondary pollutants with numerous negative effects on humanhealth and environment including plants and vegetation. Therefore, more effort is made recently bygovernments and associations to predict ozone concentrations which could help in establishing betterplans and regulation for environment protection. In this study, we use two Artificial Neural Networkbased approaches (MPL and RBF) to develop, for the first time, accurate ozone prediction models, onefor urban and another one for rural area in the eastern part of Croatia. The evaluation of actual againstthe predicted ozone concentrations revealed that MLP and RBF models are very competitive for thetraining and testing data in the case of Kopaˇcki Rit area whereas in the case of Osijek city, MLP showsbetter evaluation results with 9% improvement in the correlation coefficient. Furthermore, subsequentfeature selection process has improved the prediction power of RBF network.
Nonparametric Bayes approach for a semi-mechanistic pharmacokinetic and pharmacodynamic model
Dong, Yan
Both frequentist and Bayesian approaches have been used to characterize population pharmacokinetics and pharmacodynamics(PK/PD) models. These methods focus on estimating the population parameters and assessing the association between the characteristics of PK/PD and the subject covariates. In this work, we propose a Dirichlet process mixture model to classify the patients based on their individualized pharmacokinetic and pharmacodynamic profiles. Then we can predict the new patients' dose-response curves given their concentration-time profiles. Additionally, we implement a modern Markov Chain Monte Carlo algorithm for sampling inference of parameters. The detailed sampling procedures as well as the results are discussed in a simulation data and a real data example. We also evaluate an approximate solution of a system of nonlinear differential equations from Euler's method and compare the results with a general numerical solver, ode from R package, deSolve.
Bayesian Non-parametric model to Target Gamification Notifications Using Big Data
Nia, Meisam Hejazi; Ratchford, Brian
2016-01-01
I suggest an approach that helps the online marketers to target their Gamification elements to users by modifying the order of the list of tasks that they send to users. It is more realistic and flexible as it allows the model to learn more parameters when the online marketers collect more data. The targeting approach is scalable and quick, and it can be used over streaming data.
Directory of Open Access Journals (Sweden)
William A Griffin
Full Text Available Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects-some good and some bad-on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM. Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes.
Logistic regression for risk factor modelling in stuttering research.
Reed, Phil; Wu, Yaqionq
2013-06-01
To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.
Direction of Effects in Multiple Linear Regression Models.
Wiedermann, Wolfgang; von Eye, Alexander
2015-01-01
Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.
Modelling multimodal photometric redshift regression with noisy observations
Kügler, S D
2016-01-01
In this work, we are trying to extent the existing photometric redshift regression models from modeling pure photometric data back to the spectra themselves. To that end, we developed a PCA that is capable of describing the input uncertainty (including missing values) in a dimensionality reduction framework. With this "spectrum generator" at hand, we are capable of treating the redshift regression problem in a fully Bayesian framework, returning a posterior distribution over the redshift. This approach allows therefore to approach the multimodal regression problem in an adequate fashion. In addition, input uncertainty on the magnitudes can be included quite naturally and lastly, the proposed algorithm allows in principle to make predictions outside the training values which makes it a fascinating opportunity for the detection of high-redshifted quasars.
Robust Bayesian Regularized Estimation Based on t Regression Model
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Zean Li
2015-01-01
Full Text Available The t distribution is a useful extension of the normal distribution, which can be used for statistical modeling of data sets with heavy tails, and provides robust estimation. In this paper, in view of the advantages of Bayesian analysis, we propose a new robust coefficient estimation and variable selection method based on Bayesian adaptive Lasso t regression. A Gibbs sampler is developed based on the Bayesian hierarchical model framework, where we treat the t distribution as a mixture of normal and gamma distributions and put different penalization parameters for different regression coefficients. We also consider the Bayesian t regression with adaptive group Lasso and obtain the Gibbs sampler from the posterior distributions. Both simulation studies and real data example show that our method performs well compared with other existing methods when the error distribution has heavy tails and/or outliers.
A Multi-objective Procedure for Efficient Regression Modeling
Sinha, Ankur; Kuosmanen, Timo
2012-01-01
Variable selection is recognized as one of the most critical steps in statistical modeling. The problems encountered in engineering and social sciences are commonly characterized by over-abundance of explanatory variables, non-linearities and unknown interdependencies between the regressors. An added difficulty is that the analysts may have little or no prior knowledge on the relative importance of the variables. To provide a robust method for model selection, this paper introduces a technique called the Multi-objective Genetic Algorithm for Variable Selection (MOGA-VS) which provides the user with an efficient set of regression models for a given data-set. The algorithm considers the regression problem as a two objective task, where the purpose is to choose those models over the other which have less number of regression coefficients and better goodness of fit. In MOGA-VS, the model selection procedure is implemented in two steps. First, we generate the frontier of all efficient or non-dominated regression m...
Ma, Yanyuan; Wang, Yuanjia
2014-04-15
Huntington's disease (HD) is a neurodegenerative disorder with a dominant genetic mode of inheritance caused by an expansion of CAG repeats on chromosome 4. Typically, a longer sequence of CAG repeat length is associated with increased risk of experiencing earlier onset of HD. Previous studies of the association between HD onset age and CAG length have favored a logistic model, where the CAG repeat length enters the mean and variance components of the logistic model in a complex exponential-linear form. To relax the parametric assumption of the exponential-linear association to the true HD onset distribution, we propose to leave both mean and variance functions of the CAG repeat length unspecified and perform semiparametric estimation in this context through a local kernel and backfitting procedure. Motivated by including family history of HD information available in the family members of participants in the Cooperative Huntington's Observational Research Trial (COHORT), we develop the methodology in the context of mixture data, where some subjects have a positive probability of being risk free. We also allow censoring on the age at onset of disease and accommodate covariates other than the CAG length. We study the theoretical properties of the proposed estimator and derive its asymptotic distribution. Finally, we apply the proposed methods to the COHORT data to estimate the HD onset distribution using a group of study participants and the disease family history information available on their family members.
Analyzing industrial energy use through ordinary least squares regression models
Golden, Allyson Katherine
Extensive research has been performed using regression analysis and calibrated simulations to create baseline energy consumption models for residential buildings and commercial institutions. However, few attempts have been made to discuss the applicability of these methodologies to establish baseline energy consumption models for industrial manufacturing facilities. In the few studies of industrial facilities, the presented linear change-point and degree-day regression analyses illustrate ideal cases. It follows that there is a need in the established literature to discuss the methodologies and to determine their applicability for establishing baseline energy consumption models of industrial manufacturing facilities. The thesis determines the effectiveness of simple inverse linear statistical regression models when establishing baseline energy consumption models for industrial manufacturing facilities. Ordinary least squares change-point and degree-day regression methods are used to create baseline energy consumption models for nine different case studies of industrial manufacturing facilities located in the southeastern United States. The influence of ambient dry-bulb temperature and production on total facility energy consumption is observed. The energy consumption behavior of industrial manufacturing facilities is only sometimes sufficiently explained by temperature, production, or a combination of the two variables. This thesis also provides methods for generating baseline energy models that are straightforward and accessible to anyone in the industrial manufacturing community. The methods outlined in this thesis may be easily replicated by anyone that possesses basic spreadsheet software and general knowledge of the relationship between energy consumption and weather, production, or other influential variables. With the help of simple inverse linear regression models, industrial manufacturing facilities may better understand their energy consumption and
Applications of some discrete regression models for count data
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B. M. Golam Kibria
2006-01-01
Full Text Available In this paper we have considered several regression models to fit the count data that encounter in the field of Biometrical, Environmental, Social Sciences and Transportation Engineering. We have fitted Poisson (PO, Negative Binomial (NB, Zero-Inflated Poisson (ZIP and Zero-Inflated Negative Binomial (ZINB regression models to run-off-road (ROR crash data which collected on arterial roads in south region (rural of Florida State. To compare the performance of these models, we analyzed data with moderate to high percentage of zero counts. Because the variances were almost three times greater than the means, it appeared that both NB and ZINB models performed better than PO and ZIP models for the zero inflated and over dispersed count data.
A regression model to estimate regional ground water recharge.
Lorenz, David L; Delin, Geoffrey N
2007-01-01
A regional regression model was developed to estimate the spatial distribution of ground water recharge in subhumid regions. The regional regression recharge (RRR) model was based on a regression of basin-wide estimates of recharge from surface water drainage basins, precipitation, growing degree days (GDD), and average basin specific yield (SY). Decadal average recharge, precipitation, and GDD were used in the RRR model. The RRR estimates were derived from analysis of stream base flow using a computer program that was based on the Rorabaugh method. As expected, there was a strong correlation between recharge and precipitation. The model was applied to statewide data in Minnesota. Where precipitation was least in the western and northwestern parts of the state (50 to 65 cm/year), recharge computed by the RRR model also was lowest (0 to 5 cm/year). A strong correlation also exists between recharge and SY. SY was least in areas where glacial lake clay occurs, primarily in the northwest part of the state; recharge estimates in these areas were in the 0- to 5-cm/year range. In sand-plain areas where SY is greatest, recharge estimates were in the 15- to 29-cm/year range on the basis of the RRR model. Recharge estimates that were based on the RRR model compared favorably with estimates made on the basis of other methods. The RRR model can be applied in other subhumid regions where region wide data sets of precipitation, streamflow, GDD, and soils data are available.
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Harrell , Jr , Frank E
2015-01-01
This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically...
Modeling energy expenditure in children and adolescents using quantile regression
Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obes...
Linearity and Misspecification Tests for Vector Smooth Transition Regression Models
DEFF Research Database (Denmark)
Teräsvirta, Timo; Yang, Yukai
The purpose of the paper is to derive Lagrange multiplier and Lagrange multiplier type specification and misspecification tests for vector smooth transition regression models. We report results from simulation studies in which the size and power properties of the proposed asymptotic tests in small...
Trimmed Likelihood-based Estimation in Binary Regression Models
Cizek, P.
2005-01-01
The binary-choice regression models such as probit and logit are typically estimated by the maximum likelihood method.To improve its robustness, various M-estimation based procedures were proposed, which however require bias corrections to achieve consistency and their resistance to outliers is rela
PARAMETER ESTIMATION IN LINEAR REGRESSION MODELS FOR LONGITUDINAL CONTAMINATED DATA
Institute of Scientific and Technical Information of China (English)
QianWeimin; LiYumei
2005-01-01
The parameter estimation and the coefficient of contamination for the regression models with repeated measures are studied when its response variables are contaminated by another random variable sequence. Under the suitable conditions it is proved that the estimators which are established in the paper are strongly consistent estimators.
Häme, Yrjö; Pollari, Mika
2012-01-01
A novel liver tumor segmentation method for CT images is presented. The aim of this work was to reduce the manual labor and time required in the treatment planning of radiofrequency ablation (RFA), by providing accurate and automated tumor segmentations reliably. The developed method is semi-automatic, requiring only minimal user interaction. The segmentation is based on non-parametric intensity distribution estimation and a hidden Markov measure field model, with application of a spherical shape prior. A post-processing operation is also presented to remove the overflow to adjacent tissue. In addition to the conventional approach of using a single image as input data, an approach using images from multiple contrast phases was developed. The accuracy of the method was validated with two sets of patient data, and artificially generated samples. The patient data included preoperative RFA images and a public data set from "3D Liver Tumor Segmentation Challenge 2008". The method achieved very high accuracy with the RFA data, and outperformed other methods evaluated with the public data set, receiving an average overlap error of 30.3% which represents an improvement of 2.3% points to the previously best performing semi-automatic method. The average volume difference was 23.5%, and the average, the RMS, and the maximum surface distance errors were 1.87, 2.43, and 8.09 mm, respectively. The method produced good results even for tumors with very low contrast and ambiguous borders, and the performance remained high with noisy image data.
Improved Methodology for Parameter Inference in Nonlinear, Hydrologic Regression Models
Bates, Bryson C.
1992-01-01
A new method is developed for the construction of reliable marginal confidence intervals and joint confidence regions for the parameters of nonlinear, hydrologic regression models. A parameter power transformation is combined with measures of the asymptotic bias and asymptotic skewness of maximum likelihood estimators to determine the transformation constants which cause the bias or skewness to vanish. These optimized constants are used to construct confidence intervals and regions for the transformed model parameters using linear regression theory. The resulting confidence intervals and regions can be easily mapped into the original parameter space to give close approximations to likelihood method confidence intervals and regions for the model parameters. Unlike many other approaches to parameter transformation, the procedure does not use a grid search to find the optimal transformation constants. An example involving the fitting of the Michaelis-Menten model to velocity-discharge data from an Australian gauging station is used to illustrate the usefulness of the methodology.
On modified skew logistic regression model and its applications
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C. Satheesh Kumar
2015-12-01
Full Text Available Here we consider a modiﬁed form of the logistic regression model useful for situations where the dependent variable is dichotomous in nature and the explanatory variables exhibit asymmetric and multimodal behaviour. The proposed model has been ﬁtted to some real life data set by using method of maximum likelihood estimation and illustrated its usefulness in certain medical applications.
Improved Testing and Specifivations of Smooth Transition Regression Models
Escribano, Álvaro; Jordá, Óscar
1997-01-01
This paper extends previous work in Escribano and Jordá (1997)and introduces new LM specification procedures to choose between Logistic and Exponential Smooth Transition Regression (STR)Models. These procedures are simpler, consistent and more powerful than those previously available in the literature. An analysis of the properties of Taylor approximations around the transition function of STR models permits one to understand why these procedures work better and it suggests ways to improve te...
Support vector regression-based internal model control
Institute of Scientific and Technical Information of China (English)
HUANG Yan-wei; PENG Tie-gen
2007-01-01
This paper proposes a design of internal model control systems for process with delay by using support vector regression (SVR). The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle. Closed-system stability and steady error are analyzed for the existence of modeling errors. The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.
CONSERVATIVE ESTIMATING FUNCTIONIN THE NONLINEAR REGRESSION MODEL WITHAGGREGATED DATA
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
The purpose of this paper is to study the theory of conservative estimating functions in nonlinear regression model with aggregated data. In this model, a quasi-score function with aggregated data is defined. When this function happens to be conservative, it is projection of the true score function onto a class of estimation functions. By constructing, the potential function for the projected score with aggregated data is obtained, which have some properties of log-likelihood function.
Using regression models to determine the poroelastic properties of cartilage.
Chung, Chen-Yuan; Mansour, Joseph M
2013-07-26
The feasibility of determining biphasic material properties using regression models was investigated. A transversely isotropic poroelastic finite element model of stress relaxation was developed and validated against known results. This model was then used to simulate load intensity for a wide range of material properties. Linear regression equations for load intensity as a function of the five independent material properties were then developed for nine time points (131, 205, 304, 390, 500, 619, 700, 800, and 1000s) during relaxation. These equations illustrate the effect of individual material property on the stress in the time history. The equations at the first four time points, as well as one at a later time (five equations) could be solved for the five unknown material properties given computed values of the load intensity. Results showed that four of the five material properties could be estimated from the regression equations to within 9% of the values used in simulation if time points up to 1000s are included in the set of equations. However, reasonable estimates of the out of plane Poisson's ratio could not be found. Although all regression equations depended on permeability, suggesting that true equilibrium was not realized at 1000s of simulation, it was possible to estimate material properties to within 10% of the expected values using equations that included data up to 800s. This suggests that credible estimates of most material properties can be obtained from tests that are not run to equilibrium, which is typically several thousand seconds.
Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate
Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno
2017-03-01
This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.
REGRESSION ANALYSIS OF PRODUCTIVITY USING MIXED EFFECT MODEL
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Siana Halim
2007-01-01
Full Text Available Production plants of a company are located in several areas that spread across Middle and East Java. As the production process employs mostly manpower, we suspected that each location has different characteristics affecting the productivity. Thus, the production data may have a spatial and hierarchical structure. For fitting a linear regression using the ordinary techniques, we are required to make some assumptions about the nature of the residuals i.e. independent, identically and normally distributed. However, these assumptions were rarely fulfilled especially for data that have a spatial and hierarchical structure. We worked out the problem using mixed effect model. This paper discusses the model construction of productivity and several characteristics in the production line by taking location as a random effect. The simple model with high utility that satisfies the necessary regression assumptions was built using a free statistic software R version 2.6.1.
Illustrating Bayesian evaluation of informative hypotheses for regression models
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Anouck eKluytmans
2012-01-01
Full Text Available In the present paper we illustrate the Bayesian evaluation of informative hypotheses for regression models. This approach allows psychologists to more directly test their theories than they would using conventional statis- tical analyses. Throughout this paper, both real-world data and simulated datasets will be introduced and evaluated to investigate the pragmatical as well as the theoretical qualities of the approach. We will pave the way from forming informative hypotheses in the context of regression models to interpreting the Bayes factors that express the support for the hypotheses being evaluated. In doing so, the present approach goes beyond p-values and uninformative null hypothesis testing, moving on to informative testing and quantification of model support in a way that is accessible to everyday psychologists.
Batch Mode Active Learning for Regression With Expected Model Change.
Cai, Wenbin; Zhang, Muhan; Zhang, Ya
2016-04-20
While active learning (AL) has been widely studied for classification problems, limited efforts have been done on AL for regression. In this paper, we introduce a new AL framework for regression, expected model change maximization (EMCM), which aims at choosing the unlabeled data instances that result in the maximum change of the current model once labeled. The model change is quantified as the difference between the current model parameters and the updated parameters after the inclusion of the newly selected examples. In light of the stochastic gradient descent learning rule, we approximate the change as the gradient of the loss function with respect to each single candidate instance. Under the EMCM framework, we propose novel AL algorithms for the linear and nonlinear regression models. In addition, by simulating the behavior of the sequential AL policy when applied for k iterations, we further extend the algorithms to batch mode AL to simultaneously choose a set of k most informative instances at each query time. Extensive experimental results on both UCI and StatLib benchmark data sets have demonstrated that the proposed algorithms are highly effective and efficient.
Hierarchical Neural Regression Models for Customer Churn Prediction
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Golshan Mohammadi
2013-01-01
Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.
A semiparametric Wald statistic for testing logistic regression models based on case-control data
Institute of Scientific and Technical Information of China (English)
WAN ShuWen
2008-01-01
We propose a semiparametric Wald statistic to test the validity of logistic regression models based on case-control data.The test statistic is constructed using a semiparametric ROC curve estimator and a nonparametric ROC curve estimator.The statistic has an asymptotic chi-squared distribution and is an alternative to the Kolmogorov-Smirnov-type statistic proposed by Qin and Zhang in 1997,the chi-squared-type statistic proposed by Zhang in 1999 and the information matrix test statistic proposed by Zhang in 2001.The statistic is easy to compute in the sense that it requires none of the following methods:using a bootstrap method to find its critical values,partitioning the sample data or inverting a high-dimensional matrix.We present some results on simulation and on analysis of two real examples.Moreover,we discuss how to extend our statistic to a family of statistics and how to construct its Kolmogorov-Smirnov counterpart.
Institute of Scientific and Technical Information of China (English)
Wengang Zhang; Anthony T.C. Goh
2016-01-01
Piles are long, slender structural elements used to transfer the loads from the superstructure through weak strata onto stiffer soils or rocks. For driven piles, the impact of the piling hammer induces compression and tension stresses in the piles. Hence, an important design consideration is to check that the strength of the pile is sufficient to resist the stresses caused by the impact of the pile hammer. Due to its complexity, pile drivability lacks a precise analytical solution with regard to the phenomena involved. In situations where measured data or numerical hypothetical results are available, neural networks stand out in mapping the nonlinear interactions and relationships between the system’s predictors and dependent responses. In addition, unlike most computational tools, no mathematical relationship assumption between the dependent and independent variables has to be made. Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configu-ration is not known a priori. This paper investigates the use of a fairly simple nonparametric regression algorithm known as multivariate adaptive regression splines (MARS), as an alternative to neural net-works, to approximate the relationship between the inputs and dependent response, and to mathe-matically interpret the relationship between the various parameters. In this paper, the Back propagation neural network (BPNN) and MARS models are developed for assessing pile drivability in relation to the prediction of the Maximum compressive stresses (MCS), Maximum tensile stresses (MTS), and Blow per foot (BPF). A database of more than four thousand piles is utilized for model development and comparative performance between BPNN and MARS predictions.
Regression Model to Predict Global Solar Irradiance in Malaysia
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Hairuniza Ahmed Kutty
2015-01-01
Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.
Phone Duration Modeling of Affective Speech Using Support Vector Regression
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Alexandros Lazaridis
2012-07-01
Full Text Available In speech synthesis accurate modeling of prosody is important for producing high quality synthetic speech. One of the main aspects of prosody is phone duration. Robust phone duration modeling is a prerequisite for synthesizing emotional speech with natural sounding. In this work ten phone duration models are evaluated. These models belong to well known and widely used categories of algorithms, such as the decision trees, linear regression, lazy-learning algorithms and meta-learning algorithms. Furthermore, we investigate the effectiveness of Support Vector Regression (SVR in phone duration modeling in the context of emotional speech. The evaluation of the eleven models is performed on a Modern Greek emotional speech database which consists of four categories of emotional speech (anger, fear, joy, sadness plus neutral speech. The experimental results demonstrated that the SVR-based modeling outperforms the other ten models across all the four emotion categories. Specifically, the SVR model achieved an average relative reduction of 8% in terms of root mean square error (RMSE throughout all emotional categories.
Data correction for seven activity trackers based on regression models.
Andalibi, Vafa; Honko, Harri; Christophe, Francois; Viik, Jari
2015-08-01
Using an activity tracker for measuring activity-related parameters, e.g. steps and energy expenditure (EE), can be very helpful in assisting a person's fitness improvement. Unlike the measuring of number of steps, an accurate EE estimation requires additional personal information as well as accurate velocity of movement, which is hard to achieve due to inaccuracy of sensors. In this paper, we have evaluated regression-based models to improve the precision for both steps and EE estimation. For this purpose, data of seven activity trackers and two reference devices was collected from 20 young adult volunteers wearing all devices at once in three different tests, namely 60-minute office work, 6-hour overall activity and 60-minute walking. Reference data is used to create regression models for each device and relative percentage errors of adjusted values are then statistically compared to that of original values. The effectiveness of regression models are determined based on the result of a statistical test. During a walking period, EE measurement was improved in all devices. The step measurement was also improved in five of them. The results show that improvement of EE estimation is possible only with low-cost implementation of fitting model over the collected data e.g. in the app or in corresponding service back-end.
Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad
2015-11-01
One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.
Forecasting relativistic electron flux using dynamic multiple regression models
Directory of Open Access Journals (Sweden)
H.-L. Wei
2011-02-01
Full Text Available The forecast of high energy electron fluxes in the radiation belts is important because the exposure of modern spacecraft to high energy particles can result in significant damage to onboard systems. A comprehensive physical model of processes related to electron energisation that can be used for such a forecast has not yet been developed. In the present paper a systems identification approach is exploited to deduce a dynamic multiple regression model that can be used to predict the daily maximum of high energy electron fluxes at geosynchronous orbit from data. It is shown that the model developed provides reliable predictions.
Resampling procedures to validate dendro-auxometric regression models
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available Regression analysis has a large use in several sectors of forest research. The validation of a dendro-auxometric model is a basic step in the building of the model itself. The more a model resists to attempts of demonstrating its groundlessness, the more its reliability increases. In the last decades many new theories, that quite utilizes the calculation speed of the calculators, have been formulated. Here we show the results obtained by the application of a bootsprap resampling procedure as a validation tool.
Bootstrap Estimation for Nonparametric Efficiency Estimates
1995-01-01
This paper develops a consistent bootstrap estimation procedure to obtain confidence intervals for nonparametric measures of productive efficiency. Although the methodology is illustrated in terms of technical efficiency measured by output distance functions, the technique can be easily extended to other consistent nonparametric frontier models. Variation in estimated efficiency scores is assumed to result from variation in empirical approximations to the true boundary of the production set. ...
Two-step variable selection in quantile regression models
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FAN Yali
2015-06-01
Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions,in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform l1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.
Fuzzy and Regression Modelling of Hard Milling Process
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A. Tamilarasan
2014-04-01
Full Text Available The present study highlights the application of box-behnken design coupled with fuzzy and regression modeling approach for making expert system in hard milling process to improve the process performance with systematic reduction of production cost. The important input fields of work piece hardness, nose radius, feed per tooth, radial depth of cut and axial depth cut were considered. The cutting forces, work surface temperature and sound pressure level were identified as key index of machining outputs. The results indicate that the fuzzy logic and regression modeling technique can be effectively used for the prediction of desired responses with less average error variation. Predicted results were verified by experiments and shown the good potential characteristics of the developed system for automated machining environment.
Regression Cloud Models and Their Applications in Energy Consumption of Data Center
Directory of Open Access Journals (Sweden)
Yanshuang Zhou
2015-01-01
Full Text Available As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.
Central limit theorem of linear regression model under right censorship
Institute of Scientific and Technical Information of China (English)
HE; Shuyuan(何书元); HUANG; Xiang(Heung; Wong)(黄香)
2003-01-01
In this paper, the estimation of joint distribution F(y,z) of (Y, Z) and the estimation in thelinear regression model Y = b′Z + ε for complete data are extended to that of the right censored data. Theregression parameter estimates of b and the variance of ε are weighted least square estimates with randomweights. The central limit theorems of the estimators are obtained under very weak conditions and the derivedasymptotic variance has a very simple form.
APPLYING LOGISTIC REGRESSION MODEL TO THE EXAMINATION RESULTS DATA
Directory of Open Access Journals (Sweden)
Goutam Saha
2011-01-01
Full Text Available The binary logistic regression model is used to analyze the school examination results(scores of 1002 students. The analysis is performed on the basis of the independent variables viz.gender, medium of instruction, type of schools, category of schools, board of examinations andlocation of schools, where scores or marks are assumed to be dependent variables. The odds ratioanalysis compares the scores obtained in two examinations viz. matriculation and highersecondary.
Predicting and Modelling of Survival Data when Cox's Regression Model does not hold
DEFF Research Database (Denmark)
Scheike, Thomas H.; Zhang, Mei-Jie
2002-01-01
Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...
GAUSSIAN COPULA MARGINAL REGRESSION FOR MODELING EXTREME DATA WITH APPLICATION
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Sutikno
2014-01-01
Full Text Available Regression is commonly used to determine the relationship between the response variable and the predictor variable, where the parameters are estimated by Ordinary Least Square (OLS. This method can be used with an assumption that residuals are normally distributed (0, σ^{2}. However, the assumption of normality of the data is often violated due to extreme observations, which are often found in the climate data. Modeling of rice harvested area with rainfall predictor variables allows extreme observations. Therefore, another approximation is necessary to be applied in order to overcome the presence of extreme observations. The method used to solve this problem is a Gaussian Copula Marginal Regression (GCMR, the regression-based Copula. As a case study, the method is applied to model rice harvested area of rice production centers in East Java, Indonesia, covering District: Banyuwangi, Lamongan, Bojonegoro, Ngawi and Jember. Copula is chosen because this method is not strict against the assumption distribution, especially the normal distribution. Moreover, this method can describe dependency on extreme point clearly. The GCMR performance will be compared with OLS and Generalized Linear Models (GLM. The identification result of the dependencies structure between the Rice Harvest per period (RH and monthly rainfall showed a dependency in all areas of research. It is shown that the real test copula type mostly follows the Gumbel distribution. While the comparison of the model goodness for rice harvested area in the modeling showed that the method used to model the exact GCMR in five districts RH1 and RH2 in Jember district since its lowest AICc. Looking at the data distribution pattern of response variables, it can be concluded that the GCMR good for modeling the response variable that is not normally distributed and tend to have a large skew.
Online Statistical Modeling (Regression Analysis) for Independent Responses
Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus
2017-06-01
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
Nonparametric statistical methods
Hollander, Myles; Chicken, Eric
2013-01-01
Praise for the Second Edition"This book should be an essential part of the personal library of every practicing statistician."-Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given sit
DEFF Research Database (Denmark)
Klein, John P.; Andersen, Per Kragh
2005-01-01
Bone marrow transplantation; Generalized estimating equations; Jackknife statistics; Regression models......Bone marrow transplantation; Generalized estimating equations; Jackknife statistics; Regression models...
K factor estimation in distribution transformers using linear regression models
Directory of Open Access Journals (Sweden)
Juan Miguel Astorga Gómez
2016-06-01
Full Text Available Background: Due to massive incorporation of electronic equipment to distribution systems, distribution transformers are subject to operation conditions other than the design ones, because of the circulation of harmonic currents. It is necessary to quantify the effect produced by these harmonic currents to determine the capacity of the transformer to withstand these new operating conditions. The K-factor is an indicator that estimates the ability of a transformer to withstand the thermal effects caused by harmonic currents. This article presents a linear regression model to estimate the value of the K-factor, from total current harmonic content obtained with low-cost equipment.Method: Two distribution transformers that feed different loads are studied variables, current total harmonic distortion factor K are recorded, and the regression model that best fits the data field is determined. To select the regression model the coefficient of determination R2 and the Akaike Information Criterion (AIC are used. With the selected model, the K-factor is estimated to actual operating conditions.Results: Once determined the model it was found that for both agricultural cargo and industrial mining, present harmonic content (THDi exceeds the values that these transformers can drive (average of 12.54% and minimum 8,90% in the case of agriculture and average value of 18.53% and a minimum of 6.80%, for industrial mining case.Conclusions: When estimating the K factor using polynomial models it was determined that studied transformers can not withstand the current total harmonic distortion of their current loads. The appropriate K factor for studied transformer should be 4; this allows transformers support the current total harmonic distortion of their respective loads.
Extended cox regression model: The choice of timefunction
Isik, Hatice; Tutkun, Nihal Ata; Karasoy, Durdu
2017-07-01
Cox regression model (CRM), which takes into account the effect of censored observations, is one the most applicative and usedmodels in survival analysis to evaluate the effects of covariates. Proportional hazard (PH), requires a constant hazard ratio over time, is the assumptionofCRM. Using extended CRM provides the test of including a time dependent covariate to assess the PH assumption or an alternative model in case of nonproportional hazards. In this study, the different types of real data sets are used to choose the time function and the differences between time functions are analyzed and discussed.
A New Approach in Regression Analysis for Modeling Adsorption Isotherms
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Dana D. Marković
2014-01-01
Full Text Available Numerous regression approaches to isotherm parameters estimation appear in the literature. The real insight into the proper modeling pattern can be achieved only by testing methods on a very big number of cases. Experimentally, it cannot be done in a reasonable time, so the Monte Carlo simulation method was applied. The objective of this paper is to introduce and compare numerical approaches that involve different levels of knowledge about the noise structure of the analytical method used for initial and equilibrium concentration determination. Six levels of homoscedastic noise and five types of heteroscedastic noise precision models were considered. Performance of the methods was statistically evaluated based on median percentage error and mean absolute relative error in parameter estimates. The present study showed a clear distinction between two cases. When equilibrium experiments are performed only once, for the homoscedastic case, the winning error function is ordinary least squares, while for the case of heteroscedastic noise the use of orthogonal distance regression or Margart’s percent standard deviation is suggested. It was found that in case when experiments are repeated three times the simple method of weighted least squares performed as well as more complicated orthogonal distance regression method.
A Bayesian Nonparametric Approach to Test Equating
Karabatsos, George; Walker, Stephen G.
2009-01-01
A Bayesian nonparametric model is introduced for score equating. It is applicable to all major equating designs, and has advantages over previous equating models. Unlike the previous models, the Bayesian model accounts for positive dependence between distributions of scores from two tests. The Bayesian model and the previous equating models are…
Regularized multivariate regression models with skew-t error distributions
Chen, Lianfu
2014-06-01
We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both the regression coefficient and inverse scale matrices simultaneously. The sparsity is introduced through penalizing the negative log-likelihood by adding L1-penalties on the entries of the two matrices. Taking advantage of the hierarchical representation of skew-t distributions, and using the expectation conditional maximization (ECM) algorithm, we reduce the problem to penalized normal likelihood and develop a procedure to minimize the ensuing objective function. Using a simulation study the performance of the method is assessed, and the methodology is illustrated using a real data set with a 24-dimensional response vector. © 2014 Elsevier B.V.
Modeling the number of car theft using Poisson regression
Zulkifli, Malina; Ling, Agnes Beh Yen; Kasim, Maznah Mat; Ismail, Noriszura
2016-10-01
Regression analysis is the most popular statistical methods used to express the relationship between the variables of response with the covariates. The aim of this paper is to evaluate the factors that influence the number of car theft using Poisson regression model. This paper will focus on the number of car thefts that occurred in districts in Peninsular Malaysia. There are two groups of factor that have been considered, namely district descriptive factors and socio and demographic factors. The result of the study showed that Bumiputera composition, Chinese composition, Other ethnic composition, foreign migration, number of residence with the age between 25 to 64, number of employed person and number of unemployed person are the most influence factors that affect the car theft cases. These information are very useful for the law enforcement department, insurance company and car owners in order to reduce and limiting the car theft cases in Peninsular Malaysia.
Panel data nonparametric estimation of production risk and risk preferences
DEFF Research Database (Denmark)
Czekaj, Tomasz Gerard; Henningsen, Arne
We apply nonparametric panel data kernel regression to investigate production risk, out-put price uncertainty, and risk attitudes of Polish dairy farms based on a firm-level unbalanced panel data set that covers the period 2004–2010. We compare different model specifications and different...... approaches for obtaining firm-specific measures of risk attitudes. We found that Polish dairy farmers are risk averse regarding production risk and price uncertainty. According to our results, Polish dairy farmers perceive the production risk as being more significant than the risk related to output price...
Interpreting parameters in the logistic regression model with random effects
DEFF Research Database (Denmark)
Larsen, Klaus; Petersen, Jørgen Holm; Budtz-Jørgensen, Esben
2000-01-01
interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects......interpretation, interval odds ratio, logistic regression, median odds ratio, normally distributed random effects...
Dynamic Regression Intervention Modeling for the Malaysian Daily Load
Directory of Open Access Journals (Sweden)
Fadhilah Abdrazak
2014-05-01
Full Text Available Malaysia is a unique country due to having both fixed and moving holidays. These moving holidays may overlap with other fixed holidays and therefore, increase the complexity of the load forecasting activities. The errors due to holidays’ effects in the load forecasting are known to be higher than other factors. If these effects can be estimated and removed, the behavior of the series could be better viewed. Thus, the aim of this paper is to improve the forecasting errors by using a dynamic regression model with intervention analysis. Based on the linear transfer function method, a daily load model consists of either peak or average is developed. The developed model outperformed the seasonal ARIMA model in estimating the fixed and moving holidays’ effects and achieved a smaller Mean Absolute Percentage Error (MAPE in load forecast.
Modeling of the Monthly Rainfall-Runoff Process Through Regressions
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Campos-Aranda Daniel Francisco
2014-10-01
Full Text Available To solve the problems associated with the assessment of water resources of a river, the modeling of the rainfall-runoff process (RRP allows the deduction of runoff missing data and to extend its record, since generally the information available on precipitation is larger. It also enables the estimation of inputs to reservoirs, when their building led to the suppression of the gauging station. The simplest mathematical model that can be set for the RRP is the linear regression or curve on a monthly basis. Such a model is described in detail and is calibrated with the simultaneous record of monthly rainfall and runoff in Ballesmi hydrometric station, which covers 35 years. Since the runoff of this station has an important contribution from the spring discharge, the record is corrected first by removing that contribution. In order to do this a procedure was developed based either on the monthly average regional runoff coefficients or on nearby and similar watershed; in this case the Tancuilín gauging station was used. Both stations belong to the Partial Hydrologic Region No. 26 (Lower Rio Panuco and are located within the state of San Luis Potosi, México. The study performed indicates that the monthly regression model, due to its conceptual approach, faithfully reproduces monthly average runoff volumes and achieves an excellent approximation in relation to the dispersion, proved by calculation of the means and standard deviations.
Mixed-model Regression for Variable-star Photometry
Dose, Eric
2016-05-01
Mixed-model regression, a recent advance from social-science statistics, applies directly to reducing one night's photometric raw data, especially for variable stars in fields with multiple comparison stars. One regression model per filter/passband yields any or all of: transform values, extinction values, nightly zero-points, rapid zero-point fluctuations ("cirrus effect"), ensemble comparisons, vignette and gradient removal arising from incomplete flat-correction, check-star and target-star magnitudes, and specific indications of unusually large catalog magnitude errors. When images from several different fields of view are included, the models improve without complicating the calculations. The mixed-model approach is generally robust to outliers and missing data points, and it directly yields 14 diagnostic plots, used to monitor data set quality and/or residual systematic errors - these diagnostic plots may in fact turn out to be the prime advantage of this approach. Also presented is initial work on a split-annulus approach to sky background estimation, intended to address the sensitivity of photometric observations to noise within the sky-background annulus.
Genetic evaluation of European quails by random regression models
Directory of Open Access Journals (Sweden)
Flaviana Miranda Gonçalves
2012-09-01
Full Text Available The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (covariance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6 to determine which model had the best fit to describe the (covariance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT, the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day to 0.16 (42 days. Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.
Fuzzy regression modeling for tool performance prediction and degradation detection.
Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L
2010-10-01
In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.
A hybrid neural network model for noisy data regression.
Lee, Eric W M; Lim, Chee Peng; Yuen, Richard K K; Lo, S M
2004-04-01
A hybrid neural network model, based on the fusion of fuzzy adaptive resonance theory (FA ART) and the general regression neural network (GRNN), is proposed in this paper. Both FA and the GRNN are incremental learning systems and are very fast in network training. The proposed hybrid model, denoted as GRNNFA, is able to retain these advantages and, at the same time, to reduce the computational requirements in calculating and storing information of the kernels. A clustering version of the GRNN is designed with data compression by FA for noise removal. An adaptive gradient-based kernel width optimization algorithm has also been devised. Convergence of the gradient descent algorithm can be accelerated by the geometric incremental growth of the updating factor. A series of experiments with four benchmark datasets have been conducted to assess and compare effectiveness of GRNNFA with other approaches. The GRNNFA model is also employed in a novel application task for predicting the evacuation time of patrons at typical karaoke centers in Hong Kong in the event of fire. The results positively demonstrate the applicability of GRNNFA in noisy data regression problems.
Why preferring parametric forecasting to nonparametric methods?
Jabot, Franck
2015-05-07
A recent series of papers by Charles T. Perretti and collaborators have shown that nonparametric forecasting methods can outperform parametric methods in noisy nonlinear systems. Such a situation can arise because of two main reasons: the instability of parametric inference procedures in chaotic systems which can lead to biased parameter estimates, and the discrepancy between the real system dynamics and the modeled one, a problem that Perretti and collaborators call "the true model myth". Should ecologists go on using the demanding parametric machinery when trying to forecast the dynamics of complex ecosystems? Or should they rely on the elegant nonparametric approach that appears so promising? It will be here argued that ecological forecasting based on parametric models presents two key comparative advantages over nonparametric approaches. First, the likelihood of parametric forecasting failure can be diagnosed thanks to simple Bayesian model checking procedures. Second, when parametric forecasting is diagnosed to be reliable, forecasting uncertainty can be estimated on virtual data generated with the fitted to data parametric model. In contrast, nonparametric techniques provide forecasts with unknown reliability. This argumentation is illustrated with the simple theta-logistic model that was previously used by Perretti and collaborators to make their point. It should convince ecologists to stick to standard parametric approaches, until methods have been developed to assess the reliability of nonparametric forecasting. Copyright © 2015 Elsevier Ltd. All rights reserved.
Multivariate parametric random effect regression models for fecundability studies.
Ecochard, R; Clayton, D G
2000-12-01
Delay until conception is generally described by a mixture of geometric distributions. Weinberg and Gladen (1986, Biometrics 42, 547-560) proposed a regression generalization of the beta-geometric mixture model where covariates effects were expressed in terms of contrasts of marginal hazards. Scheike and Jensen (1997, Biometrics 53, 318-329) developed a frailty model for discrete event times data based on discrete-time analogues of Hougaard's results (1984, Biometrika 71, 75-83). This paper is on a generalization to a three-parameter family distribution and an extension to multivariate cases. The model allows the introduction of explanatory variables, including time-dependent variables at the subject-specific level, together with a choice from a flexible family of random effect distributions. This makes it possible, in the context of medically assisted conception, to include data sources with multiple pregnancies (or attempts at pregnancy) per couple.
Ciupak, Maurycy; Ozga-Zielinski, Bogdan; Adamowski, Jan; Quilty, John; Khalil, Bahaa
2015-11-01
A novel implementation of Dynamic Linear Bayesian Models (DLBM), using either a Varying Coefficient Regression (VCR) or a Discount Weighted Regression (DWR) algorithm was used in the hydrological modeling of annual hydrographs as well as 1-, 2-, and 3-day lead time stream flow forecasting. Using hydrological data (daily discharge, rainfall, and mean, maximum and minimum air temperatures) from the Upper Narew River watershed in Poland, the forecasting performance of DLBM was compared to that of traditional multiple linear regression (MLR) and more recent artificial neural network (ANN) based models. Model performance was ranked DLBM-DWR > DLBM-VCR > MLR > ANN for both annual hydrograph modeling and 1-, 2-, and 3-day lead forecasting, indicating that the DWR and VCR algorithms, operating in a DLBM framework, represent promising new methods for both annual hydrograph modeling and short-term stream flow forecasting.
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.
Regression Models for Predicting Force Coefficients of Aerofoils
Directory of Open Access Journals (Sweden)
Mohammed ABDUL AKBAR
2015-09-01
Full Text Available Renewable sources of energy are attractive and advantageous in a lot of different ways. Among the renewable energy sources, wind energy is the fastest growing type. Among wind energy converters, Vertical axis wind turbines (VAWTs have received renewed interest in the past decade due to some of the advantages they possess over their horizontal axis counterparts. VAWTs have evolved into complex 3-D shapes. A key component in predicting the output of VAWTs through analytical studies is obtaining the values of lift and drag coefficients which is a function of shape of the aerofoil, ‘angle of attack’ of wind and Reynolds’s number of flow. Sandia National Laboratories have carried out extensive experiments on aerofoils for the Reynolds number in the range of those experienced by VAWTs. The volume of experimental data thus obtained is huge. The current paper discusses three Regression analysis models developed wherein lift and drag coefficients can be found out using simple formula without having to deal with the bulk of the data. Drag coefficients and Lift coefficients were being successfully estimated by regression models with R2 values as high as 0.98.
Approximation by randomly weighting method in censored regression model
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Censored regression ("Tobit") models have been in common use, and their linear hypothesis testings have been widely studied. However, the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters. In this paper, we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic. It is shown that, under both the null and local alternative hypotheses, conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic. Therefore, the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters. At the same time, we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model. Simulation studies illustrate that the per-formance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.
Approximation by randomly weighting method in censored regression model
Institute of Scientific and Technical Information of China (English)
WANG ZhanFeng; WU YaoHua; ZHAO LinCheng
2009-01-01
Censored regression ("Tobit") models have been in common use,and their linear hypothesis testings have been widely studied.However,the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters.In this paper,we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic.It is shown that,under both the null and local alternative hypotheses,conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic.Therefore,the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters.At the same time,we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model.Simulation studies illustrate that the performance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.
Remodeling and Estimation for Sparse Partially Linear Regression Models
Directory of Open Access Journals (Sweden)
Yunhui Zeng
2013-01-01
Full Text Available When the dimension of covariates in the regression model is high, one usually uses a submodel as a working model that contains significant variables. But it may be highly biased and the resulting estimator of the parameter of interest may be very poor when the coefficients of removed variables are not exactly zero. In this paper, based on the selected submodel, we introduce a two-stage remodeling method to get the consistent estimator for the parameter of interest. More precisely, in the first stage, by a multistep adjustment, we reconstruct an unbiased model based on the correlation information between the covariates; in the second stage, we further reduce the adjusted model by a semiparametric variable selection method and get a new estimator of the parameter of interest simultaneously. Its convergence rate and asymptotic normality are also obtained. The simulation results further illustrate that the new estimator outperforms those obtained by the submodel and the full model in the sense of mean square errors of point estimation and mean square prediction errors of model prediction.
A non-parametric framework for estimating threshold limit values
Directory of Open Access Journals (Sweden)
Ulm Kurt
2005-11-01
Full Text Available Abstract Background To estimate a threshold limit value for a compound known to have harmful health effects, an 'elbow' threshold model is usually applied. We are interested on non-parametric flexible alternatives. Methods We describe how a step function model fitted by isotonic regression can be used to estimate threshold limit values. This method returns a set of candidate locations, and we discuss two algorithms to select the threshold among them: the reduced isotonic regression and an algorithm considering the closed family of hypotheses. We assess the performance of these two alternative approaches under different scenarios in a simulation study. We illustrate the framework by analysing the data from a study conducted by the German Research Foundation aiming to set a threshold limit value in the exposure to total dust at workplace, as a causal agent for developing chronic bronchitis. Results In the paper we demonstrate the use and the properties of the proposed methodology along with the results from an application. The method appears to detect the threshold with satisfactory success. However, its performance can be compromised by the low power to reject the constant risk assumption when the true dose-response relationship is weak. Conclusion The estimation of thresholds based on isotonic framework is conceptually simple and sufficiently powerful. Given that in threshold value estimation context there is not a gold standard method, the proposed model provides a useful non-parametric alternative to the standard approaches and can corroborate or challenge their findings.
Information Criteria for Deciding between Normal Regression Models
Maier, Robert S
2013-01-01
Regression models fitted to data can be assessed on their goodness of fit, though models with many parameters should be disfavored to prevent over-fitting. Statisticians' tools for this are little known to physical scientists. These include the Akaike Information Criterion (AIC), a penalized goodness-of-fit statistic, and the AICc, a variant including a small-sample correction. They entered the physical sciences through being used by astrophysicists to compare cosmological models; e.g., predictions of the distance-redshift relation. The AICc is shown to have been misapplied, being applicable only if error variances are unknown. If error bars accompany the data, the AIC should be used instead. Erroneous applications of the AICc are listed in an appendix. It is also shown how the variability of the AIC difference between models with a known error variance can be estimated. This yields a significance test that can potentially replace the use of `Akaike weights' for deciding between such models. Additionally, the...
THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE.
Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan
2015-10-01
The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran's universities. This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran's public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran's libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries.
THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE
Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan
2015-01-01
Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran’s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran’s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For predicting the organizational climate pattern of the libraries is used from the multivariate linear regression and track diagram. Results: of the 9 variables affecting organizational climate, 5 variables of innovation, teamwork, customer service, psychological safety and deep diversity play a major role in prediction of the organizational climate of Iran’s libraries. The results also indicate that each of these variables with different coefficient have the power to predict organizational climate but the climate score of psychological safety (0.94) plays a very crucial role in predicting the organizational climate. Track diagram showed that five variables of teamwork, customer service, psychological safety, deep diversity and innovation directly effects on the organizational climate variable that contribution of the team work from this influence is more than any other variables. Conclusions: Of the indicator of the organizational climate of climateQual, the contribution of the team work from this influence is more than any other variables that reinforcement of teamwork in academic libraries can be more effective in improving the organizational climate of this type libraries. PMID:26622203
A Gompertz regression model for fern spores germination
Directory of Open Access Journals (Sweden)
Gabriel y Galán, Jose María
2015-06-01
Full Text Available Germination is one of the most important biological processes for both seed and spore plants, also for fungi. At present, mathematical models of germination have been developed in fungi, bryophytes and several plant species. However, ferns are the only group whose germination has never been modelled. In this work we develop a regression model of the germination of fern spores. We have found that for Blechnum serrulatum, Blechnum yungense, Cheilanthes pilosa, Niphidium macbridei and Polypodium feuillei species the Gompertz growth model describe satisfactorily cumulative germination. An important result is that regression parameters are independent of fern species and the model is not affected by intraspecific variation. Our results show that the Gompertz curve represents a general germination model for all the non-green spore leptosporangiate ferns, including in the paper a discussion about the physiological and ecological meaning of the model.La germinación es uno de los procesos biológicos más relevantes tanto para las plantas con esporas, como para las plantas con semillas y los hongos. Hasta el momento, se han desarrollado modelos de germinación para hongos, briofitos y diversas especies de espermatófitos. Los helechos son el único grupo de plantas cuya germinación nunca ha sido modelizada. En este trabajo se desarrolla un modelo de regresión para explicar la germinación de las esporas de helechos. Observamos que para las especies Blechnum serrulatum, Blechnum yungense, Cheilanthes pilosa, Niphidium macbridei y Polypodium feuillei el modelo de crecimiento de Gompertz describe satisfactoriamente la germinación acumulativa. Un importante resultado es que los parámetros de la regresión son independientes de la especie y que el modelo no está afectado por variación intraespecífica. Por lo tanto, los resultados del trabajo muestran que la curva de Gompertz puede representar un modelo general para todos los helechos leptosporangiados
Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing
Stinstra, E.; Rennen, G.; Teeuwen, G.J.A.
2006-01-01
The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval arithm
Yu, Yang; Li, Yancheng; Li, Jianchun
2015-03-01
Due to its inherent hysteretic characteristics, the main challenge for the application of a magnetorheological elastomer- (MRE) based isolator is the exploitation of the accurate model, which could fully describe its unique behaviour. This paper proposes a nonparametric model for a MRE-based isolator based on support vector regression (SVR). The trained identification model is to forecast the shear force of the MRE-based isolator online; thus, the dynamic response from the MRE-based isolator can be well captured. In order to improve the forecast capacity of the model, a type of improved particle swarm optimization (IPSO) is employed to optimize the parameters in SVR. Eventually, the trained model is applied to the MRE-based isolator modelling with testing data. The results indicate that the proposed hybrid model has a better generalization capacity and better recognition accuracy than other conventional models, and it is an effective and suitable approach for forecasting the behaviours of a MRE-based isolator.
Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.
Ferrari, Alberto
2017-02-16
Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.
A nonlinear regression model-based predictive control algorithm.
Dubay, R; Abu-Ayyad, M; Hernandez, J M
2009-04-01
This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.
Statistical Inference for Partially Linear Regression Models with Measurement Errors
Institute of Scientific and Technical Information of China (English)
Jinhong YOU; Qinfeng XU; Bin ZHOU
2008-01-01
In this paper, the authors investigate three aspects of statistical inference for the partially linear regression models where some covariates are measured with errors. Firstly,a bandwidth selection procedure is proposed, which is a combination of the difference-based technique and GCV method. Secondly, a goodness-of-fit test procedure is proposed,which is an extension of the generalized likelihood technique. Thirdly, a variable selection procedure for the parametric part is provided based on the nonconcave penalization and corrected profile least squares. Same as "Variable selection via nonconcave penalized like-lihood and its oracle properties" (J. Amer. Statist. Assoc., 96, 2001, 1348-1360), it is shown that the resulting estimator has an oracle property with a proper choice of regu-larization parameters and penalty function. Simulation studies are conducted to illustrate the finite sample performances of the proposed procedures.
Projection-type estimation for varying coefficient regression models
Lee, Young K; Park, Byeong U; 10.3150/10-BEJ331
2012-01-01
In this paper we introduce new estimators of the coefficient functions in the varying coefficient regression model. The proposed estimators are obtained by projecting the vector of the full-dimensional kernel-weighted local polynomial estimators of the coefficient functions onto a Hilbert space with a suitable norm. We provide a backfitting algorithm to compute the estimators. We show that the algorithm converges at a geometric rate under weak conditions. We derive the asymptotic distributions of the estimators and show that the estimators have the oracle properties. This is done for the general order of local polynomial fitting and for the estimation of the derivatives of the coefficient functions, as well as the coefficient functions themselves. The estimators turn out to have several theoretical and numerical advantages over the marginal integration estimators studied by Yang, Park, Xue and H\\"{a}rdle [J. Amer. Statist. Assoc. 101 (2006) 1212--1227].
The R Package threg to Implement Threshold Regression Models
Directory of Open Access Journals (Sweden)
Tao Xiao
2015-08-01
This new package includes four functions: threg, and the methods hr, predict and plot for threg objects returned by threg. The threg function is the model-fitting function which is used to calculate regression coefficient estimates, asymptotic standard errors and p values. The hr method for threg objects is the hazard-ratio calculation function which provides the estimates of hazard ratios at selected time points for specified scenarios (based on given categories or value settings of covariates. The predict method for threg objects is used for prediction. And the plot method for threg objects provides plots for curves of estimated hazard functions, survival functions and probability density functions of the first-hitting-time; function curves corresponding to different scenarios can be overlaid in the same plot for comparison to give additional research insights.
Epistasis analysis for quantitative traits by functional regression model.
Zhang, Futao; Boerwinkle, Eric; Xiong, Momiao
2014-06-01
The critical barrier in interaction analysis for rare variants is that most traditional statistical methods for testing interactions were originally designed for testing the interaction between common variants and are difficult to apply to rare variants because of their prohibitive computational time and poor ability. The great challenges for successful detection of interactions with next-generation sequencing (NGS) data are (1) lack of methods for interaction analysis with rare variants, (2) severe multiple testing, and (3) time-consuming computations. To meet these challenges, we shift the paradigm of interaction analysis between two loci to interaction analysis between two sets of loci or genomic regions and collectively test interactions between all possible pairs of SNPs within two genomic regions. In other words, we take a genome region as a basic unit of interaction analysis and use high-dimensional data reduction and functional data analysis techniques to develop a novel functional regression model to collectively test interactions between all possible pairs of single nucleotide polymorphisms (SNPs) within two genome regions. By intensive simulations, we demonstrate that the functional regression models for interaction analysis of the quantitative trait have the correct type 1 error rates and a much better ability to detect interactions than the current pairwise interaction analysis. The proposed method was applied to exome sequence data from the NHLBI's Exome Sequencing Project (ESP) and CHARGE-S study. We discovered 27 pairs of genes showing significant interactions after applying the Bonferroni correction (P-values < 4.58 × 10(-10)) in the ESP, and 11 were replicated in the CHARGE-S study.
Modeling Pan Evaporation for Kuwait by Multiple Linear Regression
Directory of Open Access Journals (Sweden)
Jaber Almedeij
2012-01-01
Full Text Available Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values.
The microcomputer scientific software series 2: general linear model--regression.
Harold M. Rauscher
1983-01-01
The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...
Air Pollution Analysis using Ontologies and Regression Models
Directory of Open Access Journals (Sweden)
Parul Choudhary
2016-07-01
Full Text Available Rapidly throughout the world economy, "the expansive Web" in the "world" explosive growth, rapidly growing market characterized by short product cycles exists and the demand for increased flexibility as well as the extensive use of a new data vision managed data society. A new socio-economic system that relies more and more on movement and allocation results in data whose daily existence, refinement, economy and adjust the exchange industry. Cooperative Engineering Co -operation and multi -disciplinary installed on people's cooperation is a good example. Semantic Web is a new form of Web content that is meaningful to computers and additional approved another example. Communication, vision sharing and exchanging data Society's are new commercial bet. Urban air pollution modeling and data processing techniques need elevated Association. Artificial intelligence in countless ways and breakthrough technologies can solve environmental problems from uneven offers. A method for data to formal ontology means a true meaning and lack of ambiguity to allow us to portray memo. In this work we survey regression model for ontologies and air pollution.
Nonparametric statistical inference
Gibbons, Jean Dickinson
2014-01-01
Thoroughly revised and reorganized, the fourth edition presents in-depth coverage of the theory and methods of the most widely used nonparametric procedures in statistical analysis and offers example applications appropriate for all areas of the social, behavioral, and life sciences. The book presents new material on the quantiles, the calculation of exact and simulated power, multiple comparisons, additional goodness-of-fit tests, methods of analysis of count data, and modern computer applications using MINITAB, SAS, and STATXACT. It includes tabular guides for simplified applications of tests and finding P values and confidence interval estimates.
Song, Chao; Kwan, Mei-Po; Zhu, Jiping
2017-04-08
An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.
The Nonparametric Estimate of Exponential Premium Under Collective Risk Models%聚合风险模型下指数保费的非参数估计
Institute of Scientific and Technical Information of China (English)
张林娜; 温利民; 方婧
2016-01-01
The exponential premium principle is one of the most important premium principles and is wide ‐ly applied in non‐life insurance actuarial science .In this paper ,the nonparametric estimate of exponential premium is investigated under collective risk models .In addition ,the estimator is proved strongly consist‐ent and asymptotically normal .Finally ,a numerical simulation method is used to verify the estimated speed of convergence ,and the asymptotic normality of the estimator is checked in the simulations .%在聚合风险模型的假设下，研究了聚合风险下指数保费的非参数估计，证明了估计的强相合性和渐近正态性。最后通过数值模拟的方法验证了估计的收敛速度及渐近正态性。
MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION
Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...
Correlation-regression model for physico-chemical quality of ...
African Journals Online (AJOL)
abusaad
Key words: Groundwater, water quality, bore well, water supply, correlation, regression. INTRODUCTION ..... interpreting groundwater quality data and relating them to specific hydro ..... Regional trends in nitrate content of Texas groundwater.
Thirty years of nonparametric item response theory
Molenaar, W.
2001-01-01
Relationships between a mathematical measurement model and its real-world applications are discussed. A distinction is made between large data matrices commonly found in educational measurement and smaller matrices found in attitude and personality measurement. Nonparametric methods are evaluated fo
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the trea
Decompounding random sums: A nonparametric approach
DEFF Research Database (Denmark)
Hansen, Martin Bøgsted; Pitts, Susan M.
review a number of applications and consider the nonlinear inverse problem of inferring the cumulative distribution function of the components in the random sum. We review the existing literature on non-parametric approaches to the problem. The models amenable to the analysis are generalized considerably...
Nonparametric confidence intervals for monotone functions
Groeneboom, P.; Jongbloed, G.
2015-01-01
We study nonparametric isotonic confidence intervals for monotone functions. In [Ann. Statist. 29 (2001) 1699–1731], pointwise confidence intervals, based on likelihood ratio tests using the restricted and unrestricted MLE in the current status model, are introduced. We extend the method to the
How Are Teachers Teaching? A Nonparametric Approach
De Witte, Kristof; Van Klaveren, Chris
2014-01-01
This paper examines which configuration of teaching activities maximizes student performance. For this purpose a nonparametric efficiency model is formulated that accounts for (1) self-selection of students and teachers in better schools and (2) complementary teaching activities. The analysis distinguishes both individual teaching (i.e., a…
Regression of retinopathy by squalamine in a mouse model.
Higgins, Rosemary D; Yan, Yun; Geng, Yixun; Zasloff, Michael; Williams, Jon I
2004-07-01
The goal of this study was to determine whether an antiangiogenic agent, squalamine, given late during the evolution of oxygen-induced retinopathy (OIR) in the mouse, could improve retinal neovascularization. OIR was induced in neonatal C57BL6 mice and the neonates were treated s.c. with squalamine doses begun at various times after OIR induction. A system of retinal whole mounts and assessment of neovascular nuclei extending beyond the inner limiting membrane from animals reared under room air or OIR conditions and killed periodically from d 12 to 21 were used to assess retinopathy in squalamine-treated and untreated animals. OIR evolved after 75% oxygen exposure in neonatal mice with florid retinal neovascularization developing by d 14. Squalamine (single dose, 25 mg/kg s.c.) given on d 15 or 16, but not d 17, substantially improved retinal neovascularization in the mouse model of OIR. There was improvement seen in the degree of blood vessel tuft formation, blood vessel tortuosity, and central vasoconstriction with squalamine treatment at d 15 or 16. Single-dose squalamine at d 12 was effective at reducing subsequent development of retinal neovascularization at doses as low as 1 mg/kg. Squalamine is a very active inhibitor of OIR in mouse neonates at doses as low as 1 mg/kg given once. Further, squalamine given late in the course of OIR improves retinopathy by inducing regression of retinal neovessels and abrogating invasion of new vessels beyond the inner-limiting membrane of the retina.
Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model
DEFF Research Database (Denmark)
Møller, Niels Framroze
This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its...
Regression model for tuning the PID controller with fractional order time delay system
S.P. Agnihotri; Laxman Madhavrao Waghmare
2014-01-01
In this paper a regression model based for tuning proportional integral derivative (PID) controller with fractional order time delay system is proposed. The novelty of this paper is that tuning parameters of the fractional order time delay system are optimally predicted using the regression model. In the proposed method, the output parameters of the fractional order system are used to derive the regression function. Here, the regression model depends on the weights of the exponential function...
Gu, Fei; Preacher, Kristopher J; Wu, Wei; Yung, Yiu-Fai
2014-01-01
Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.
Non-parametric approach to the study of phenotypic stability.
Ferreira, D F; Fernandes, S B; Bruzi, A T; Ramalho, M A P
2016-02-19
The aim of this study was to undertake the theoretical derivations of non-parametric methods, which use linear regressions based on rank order, for stability analyses. These methods were extension different parametric methods used for stability analyses and the result was compared with a standard non-parametric method. Intensive computational methods (e.g., bootstrap and permutation) were applied, and data from the plant-breeding program of the Biology Department of UFLA (Minas Gerais, Brazil) were used to illustrate and compare the tests. The non-parametric stability methods were effective for the evaluation of phenotypic stability. In the presence of variance heterogeneity, the non-parametric methods exhibited greater power of discrimination when determining the phenotypic stability of genotypes.
A non-parametric approach to investigating fish population dynamics
National Research Council Canada - National Science Library
Cook, R.M; Fryer, R.J
2001-01-01
.... Using a non-parametric model for the stock-recruitment relationship it is possible to avoid defining specific functions relating recruitment to stock size while also providing a natural framework to model process error...
2nd Conference of the International Society for Nonparametric Statistics
Manteiga, Wenceslao; Romo, Juan
2016-01-01
This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in Cádiz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid. The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference. The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers...
Linear regression model selection using p-values when the model dimension grows
Pokarowski, Piotr; Teisseyre, Paweł
2012-01-01
We consider a new criterion-based approach to model selection in linear regression. Properties of selection criteria based on p-values of a likelihood ratio statistic are studied for families of linear regression models. We prove that such procedures are consistent i.e. the minimal true model is chosen with probability tending to 1 even when the number of models under consideration slowly increases with a sample size. The simulation study indicates that introduced methods perform promisingly when compared with Akaike and Bayesian Information Criteria.
Lee, Taesam; Park, Taewoong
2017-04-01
It is critical to downscale temporally coarse GCM or RCM outputs (e.g., monthly or daily) to fine time scales, such as sub-daily or hourly. Recently, a temporal downscaling model employing a nonparametric framework (NTD) with k-nearest resampling and a genetic algorithm has been developed to preserve key statistics as well as the diurnal cycle. However, this model's usage can be limited in estimating precipitation for design storms or floods because the key statistics of annual maximum precipitation (AMP), especially for longer hourly durations, present a systematic bias that cannot be preserved due to the discontinuity of multiday consecutive precipitation events in the downscaling procedure. In the current study, we develop an approach to downscale a consecutive daily precipitation at once focusing on the reproduction of AMP totals for different durations instead of day-by-day downscaling. The proposed model has been verified with the precipitation datasets for the 60 stations across South Korea over the period 1979-2005. Additionally, two validation studies were performed with the recent datasets of 2006-2014 and nearest neighbor stations. The verification and the two validation tests conclude that the population-based NTD (PNTD) model proposed in the current study is superior to the existing NTD model in preserving the key statistics of the observed AMP series and suitable for downscaling future climate scenarios.
Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William
2016-01-01
Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather
CURRENT STATUS OF NONPARAMETRIC STATISTICS
Directory of Open Access Journals (Sweden)
Orlov A. I.
2015-02-01
Full Text Available Nonparametric statistics is one of the five points of growth of applied mathematical statistics. Despite the large number of publications on specific issues of nonparametric statistics, the internal structure of this research direction has remained undeveloped. The purpose of this article is to consider its division into regions based on the existing practice of scientific activity determination of nonparametric statistics and classify investigations on nonparametric statistical methods. Nonparametric statistics allows to make statistical inference, in particular, to estimate the characteristics of the distribution and testing statistical hypotheses without, as a rule, weakly proven assumptions about the distribution function of samples included in a particular parametric family. For example, the widespread belief that the statistical data are often have the normal distribution. Meanwhile, analysis of results of observations, in particular, measurement errors, always leads to the same conclusion - in most cases the actual distribution significantly different from normal. Uncritical use of the hypothesis of normality often leads to significant errors, in areas such as rejection of outlying observation results (emissions, the statistical quality control, and in other cases. Therefore, it is advisable to use nonparametric methods, in which the distribution functions of the results of observations are imposed only weak requirements. It is usually assumed only their continuity. On the basis of generalization of numerous studies it can be stated that to date, using nonparametric methods can solve almost the same number of tasks that previously used parametric methods. Certain statements in the literature are incorrect that nonparametric methods have less power, or require larger sample sizes than parametric methods. Note that in the nonparametric statistics, as in mathematical statistics in general, there remain a number of unresolved problems
VARIABLE SELECTION BY PSEUDO WAVELETS IN HETEROSCEDASTIC REGRESSION MODELS INVOLVING TIME SERIES
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A simple but efficient method has been proposed to select variables in heteroscedastic regression models. It is shown that the pseudo empirical wavelet coefficients corresponding to the significant explanatory variables in the regression models are clearly larger than those nonsignificant ones, on the basis of which a procedure is developed to select variables in regression models. The coefficients of the models are also estimated. All estimators are proved to be consistent.
Lamont, A.E.; Vermunt, J.K.; Van Horn, M.L.
2016-01-01
Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we tested the effects of violating an implicit assumption often made in these models; that is, independent variables in the
Institute of Scientific and Technical Information of China (English)
许斌; 龚安苏; 贺佳; MASRI Sami F
2011-01-01
Structural identification has become an increasingly important topic for health monitoring and performance assessment of engineering structures. On one hand, the auto-regressive and moving average (ARMA)model has been widely employed as a typical time-domain modeling method for dynamic systems. On the other hand, with the ability of mapping any complex function and its parallel computation characteristics, neural networks-based identification method has been widely employed as a nonparametric model for civil and mechanical engineering structures even the weights and thresholds in the trained neural network model do not necessarily have clear physical meaning. The equivalence of the two representative time-domain identification methodologies was studied firstly according to the discrete solution of structural dynamic responses. Then, a novel structural parameters extraction methodology was proposed by the use of excitation and dynamic response measurement time series. Finally, the accuracy and efficiency of the proposed approach were validated via a numerical model with three degree-of-freedoms (DOFs), and via a dynamic test on a four-story model structure with impulse excitation. Results show the structural parameters can be extracted from a nonparametric model with neural networks.%参数识别是结构健康监测、性能评估的关键问题之一.作为一种代表性的动力系统时域参数化模型方法,自回归滑动平均(Auto-regressive and moving average,ARMA)模型在机械和土木工程结构的参数识别中得到了广泛应用;另一方面,尽管一般而言神经网络模型的权重和阈值并不需要具备明确的物理意义,但由于神经网络具有描述复杂函数关系的能力,作为一种非参数化模型方法在结构动力系统的建模和控制领域发挥重要作用.该文首先通过结构运动平衡方程的离散时间解,证明了非参数化神经网络模型与ARMA模型在描述线性结构动力系统的响
Directory of Open Access Journals (Sweden)
Simone Becker Lopes
2014-04-01
Full Text Available Considering the importance of spatial issues in transport planning, the main objective of this study was to analyze the results obtained from different approaches of spatial regression models. In the case of spatial autocorrelation, spatial dependence patterns should be incorporated in the models, since that dependence may affect the predictive power of these models. The results obtained with the spatial regression models were also compared with the results of a multiple linear regression model that is typically used in trips generation estimations. The findings support the hypothesis that the inclusion of spatial effects in regression models is important, since the best results were obtained with alternative models (spatial regression models or the ones with spatial variables included. This was observed in a case study carried out in the city of Porto Alegre, in the state of Rio Grande do Sul, Brazil, in the stages of specification and calibration of the models, with two distinct datasets.
First Look at Photometric Reduction via Mixed-Model Regression (Poster abstract)
Dose, E.
2016-12-01
(Abstract only) Mixed-model regression is proposed as a new approach to photometric reduction, especially for variable-star photometry in several filters. Mixed-model regression adds to normal multivariate regression certain "random effects": categorical-variable terms that model and extract specific systematic errors such as image-to-image zero-point fluctuations (cirrus effect) or even errors in comp-star catalog magnitudes.
Introduction to mixed modelling beyond regression and analysis of variance
Galwey, N W
2007-01-01
Mixed modelling is one of the most promising and exciting areas of statistical analysis, enabling more powerful interpretation of data through the recognition of random effects. However, many perceive mixed modelling as an intimidating and specialized technique.
Waller, Niels; Jones, Jeff
2011-01-01
We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…
U.S. Environmental Protection Agency — Spreadsheets are included here to support the manuscript "Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition". This...
Preference learning with evolutionary Multivariate Adaptive Regression Spline model
DEFF Research Database (Denmark)
Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll
2015-01-01
for human decision making. Learning models from pairwise preference data is however an NP-hard problem. Therefore, constructing models that can effectively learn such data is a challenging task. Models are usually constructed with accuracy being the most important factor. Another vitally important aspect...... that is usually given less attention is expressiveness, i.e. how easy it is to explain the relationship between the model input and output. Most machine learning techniques are focused either on performance or on expressiveness. This paper employ MARS models which have the advantage of being a powerful method...
Cepeda-Cuervo, Edilberto; Núñez-Antón, Vicente
2013-01-01
In this article, a proposed Bayesian extension of the generalized beta spatial regression models is applied to the analysis of the quality of education in Colombia. We briefly revise the beta distribution and describe the joint modeling approach for the mean and dispersion parameters in the spatial regression models' setting. Finally, we motivate…
von Davier, Matthias; Sinharay, Sandip
2009-01-01
This paper presents an application of a stochastic approximation EM-algorithm using a Metropolis-Hastings sampler to estimate the parameters of an item response latent regression model. Latent regression models are extensions of item response theory (IRT) to a 2-level latent variable model in which covariates serve as predictors of the…
Kleibergen, F.
2003-01-01
We obtain the prior and posterior probability of a nested regression model as the Hausdorff-integral of the prior and posterior on the parameters of an encompassing linear regression model over a lower dimensional set that represents the nested model. The invariant expression of the
Kleibergen, F.R.
2004-01-01
We obtain the prior and posterior probability of a nested regression model as the Hausdorff-integral of the prior and posterior on the parameters of an encompassing linear regression model over a lower-dimensional set that represents the nested model. The Hausdorff-integral is invariant and
A note on the maximum likelihood estimator in the gamma regression model
Directory of Open Access Journals (Sweden)
Jerzy P. Rydlewski
2009-01-01
Full Text Available This paper considers a nonlinear regression model, in which the dependent variable has the gamma distribution. A model is considered in which the shape parameter of the random variable is the sum of continuous and algebraically independent functions. The paper proves that there is exactly one maximum likelihood estimator for the gamma regression model.
Genetic parameters for various random regression models to describe the weight data of pigs
Huisman, A.E.; Veerkamp, R.F.; Arendonk, van J.A.M.
2002-01-01
Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random
Genetic parameters for different random regression models to describe weight data of pigs
Huisman, A.E.; Veerkamp, R.F.; Arendonk, van J.A.M.
2001-01-01
Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random
Cepeda-Cuervo, Edilberto; Núñez-Antón, Vicente
2013-01-01
In this article, a proposed Bayesian extension of the generalized beta spatial regression models is applied to the analysis of the quality of education in Colombia. We briefly revise the beta distribution and describe the joint modeling approach for the mean and dispersion parameters in the spatial regression models' setting. Finally, we…
Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.
Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko
2016-03-01
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.
Nonparametric Bayes analysis of social science data
Kunihama, Tsuyoshi
Social science data often contain complex characteristics that standard statistical methods fail to capture. Social surveys assign many questions to respondents, which often consist of mixed-scale variables. Each of the variables can follow a complex distribution outside parametric families and associations among variables may have more complicated structures than standard linear dependence. Therefore, it is not straightforward to develop a statistical model which can approximate structures well in the social science data. In addition, many social surveys have collected data over time and therefore we need to incorporate dynamic dependence into the models. Also, it is standard to observe massive number of missing values in the social science data. To address these challenging problems, this thesis develops flexible nonparametric Bayesian methods for the analysis of social science data. Chapter 1 briefly explains backgrounds and motivations of the projects in the following chapters. Chapter 2 develops a nonparametric Bayesian modeling of temporal dependence in large sparse contingency tables, relying on a probabilistic factorization of the joint pmf. Chapter 3 proposes nonparametric Bayes inference on conditional independence with conditional mutual information used as a measure of the strength of conditional dependence. Chapter 4 proposes a novel Bayesian density estimation method in social surveys with complex designs where there is a gap between sample and population. We correct for the bias by adjusting mixture weights in Bayesian mixture models. Chapter 5 develops a nonparametric model for mixed-scale longitudinal surveys, in which various types of variables can be induced through latent continuous variables and dynamic latent factors lead to flexibly time-varying associations among variables.
Nonparametric tests for pathwise properties of semimartingales
Cont, Rama; 10.3150/10-BEJ293
2011-01-01
We propose two nonparametric tests for investigating the pathwise properties of a signal modeled as the sum of a L\\'{e}vy process and a Brownian semimartingale. Using a nonparametric threshold estimator for the continuous component of the quadratic variation, we design a test for the presence of a continuous martingale component in the process and a test for establishing whether the jumps have finite or infinite variation, based on observations on a discrete-time grid. We evaluate the performance of our tests using simulations of various stochastic models and use the tests to investigate the fine structure of the DM/USD exchange rate fluctuations and SPX futures prices. In both cases, our tests reveal the presence of a non-zero Brownian component and a finite variation jump component.
Martins, T. M.; Alberto, J.
2015-12-01
The uncertainties of wind and solar generation patterns tends to be a critical factor in operation and expansion planning studies of electrical energy systems, as these generations are highly dependent on atmospheric variables which are difficult to predict. Traditionally, the uncertainty of renewable generation has been represented through scenarios generated by autoregressive parametric models (ARMA, PAR(p), SARIMA, etc.), that have been widely used for simulating the uncertainty of inflows and electrical demand. These methods have 3 disadvantages: (i) it is assumed that the random variables can be modelled through a known probability distribution, usually Weibull, log-normal, or normal, which are not always adequate; (ii) the temporal and spatial coupling of the represented variables are generally constructed from the Pearson Correlation, strictly requiring the hypothesis of data normality, that in the case of wind and solar generation is not met; (iii) there is an exponential increase in the model complexity due to its dimensionality. This work proposes the use of a stochastic model built from the combination of a non-parametric approach of a probability density function (the kernel density estimation method) with a dynamic Bayesian network framework. The kernel density estimation method is used to obtain the probability density function of the random variables directly from historical records, eliminating the need of choosing prior distributions. The Bayesian network allows the representation of nonlinearities in the temporal coupling of the time series, since they allow reproducing a compact probability distribution of a variable, subject to preceding stages. The proposed model was used to the generate wind power scenarios in long-term operation studies of the Brazilian Electric System, in which inflows of major rivers were also represented. The results show a considerable quality gain when compared to scenarios generated by traditional approaches.
Modeling by regression for laser cutting of quartz crystal
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Presents the theoretical models built by analysis of the mechanism of laser cutting of quartz crystal and re gression of test results for the laser cutting of quartz crystal, and comparative analysis of calculation errors for these models, and concludes with test results that these models comprehensively reflect the physical features of laser cutting of quartz crystal and satisfy the industrial production requirements, and they can be used to select right parameters for improvement of productivity and quality and saving of energy.
Logistic Regression Models to Forecast Travelling Behaviour in Tripoli City
Directory of Open Access Journals (Sweden)
Amiruddin Ismail
2011-01-01
Full Text Available Transport modes are very important to Libyan’s Tripoli residents for their daily trips. However, the total number of own car and private transport namely taxi and micro buses on the road increases and causes many problems such as traffic congestion, accidents, air and noise pollution. These problems then causes other related phenomena to the travel activities such as delay in trips, stress and frustration to motorists which may affect their productivity and efficiency to both workers and students. Delay may also increase travel cost as well inefficiency in trips making if compare to other public transport users in some Arabs cities. Switching to public transport (PT modes alternatives such as buses, light rail transit and underground train could improve travel time and travel costs. A transport study has been carried out at Tripoli City Authority areas among own car users who live in areas with inadequate of private transport and poor public transportation services. Analyses about relation between factors such as travel time, travel cost, trip purpose and parking cost have been made to answer research questions. Logistic regression technique has been used to analyse these factors that influence users to switch their trips mode to public transport alternatives.
Teacher training through the Regression Model in foreign language education
Directory of Open Access Journals (Sweden)
Jesús García Laborda
2011-01-01
Full Text Available In the last few years, Spain has seen dramatic changes in its educational system. Many of them have been rejected by most teachers after their implementation (LOGSE while others have found potential drawbacks even before starting operating (LOCE, LOE. To face these changes, schools need well qualified instructors. Given this need, and also considering that, although all the schools want the best teachers but, as teachers’ salaries are regulated by the state, few schools can actually offer incentives to their teachers and consequently schools never have the instructors they wish. Apart from this, state schools have a fixed salary for their teachers and private institutions offer no additional bonuses for things like additional training or diplomas (for example, masters or post-degree courses and, therefore, teachers are rarely interested in pursuing any further studies in methodology or any other related fields such as education or applied linguistics. Although many teachers acknowledge their love to teaching, the current situation in schools (school violence, bad salaries, depression, social desprestige, legal changes and so has made the teaching job one of the most complicated and undevoted in Spain. It is not unusual to have a couple of instructors ill due to depression and other psychological sicknesses. This paper deals with the development and implementation of a training program based on regressive visualizations of one’s experience both as a teacher as well as a learner.
Bayesian nonparametric estimation for Quantum Homodyne Tomography
Naulet, Zacharie; Barat, Eric
2016-01-01
We estimate the quantum state of a light beam from results of quantum homodyne tomography noisy measurements performed on identically prepared quantum systems. We propose two Bayesian nonparametric approaches. The first approach is based on mixture models and is illustrated through simulation examples. The second approach is based on random basis expansions. We study the theoretical performance of the second approach by quantifying the rate of contraction of the posterior distribution around ...
Misspecified poisson regression models for large-scale registry data
DEFF Research Database (Denmark)
Grøn, Randi; Gerds, Thomas A.; Andersen, Per K.
2016-01-01
working models that are then likely misspecified. To support and improve conclusions drawn from such models, we discuss methods for sensitivity analysis, for estimation of average exposure effects using aggregated data, and a semi-parametric bootstrap method to obtain robust standard errors. The methods...
CONSISTENCY OF LS ESTIMATOR IN SIMPLE LINEAR EV REGRESSION MODELS
Institute of Scientific and Technical Information of China (English)
Liu Jixue; Chen Xiru
2005-01-01
Consistency of LS estimate of simple linear EV model is studied. It is shown that under some common assumptions of the model, both weak and strong consistency of the estimate are equivalent but it is not so for quadratic-mean consistency.
A Noncentral "t" Regression Model for Meta-Analysis
Camilli, Gregory; de la Torre, Jimmy; Chiu, Chia-Yi
2010-01-01
In this article, three multilevel models for meta-analysis are examined. Hedges and Olkin suggested that effect sizes follow a noncentral "t" distribution and proposed several approximate methods. Raudenbush and Bryk further refined this model; however, this procedure is based on a normal approximation. In the current research literature, this…
A Negative Binomial Regression Model for Accuracy Tests
Hung, Lai-Fa
2012-01-01
Rasch used a Poisson model to analyze errors and speed in reading tests. An important property of the Poisson distribution is that the mean and variance are equal. However, in social science research, it is very common for the variance to be greater than the mean (i.e., the data are overdispersed). This study embeds the Rasch model within an…
Additive Intensity Regression Models in Corporate Default Analysis
DEFF Research Database (Denmark)
Lando, David; Medhat, Mamdouh; Nielsen, Mads Stenbo
2013-01-01
We consider additive intensity (Aalen) models as an alternative to the multiplicative intensity (Cox) models for analyzing the default risk of a sample of rated, nonfinancial U.S. firms. The setting allows for estimating and testing the significance of time-varying effects. We use a variety of mo...
A generalized exponential time series regression model for electricity prices
DEFF Research Database (Denmark)
Haldrup, Niels; Knapik, Oskar; Proietti, Tomasso
We consider the issue of modeling and forecasting daily electricity spot prices on the Nord Pool Elspot power market. We propose a method that can handle seasonal and non-seasonal persistence by modelling the price series as a generalized exponential process. As the presence of spikes can distort...... the estimation of the dynamic structure of the series we consider an iterative estimation strategy which, conditional on a set of parameter estimates, clears the spikes using a data cleaning algorithm, and reestimates the parameters using the cleaned data so as to robustify the estimates. Conditional...... on the estimated model, the best linear predictor is constructed. Our modeling approach provides good fit within sample and outperforms competing benchmark predictors in terms of forecasting accuracy. We also find that building separate models for each hour of the day and averaging the forecasts is a better...
Uniform Consistency for Nonparametric Estimators in Null Recurrent Time Series
DEFF Research Database (Denmark)
Gao, Jiti; Kanaya, Shin; Li, Degui
2015-01-01
This paper establishes uniform consistency results for nonparametric kernel density and regression estimators when time series regressors concerned are nonstationary null recurrent Markov chains. Under suitable regularity conditions, we derive uniform convergence rates of the estimators. Our...... results can be viewed as a nonstationary extension of some well-known uniform consistency results for stationary time series....
de Uña-Álvarez, Jacobo; Meira-Machado, Luís
2015-06-01
Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions of the process. These quantities have been traditionally estimated by the Aalen-Johansen estimator, which is consistent if the process is Markov. Several non-Markov estimators have been proposed in the recent literature, and their superiority with respect to the Aalen-Johansen estimator has been proved in situations in which the Markov condition is strongly violated. However, the existing estimators have the drawback of requiring that the support of the censoring distribution contains the support of the lifetime distribution, which is not often the case. In this article, we propose two new methods for estimating the transition probabilities in the progressive illness-death model. Some asymptotic results are derived. The proposed estimators are consistent regardless the Markov condition and the referred assumption about the censoring support. We explore the finite sample behavior of the estimators through simulations. The main conclusion of this piece of research is that the proposed estimators are much more efficient than the existing non-Markov estimators in most cases. An application to a clinical trial on colon cancer is included. Extensions to progressive processes beyond the three-state illness-death model are discussed.
A Unified Framework for Systematic Model Improvement
DEFF Research Database (Denmark)
Kristensen, Niels Rode; Madsen, Henrik; Jørgensen, Sten Bay
2003-01-01
A unified framework for improving the quality of continuous time models of dynamic systems based on experimental data is presented. The framework is based on an interplay between stochastic differential equation (SDE) modelling, statistical tests and multivariate nonparametric regression...
Using the classical linear regression model in analysis of the dependences of conveyor belt life
Directory of Open Access Journals (Sweden)
Miriam Andrejiová
2013-12-01
Full Text Available The paper deals with the classical linear regression model of the dependence of conveyor belt life on some selected parameters: thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material. The first part of the article is about regression model design, point and interval estimation of parameters, verification of statistical significance of the model, and about the parameters of the proposed regression model. The second part of the article deals with identification of influential and extreme values that can have an impact on estimation of regression model parameters. The third part focuses on assumptions of the classical regression model, i.e. on verification of independence assumptions, normality and homoscedasticity of residuals.
Finch, Holmes; Edwards, Julianne M.
2016-01-01
Standard approaches for estimating item response theory (IRT) model parameters generally work under the assumption that the latent trait being measured by a set of items follows the normal distribution. Estimation of IRT parameters in the presence of nonnormal latent traits has been shown to generate biased person and item parameter estimates. A…
Zhang, Ying; Bi, Peng; Hiller, Janet
2008-01-01
This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.
Wheeler, David C.; Calder, Catherine A.
2007-06-01
The realization in the statistical and geographical sciences that a relationship between an explanatory variable and a response variable in a linear regression model is not always constant across a study area has led to the development of regression models that allow for spatially varying coefficients. Two competing models of this type are geographically weighted regression (GWR) and Bayesian regression models with spatially varying coefficient processes (SVCP). In the application of these spatially varying coefficient models, marginal inference on the regression coefficient spatial processes is typically of primary interest. In light of this fact, there is a need to assess the validity of such marginal inferences, since these inferences may be misleading in the presence of explanatory variable collinearity. In this paper, we present the results of a simulation study designed to evaluate the sensitivity of the spatially varying coefficients in the competing models to various levels of collinearity. The simulation study results show that the Bayesian regression model produces more accurate inferences on the regression coefficients than does GWR. In addition, the Bayesian regression model is overall fairly robust in terms of marginal coefficient inference to moderate levels of collinearity, and degrades less substantially than GWR with strong collinearity.
Moment-bases estimation of smooth transition regression models with endogenous variables
W.D. Areosa (Waldyr Dutra); M.J. McAleer (Michael); M.C. Medeiros (Marcelo)
2008-01-01
textabstractNonlinear regression models have been widely used in practice for a variety of time series and cross-section datasets. For purposes of analyzing univariate and multivariate time series data, in particular, Smooth Transition Regression (STR) models have been shown to be very useful for re
Covariance Functions and Random Regression Models in the ...
African Journals Online (AJOL)
ARC-IRENE
modelled to account for heterogeneity of variance by AY. ... Results suggest that selection for CW could be effective and that RRM could be .... permanent environmental effects; and εij is the temporary environmental effect or measurement error. .... (1999), however, obtained correlations that were variable as low as 0.23 ...
Genomic Prediction of Genotype × Environment Interaction Kernel Regression Models.
Cuevas, Jaime; Crossa, José; Soberanis, Víctor; Pérez-Elizalde, Sergio; Pérez-Rodríguez, Paulino; Campos, Gustavo de Los; Montesinos-López, O A; Burgueño, Juan
2016-11-01
In genomic selection (GS), genotype × environment interaction (G × E) can be modeled by a marker × environment interaction (M × E). The G × E may be modeled through a linear kernel or a nonlinear (Gaussian) kernel. In this study, we propose using two nonlinear Gaussian kernels: the reproducing kernel Hilbert space with kernel averaging (RKHS KA) and the Gaussian kernel with the bandwidth estimated through an empirical Bayesian method (RKHS EB). We performed single-environment analyses and extended to account for G × E interaction (GBLUP-G × E, RKHS KA-G × E and RKHS EB-G × E) in wheat ( L.) and maize ( L.) data sets. For single-environment analyses of wheat and maize data sets, RKHS EB and RKHS KA had higher prediction accuracy than GBLUP for all environments. For the wheat data, the RKHS KA-G × E and RKHS EB-G × E models did show up to 60 to 68% superiority over the corresponding single environment for pairs of environments with positive correlations. For the wheat data set, the models with Gaussian kernels had accuracies up to 17% higher than that of GBLUP-G × E. For the maize data set, the prediction accuracy of RKHS EB-G × E and RKHS KA-G × E was, on average, 5 to 6% higher than that of GBLUP-G × E. The superiority of the Gaussian kernel models over the linear kernel is due to more flexible kernels that accounts for small, more complex marker main effects and marker-specific interaction effects.
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model
DEFF Research Database (Denmark)
Møller, Niels Framroze
This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its......, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...
Asymptotic Normality of LS Estimate in Simple Linear EV Regression Model
Institute of Scientific and Technical Information of China (English)
Jixue LIU
2006-01-01
Though EV model is theoretically more appropriate for applications in which measurement errors exist, people are still more inclined to use the ordinary regression models and the traditional LS method owing to the difficulties of statistical inference and computation. So it is meaningful to study the performance of LS estimate in EV model.In this article we obtain general conditions guaranteeing the asymptotic normality of the estimates of regression coefficients in the linear EV model. It is noticeable that the result is in some way different from the corresponding result in the ordinary regression model.
Identifiability and inference of non-parametric rates-across-sites models on large-scale phylogenies
Mossel, Elchanan
2011-01-01
Mutation rate variation across loci is well known to cause difficulties, notably identifiability issues, in the reconstruction of evolutionary trees from molecular sequences. Here we introduce a new approach for estimating general rates-across-sites models. Our results imply, in particular, that large phylogenies are typically identifiable under rate variation. We also derive sequence-length requirements for high-probability reconstruction. Our main contribution is a novel algorithm that clusters sites according to their mutation rate. Following this site clustering step, standard reconstruction techniques can be used to recover the phylogeny. Our results rely on a basic insight: that, for large trees, certain site statistics experience concentration-of-measure phenomena.
Directory of Open Access Journals (Sweden)
Ivanka Jerić
2011-11-01
Full Text Available Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.
Lashkari, Danial; Sridharan, Ramesh; Vul, Edward; Hsieh, Po-Jang; Kanwisher, Nancy; Golland, Polina
2012-01-16
Functional MRI studies have uncovered a number of brain areas that demonstrate highly specific functional patterns. In the case of visual object recognition, small, focal regions have been characterized with selectivity for visual categories such as human faces. In this paper, we develop an algorithm that automatically learns patterns of functional specificity from fMRI data in a group of subjects. The method does not require spatial alignment of functional images from different subjects. The algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to learn the patterns of functional specificity shared across the group, which we call functional systems, and estimate the number of these systems. Inference based on our model enables automatic discovery and characterization of dominant and consistent functional systems. We apply the method to data from a visual fMRI study comprised of 69 distinct stimulus images. The discovered system activation profiles correspond to selectivity for a number of image categories such as faces, bodies, and scenes. Among systems found by our method, we identify new areas that are deactivated by face stimuli. In empirical comparisons with previously proposed exploratory methods, our results appear superior in capturing the structure in the space of visual categories of stimuli.
A Vector Auto Regression Model Applied to Real Estate Development Investment: A Statistic Analysis
National Research Council Canada - National Science Library
Liu, Fengyun; Matsuno, Shuji; Malekian, Reza; Yu, Jin; Li, Zhixiong
2016-01-01
.... The above theoretical model is empirically evidenced with VAR (Vector Auto Regression) methodology. A panel VAR model shows that land leasing and real estate price appreciation positively affect local government general fiscal revenue...
Reduction of the curvature of a class of nonlinear regression models
Institute of Scientific and Technical Information of China (English)
吴翊; 易东云
2000-01-01
It is proved that the curvature of nonlinear model can be reduced to zero by increasing measured data for a class of nonlinear regression models. The result is important to actual problem and has obtained satisfying effect on data fusing.
Multivariable Linear Regression Model for Promotional Forecasting:The Coca Cola - Morrisons Case
Zheng, Yiwei/Y
2009-01-01
This paper describes a promotional forecasting model, built by linear regression module in Microsoft Excel. It intends to provide quick and reliable forecasts with a moderate credit and to assist the CPFR between the Coca Cola Enterprises (CCE) and the Morrisons. The model is derived from previous researches and literature review on CPFR, promotion, forecasting and modelling. It is designed as a multivariable linear regression model, which involves several promotional mix as variables includi...
Comparative analysis of regression and artificial neural network models for wind speed prediction
Bilgili, Mehmet; Sahin, Besir
2010-11-01
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.
Directory of Open Access Journals (Sweden)
J. Bohlin
2012-07-01
Full Text Available The recent development in software for automatic photogrammetric processing of multispectral aerial imagery, and the growing nation-wide availability of Digital Elevation Model (DEM data, are about to revolutionize data capture for forest management planning in Scandinavia. Using only already available aerial imagery and ALS-assessed DEM data, raster estimates of the forest variables mean tree height, basal area, total stem volume, and species-specific stem volumes were produced and evaluated. The study was conducted at a coniferous hemi-boreal test site in southern Sweden (lat. 58° N, long. 13° E. Digital aerial images from the Zeiss/Intergraph Digital Mapping Camera system were used to produce 3D point-cloud data with spectral information. Metrics were calculated for 696 field plots (10 m radius from point-cloud data and used in k-MSN to estimate forest variables. For these stands, the tree height ranged from 1.4 to 33.0 m (18.1 m mean, stem volume from 0 to 829 m3 ha-1 (249 m3 ha-1 mean and basal area from 0 to 62.2 m2 ha-1 (26.1 m2 ha-1 mean, with mean stand size of 2.8 ha. Estimates made using digital aerial images corresponding to the standard acquisition of the Swedish National Land Survey (Lantmäteriet showed RMSEs (in percent of the surveyed stand mean of 7.5% for tree height, 11.4% for basal area, 13.2% for total stem volume, 90.6% for pine stem volume, 26.4 for spruce stem volume, and 72.6% for deciduous stem volume. The results imply that photogrammetric matching of digital aerial images has significant potential for operational use in forestry.
Prediction of the result in race walking using regularized regression models
Directory of Open Access Journals (Sweden)
Krzysztof Przednowek
2013-04-01
Full Text Available The following paper presents the use of regularized linear models as tools to optimize training process. The models were calculated by using data collected from race-walkers' training events. The models used predict the outcomes over a 3 km race and following a prescribed training plan. The material included a total of 122 training patterns made by 21 players. The methods of analysis include: classical model of OLS regression, ridge regression, LASSO regression and elastic net regression. In order to compare and choose the best method a cross-validation of the extit{leave-one-out} was used. All models were calculated using R language with additional packages. The best model was determined by the LASSO method which generates an error of about 26 seconds. The method has simplified the structure of the model by eliminating 5 out of 18 predictors.
DEFF Research Database (Denmark)
Tan, Qihua; Bathum, L; Christiansen, L
2003-01-01
In this paper, we apply logistic regression models to measure genetic association with human survival for highly polymorphic and pleiotropic genes. By modelling genotype frequency as a function of age, we introduce a logistic regression model with polytomous responses to handle the polymorphic...... situation. Genotype and allele-based parameterization can be used to investigate the modes of gene action and to reduce the number of parameters, so that the power is increased while the amount of multiple testing minimized. A binomial logistic regression model with fractional polynomials is used to capture...
STATISTICAL INFERENCES FOR VARYING-COEFFICINT MODELS BASED ON LOCALLY WEIGHTED REGRESSION TECHNIQUE
Institute of Scientific and Technical Information of China (English)
梅长林; 张文修; 梁怡
2001-01-01
Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fired by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.
Nonparametric Kernel Smoothing Methods. The sm library in Xlisp-Stat
Directory of Open Access Journals (Sweden)
Luca Scrucca
2001-06-01
Full Text Available In this paper we describe the Xlisp-Stat version of the sm library, a software for applying nonparametric kernel smoothing methods. The original version of the sm library was written by Bowman and Azzalini in S-Plus, and it is documented in their book Applied Smoothing Techniques for Data Analysis (1997. This is also the main reference for a complete description of the statistical methods implemented. The sm library provides kernel smoothing methods for obtaining nonparametric estimates of density functions and regression curves for different data structures. Smoothing techniques may be employed as a descriptive graphical tool for exploratory data analysis. Furthermore, they can also serve for inferential purposes as, for instance, when a nonparametric estimate is used for checking a proposed parametric model. The Xlisp-Stat version includes some extensions to the original sm library, mainly in the area of local likelihood estimation for generalized linear models. The Xlisp-Stat version of the sm library has been written following an object-oriented approach. This should allow experienced Xlisp-Stat users to implement easily their own methods and new research ideas into the built-in prototypes.
Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.
Xu, Yanxun; Müller, Peter; Wahed, Abdus S; Thall, Peter F
2016-01-01
We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially between patients, depending on how the regime plays out. To evaluate the regimes, mean overall survival time is expressed as a weighted average of the means of all possible sums of successive transitions times. We assume a Bayesian nonparametric survival regression model for each transition time, with a dependent Dirichlet process prior and Gaussian process base measure (DDP-GP). Posterior simulation is implemented by Markov chain Monte Carlo (MCMC) sampling. We provide general guidelines for constructing a prior using empirical Bayes methods. The proposed approach is compared with inverse probability of treatment weighting, including a doubly robust augmented version of this approach, for both single-stage and multi-stage regimes with treatment assignment depending on baseline covariates. The simulations show that the proposed nonparametric Bayesian approach can substantially improve inference compared to existing methods. An R program for implementing the DDP-GP-based Bayesian nonparametric analysis is freely available at https://www.ma.utexas.edu/users/yxu/.
Koon, Sharon; Petscher, Yaacov
2015-01-01
The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…
Aboveground biomass and carbon stocks modelling using non-linear regression model
Ain Mohd Zaki, Nurul; Abd Latif, Zulkiflee; Nazip Suratman, Mohd; Zainee Zainal, Mohd
2016-06-01
Aboveground biomass (AGB) is an important source of uncertainty in the carbon estimation for the tropical forest due to the variation biodiversity of species and the complex structure of tropical rain forest. Nevertheless, the tropical rainforest holds the most extensive forest in the world with the vast diversity of tree with layered canopies. With the usage of optical sensor integrate with empirical models is a common way to assess the AGB. Using the regression, the linkage between remote sensing and a biophysical parameter of the forest may be made. Therefore, this paper exemplifies the accuracy of non-linear regression equation of quadratic function to estimate the AGB and carbon stocks for the tropical lowland Dipterocarp forest of Ayer Hitam forest reserve, Selangor. The main aim of this investigation is to obtain the relationship between biophysical parameter field plots with the remotely-sensed data using nonlinear regression model. The result showed that there is a good relationship between crown projection area (CPA) and carbon stocks (CS) with Pearson Correlation (p < 0.01), the coefficient of correlation (r) is 0.671. The study concluded that the integration of Worldview-3 imagery with the canopy height model (CHM) raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the lowland Dipterocarp forest.
Parametric versus non-parametric simulation
Dupeux, Bérénice; Buysse, Jeroen
2014-01-01
Most of ex-ante impact assessment policy models have been based on a parametric approach. We develop a novel non-parametric approach, called Inverse DEA. We use non parametric efficiency analysis for determining the farm’s technology and behaviour. Then, we compare the parametric approach and the Inverse DEA models to a known data generating process. We use a bio-economic model as a data generating process reflecting a real world situation where often non-linear relationships exist. Results s...
MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.
2005-01-01
Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.
Combining an additive and tree-based regression model simultaneously: STIMA
Dusseldorp, E.; Conversano, C.; Os, B.J. van
2010-01-01
Additive models and tree-based regression models are two main classes of statistical models used to predict the scores on a continuous response variable. It is known that additive models become very complex in the presence of higher order interaction effects, whereas some tree-based models, such as
Rocconi, Louis M.
2013-01-01
This study examined the differing conclusions one may come to depending upon the type of analysis chosen, hierarchical linear modeling or ordinary least squares (OLS) regression. To illustrate this point, this study examined the influences of seniors' self-reported critical thinking abilities three ways: (1) an OLS regression with the student…
Thomas, Michael S. C.; Knowland, Victoria C. P.; Karmiloff-Smith, Annette
2011-01-01
Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by…
Thomas, Michael S. C.; Knowland, Victoria C. P.; Karmiloff-Smith, Annette
2011-01-01
Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by…
CONFIDENCE REGIONS IN TERMS OF STATISTICAL CURVATURE FOR AR(q) NONLINEAR REGRESSION MODELS
Institute of Scientific and Technical Information of China (English)
刘应安; 韦博成
2004-01-01
This paper constructs a set of confidence regions of parameters in terms of statistical curvatures for AR(q) nonlinear regression models. The geometric frameworks are proposed for the model. Then several confidence regions for parameters and parameter subsets in terms of statistical curvatures are given based on the likelihood ratio statistics and score statistics. Several previous results, such as [1] and [2] are extended to AR(q)nonlinear regression models.
Directory of Open Access Journals (Sweden)
Soldić-Aleksić Jasna
2009-01-01
Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.
Martino, K G; Marks, B P
2007-12-01
Two different microbial modeling procedures were compared and validated against independent data for Listeria monocytogenes growth. The most generally used method is two consecutive regressions: growth parameters are estimated from a primary regression of microbial counts, and a secondary regression relates the growth parameters to experimental conditions. A global regression is an alternative method in which the primary and secondary models are combined, giving a direct relationship between experimental factors and microbial counts. The Gompertz equation was the primary model, and a response surface model was the secondary model. Independent data from meat and poultry products were used to validate the modeling procedures. The global regression yielded the lower standard errors of calibration, 0.95 log CFU/ml for aerobic and 1.21 log CFU/ml for anaerobic conditions. The two-step procedure yielded errors of 1.35 log CFU/ml for aerobic and 1.62 log CFU/ ml for anaerobic conditions. For food products, the global regression was more robust than the two-step procedure for 65% of the cases studied. The robustness index for the global regression ranged from 0.27 (performed better than expected) to 2.60. For the two-step method, the robustness index ranged from 0.42 to 3.88. The predictions were overestimated (fail safe) in more than 50% of the cases using the global regression and in more than 70% of the cases using the two-step regression. Overall, the global regression performed better than the two-step procedure for this specific application.
Nonparametric estimation of ultrasound pulses
DEFF Research Database (Denmark)
Jensen, Jørgen Arendt; Leeman, Sidney
1994-01-01
An algorithm for nonparametric estimation of 1D ultrasound pulses in echo sequences from human tissues is derived. The technique is a variation of the homomorphic filtering technique using the real cepstrum, and the underlying basis of the method is explained. The algorithm exploits a priori...
Biological parametric mapping with robust and non-parametric statistics.
Yang, Xue; Beason-Held, Lori; Resnick, Susan M; Landman, Bennett A
2011-07-15
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities. Copyright © 2011 Elsevier Inc. All rights reserved.
Antretter, Elfi; Dunkel, Dirk; Osvath, Peter; Voros, Viktor; Fekete, Sandor; Haring, Christian
2006-06-01
The prospective investigation of repetitive nonfatal suicidal behavior is associated with two methodological problems. Due to the commonly used definitions of nonfatal suicidal behavior, clinical samples usually consist of patients with a considerable between-person variability. Second, repeated nonfatal suicidal episodes of the same subjects are likely to be correlated. We examined three regression techniques to comparatively evaluate their efficiency in addressing the given methodological problems. Repeated episodes of nonfatal suicidal behavior were assessed in two independent patient samples during a 2-year follow-up period. The first regression design modeled repetitive nonfatal suicidal behavior as a summary measure. The second regression model treated repeated episodes of the same subject as independent events. The third regression model represented a hierarchical linear model. The estimated mean effects of the first model were likely to be nonrepresentative for a considerable part of the study subjects. The second regression design overemphasized the impact of the predictor variables. The hierarchical linear model most appropriately accounted for the heterogeneity of the samples and the correlated data structure. The nonhierarchical regression designs did not provide appropriate statistical models for the prospective investigation of repetitive nonfatal suicidal behavior. Multilevel modeling provides a convenient alternative.
Nonparametric inferences for kurtosis and conditional kurtosis
Institute of Scientific and Technical Information of China (English)
XIE Xiao-heng; HE You-hua
2009-01-01
Under the assumption of strictly stationary process, this paper proposes a nonparametric model to test the kurtosis and conditional kurtosis for risk time series. We apply this method to the daily returns of S&P500 index and the Shanghai Composite Index, and simulate GARCH data for verifying the efficiency of the presented model. Our results indicate that the risk series distribution is heavily tailed, but the historical information can make its future distribution light-tailed. However the far future distribution's tails are little affected by the historical data.
Regression modeling of streamflow, baseflow, and runoff using geographic information systems.
Zhu, Yuanhong; Day, Rick L
2009-02-01
Regression models for predicting total streamflow (TSF), baseflow (TBF), and storm runoff (TRO) are needed for water resource planning and management. This study used 54 streams with >20 years of streamflow gaging station records during the period October 1971 to September 2001 in Pennsylvania and partitioned TSF into TBF and TRO. TBF was considered a surrogate of groundwater recharge for basins. Regression models for predicting basin-wide TSF, TBF, and TRO were developed under three scenarios that varied in regression variables used for model development. Regression variables representing basin geomorphological, geological, soil, and climatic characteristics were estimated using geographic information systems. All regression models for TSF, TBF, and TRO had R(2) values >0.94 and reasonable prediction errors. The two best TSF models developed under scenarios 1 and 2 had similar absolute prediction errors. The same was true for the two best TBF models. Therefore, any one of the two best TSF and TBF models could be used for respective flow prediction depending on variable availability. The TRO model developed under scenario 1 had smaller absolute prediction errors than that developed under scenario 2. Simplified Area-alone models developed under scenario 3 might be used when variables for using best models are not available, but had lower R(2) values and higher or more variable prediction errors than the best models.
Procedures for adjusting regional regression models of urban-runoff quality using local data
Hoos, A.B.; Sisolak, J.K.
1993-01-01
Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for
Comparison of land-use regression models between Great Britain and the Netherlands.
Vienneau, D.; de Hoogh, K.; Beelen, R.M.J.; Fischer, P.; Hoek, G.; Briggs, D.
2010-01-01
Land-use regression models have increasingly been applied for air pollution mapping at typically the city level. Though models generally predict spatial variability well, the structure of models differs widely between studies. The observed differences in the models may be due to artefacts of data an
U.S. Geological Survey, Department of the Interior — This dataset was created using the PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate mapping system, developed by Dr. Christopher Daly,...
Rank Set Sampling in Improving the Estimates of Simple Regression Model
Directory of Open Access Journals (Sweden)
M Iqbal Jeelani
2015-04-01
Full Text Available In this paper Rank set sampling (RSS is introduced with a view of increasing the efficiency of estimates of Simple regression model. Regression model is considered with respect to samples taken from sampling techniques like Simple random sampling (SRS, Systematic sampling (SYS and Rank set sampling (RSS. It is found that R2 and Adj R2 obtained from regression model based on Rank set sample is higher than rest of two sampling schemes. Similarly Root mean square error, p-values, coefficient of variation are much lower in Rank set based regression model, also under validation technique (Jackknifing there is consistency in the measure of R2, Adj R2 and RMSE in case of RSS as compared to SRS and SYS. Results are supported with an empirical study involving a real data set generated of Pinus Wallichiana taken from block Langate of district Kupwara.
Institute of Scientific and Technical Information of China (English)
Tao Hu; Heng-jian Cui; Xing-wei Tong
2009-01-01
This article considers a semiparametric varying-coefficient partially linear regression model with current status data. The semiparametric varying-coefficient partially linear regression model which is a gen-eralization of the partially linear regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estima-tor for the unknown smooth function is obtained and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are conducted to examine the small-sample properties of the proposed estimates and a real dataset is used to illustrate our approach.
Population-based estimates of pesticide intake are needed to characterize exposure for particular demographic groups based on their dietary behaviors. Regression modeling performed on measurements of selected pesticides in composited duplicate diet samples allowed (1) estimation ...
Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition
Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on ...
Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition
Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on ...
Drzewiecki, Wojciech
2016-12-01
In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.
Madarang, Krish J; Kang, Joo-Hyon
2014-06-01
Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data.
Bayesian Nonparametric Clustering for Positive Definite Matrices.
Cherian, Anoop; Morellas, Vassilios; Papanikolopoulos, Nikolaos
2016-05-01
Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.
Random regression models using different functions to model milk flow in dairy cows.
Laureano, M M M; Bignardi, A B; El Faro, L; Cardoso, V L; Tonhati, H; Albuquerque, L G
2014-09-12
We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude.
Modelling QTL effect on BTA06 using random regression test day models.
Suchocki, T; Szyda, J; Zhang, Q
2013-02-01
In statistical models, a quantitative trait locus (QTL) effect has been incorporated either as a fixed or as a random term, but, up to now, it has been mainly considered as a time-independent variable. However, for traits recorded repeatedly, it is very interesting to investigate the variation of QTL over time. The major goal of this study was to estimate the position and effect of QTL for milk, fat, protein yields and for somatic cell score based on test day records, while testing whether the effects are constant or variable throughout lactation. The analysed data consisted of 23 paternal half-sib families (716 daughters of 23 sires) of Chinese Holstein-Friesian cattle genotyped at 14 microsatellites located in the area of the casein loci on BTA6. A sequence of three models was used: (i) a lactation model, (ii) a random regression model with a QTL constant in time and (iii) a random regression model with a QTL variable in time. The results showed that, for each production trait, at least one significant QTL exists. For milk and protein yields, the QTL effect was variable in time, while for fat yield, each of the three models resulted in a significant QTL effect. When a QTL is incorporated into a model as a constant over time, its effect is averaged over lactation stages and may, thereby, be difficult or even impossible to be detected. Our results showed that, in such a situation, only a longitudinal model is able to identify loci significantly influencing trait variation.
The empirical likelihood goodness-of-fit test for regression model
Institute of Scientific and Technical Information of China (English)
Li-xing ZHU; Yong-song QIN; Wang-li XU
2007-01-01
Goodness-of-fit test for regression modes has received much attention in literature. In this paper, empirical likelihood (EL) goodness-of-fit tests for regression models including classical parametric and autoregressive (AR) time series models are proposed. Unlike the existing locally smoothing and globally smoothing methodologies, the new method has the advantage that the tests are self-scale invariant and that the asymptotic null distribution is chi-squared. Simulations are carried out to illustrate the methodology.
On asymptotics of t-type regression estimation in multiple linear model
Institute of Scientific and Technical Information of China (English)
无
2004-01-01
We consider a robust estimator (t-type regression estimator) of multiple linear regression model by maximizing marginal likelihood of a scaled t-type error t-distribution.The marginal likelihood can also be applied to the de-correlated response when the withinsubject correlation can be consistently estimated from an initial estimate of the model based on the independent working assumption. This paper shows that such a t-type estimator is consistent.
Developing and testing a global-scale regression model to quantify mean annual streamflow
Barbarossa, Valerio; Huijbregts, Mark A. J.; Hendriks, A. Jan; Beusen, Arthur H. W.; Clavreul, Julie; King, Henry; Schipper, Aafke M.
2017-01-01
Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 106 km2. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process-based global hydrological models.
Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data
Ulbrich, Norbert Manfred; Volden, Thomas R.
2010-01-01
The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.
A hybrid model using logistic regression and wavelet transformation to detect traffic incidents
Directory of Open Access Journals (Sweden)
Shaurya Agarwal
2016-07-01
Full Text Available This research paper investigates a hybrid model using logistic regression with a wavelet-based feature extraction for detecting traffic incidents. A logistic regression model is suitable when the outcome can take only a limited number of values. For traffic incident detection, the outcome is limited to only two values, the presence or absence of an incident. The logistic regression model used in this study is a generalized linear model (GLM with a binomial response and a logit link function. This paper presents a framework to use logistic regression and wavelet-based feature extraction for traffic incident detection. It investigates the effect of preprocessing data on the performance of incident detection models. Results of this study indicate that logistic regression along with wavelet based feature extraction can be used effectively for incident detection by balancing the incident detection rate and the false alarm rate according to need. Logistic regression on raw data resulted in a maximum detection rate of 95.4% at the cost of 14.5% false alarm rate. Whereas the hybrid model achieved a maximum detection rate of 98.78% at the expense of 6.5% false alarm rate. Results indicate that the proposed approach is practical and efficient; with future improvements in the proposed technique, it will make an effective tool for traffic incident detection.
Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi
2012-01-01
The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.