The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Jae Kwang Kim - One of the best experts on this subject based on the ideXlab platform.
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asymptotic theory and inference of predictive mean matching imputation using a Superpopulation Model framework
Scandinavian Journal of Statistics, 2020Co-Authors: Shu Yang, Jae Kwang KimAbstract:Predictive mean matching imputation is popular for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the predictive mean matching estimator for finite-population inference using a Superpopulation Model framework. We also clarify conditions for its robustness. For variance estimation, the conventional bootstrap inference is invalid for matching estimators with a fixed number of matches due to the nonsmoothness nature of the matching estimator. We propose a new replication variance estimator, which is asymptotically valid. The key strategy is to construct replicates directly based on the linear terms of the martingale representation for the matching estimator, instead of individual records of variables. Simulation studies confirm that the proposed method provides valid inference.
Shu Yang - One of the best experts on this subject based on the ideXlab platform.
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asymptotic theory and inference of predictive mean matching imputation using a Superpopulation Model framework
Scandinavian Journal of Statistics, 2020Co-Authors: Shu Yang, Jae Kwang KimAbstract:Predictive mean matching imputation is popular for handling item nonresponse in survey sampling. In this article, we study the asymptotic properties of the predictive mean matching estimator for finite-population inference using a Superpopulation Model framework. We also clarify conditions for its robustness. For variance estimation, the conventional bootstrap inference is invalid for matching estimators with a fixed number of matches due to the nonsmoothness nature of the matching estimator. We propose a new replication variance estimator, which is asymptotically valid. The key strategy is to construct replicates directly based on the linear terms of the martingale representation for the matching estimator, instead of individual records of variables. Simulation studies confirm that the proposed method provides valid inference.
Priyanka Aggarwal - One of the best experts on this subject based on the ideXlab platform.
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bayes prediction for a stratified regression Superpopulation Model using balanced loss function
Communications in Statistics-theory and Methods, 2010Co-Authors: Ashok K Bansal, Priyanka AggarwalAbstract:We consider the stratified regression Superpopulation Model and obtain Bayes predictor of the finite population mean under Zellner's two-criterion balanced loss function (BLF). BLF predictor simplifies to a linear combination of the sample and predictive means. Furthermore, it reduces to some of the well-known classical and Bayes predictors. Relative losses and relative savings loss are obtained to investigate loss robustness of the BLF predictor. It is found to perform better than the usual sample mean as well as the predictive mean in the minimal Bayes predictive expected loss sense.
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robustness of bayes prediction under error in variables Superpopulation Model
Communications in Statistics-theory and Methods, 2009Co-Authors: Priyanka Aggarwal, Ashok K BansalAbstract:In this article, we consider Bayes prediction in a finite population under the simple location error-in-variables Superpopulation Model. Bayes predictor of the finite population mean under Zellner's balanced loss function and the corresponding relative losses and relative savings loss are derived. The prior distribution of the unknown location parameter of the Model is assumed to have a non-normal distribution belonging to the class of Edgeworth series distributions. Effects of non normality of the “true” prior distribution and that of a possible misspecification of the loss function on the Bayes predictor are illustrated for a hypothetical population.
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bayes prediction of the regression coefficient in a finite population using balanced loss function
Metron-International Journal of Statistics, 2009Co-Authors: Ashok K Bansal, Priyanka AggarwalAbstract:Summary -I nthis paper, we derive Bayes predictor of the finite population regression coefficient of a linear regression normal Superpopulation Model under Zellner’s (1994) balanced loss function. Loss robustness of the Bayes predictor is investigated in terms of relative losses and relative savings loss. The results are illustrated for location Superpopulation Model and also for a Model with diagonal covariance error matrix. Some of the well known classical predictors are found to be a limiting case of the derived Bayes predictor. Sensitivity of Bayes predictor to a possible misspecification of the loss function is examined for a hypothetical population.
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bayes prediction for a heteroscedastic regression Superpopulation Model using balanced loss function
Communications in Statistics-theory and Methods, 2007Co-Authors: Ashok K Bansal, Priyanka AggarwalAbstract:We consider Prais–Houthakker heteroscedastic normal regression Model having variance of the dependent variable same as square of its expectation. Bayes predictors for the regression coefficient and the mean of a finite population are derived using Zellner's balanced loss function. Bayes predictive expected losses are obtained and compared with those of classical predictors and Bayes predictors under squared error loss function to examine their loss robustness.
Ashok K Bansal - One of the best experts on this subject based on the ideXlab platform.
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bayes prediction for a stratified regression Superpopulation Model using balanced loss function
Communications in Statistics-theory and Methods, 2010Co-Authors: Ashok K Bansal, Priyanka AggarwalAbstract:We consider the stratified regression Superpopulation Model and obtain Bayes predictor of the finite population mean under Zellner's two-criterion balanced loss function (BLF). BLF predictor simplifies to a linear combination of the sample and predictive means. Furthermore, it reduces to some of the well-known classical and Bayes predictors. Relative losses and relative savings loss are obtained to investigate loss robustness of the BLF predictor. It is found to perform better than the usual sample mean as well as the predictive mean in the minimal Bayes predictive expected loss sense.
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robustness of bayes prediction under error in variables Superpopulation Model
Communications in Statistics-theory and Methods, 2009Co-Authors: Priyanka Aggarwal, Ashok K BansalAbstract:In this article, we consider Bayes prediction in a finite population under the simple location error-in-variables Superpopulation Model. Bayes predictor of the finite population mean under Zellner's balanced loss function and the corresponding relative losses and relative savings loss are derived. The prior distribution of the unknown location parameter of the Model is assumed to have a non-normal distribution belonging to the class of Edgeworth series distributions. Effects of non normality of the “true” prior distribution and that of a possible misspecification of the loss function on the Bayes predictor are illustrated for a hypothetical population.
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bayes prediction of the regression coefficient in a finite population using balanced loss function
Metron-International Journal of Statistics, 2009Co-Authors: Ashok K Bansal, Priyanka AggarwalAbstract:Summary -I nthis paper, we derive Bayes predictor of the finite population regression coefficient of a linear regression normal Superpopulation Model under Zellner’s (1994) balanced loss function. Loss robustness of the Bayes predictor is investigated in terms of relative losses and relative savings loss. The results are illustrated for location Superpopulation Model and also for a Model with diagonal covariance error matrix. Some of the well known classical predictors are found to be a limiting case of the derived Bayes predictor. Sensitivity of Bayes predictor to a possible misspecification of the loss function is examined for a hypothetical population.
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bayes prediction for a heteroscedastic regression Superpopulation Model using balanced loss function
Communications in Statistics-theory and Methods, 2007Co-Authors: Ashok K Bansal, Priyanka AggarwalAbstract:We consider Prais–Houthakker heteroscedastic normal regression Model having variance of the dependent variable same as square of its expectation. Bayes predictors for the regression coefficient and the mean of a finite population are derived using Zellner's balanced loss function. Bayes predictive expected losses are obtained and compared with those of classical predictors and Bayes predictors under squared error loss function to examine their loss robustness.
Jean D Opsomer - One of the best experts on this subject based on the ideXlab platform.
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local polynomial regresssion estimators in survey sampling
Annals of Statistics, 2000Co-Authors: Jay F Breidt, Jean D OpsomerAbstract:Estimation of finite population totals in the presence of auxiliary information is considered. A class of estimators based on local polynomial regression is proposed. Like generalized regression estimators, these estimators are weighted linear combinations of study variables, in which the weights are calibrated to known control totals, but the assumptions on the Superpopulation Model are considerably weaker. The estimators are shown to be asymptotically design-unbiased and consistent under mild assumptions. A variance approximation based on Taylor linearization is suggested and shown to be consistent for the design mean squared error of the estimators. The estimators are robust in the sense of asymptotically attaining the Godambe-Joshi lower bound to the anticipated variance. Simulation experiments indicate that the estimators are more efficient than regression estimators when the Model regression function is incorrectly specified, while being approximately as efficient when the parametric specification is correct.