Parimal Mukhopadhyay

Bio: Parimal Mukhopadhyay is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Population & Estimator. The author has an hindex of 5, co-authored 21 publications receiving 84 citations. Previous affiliations of Parimal Mukhopadhyay include Obafemi Awolowo University.

Topics in survey sampling

Abstract: 1 The Basic Concepts.- 1.1 Introduction.- 1.2 The Fixed Population model.- 1.3 Different Types of Sampling Designs.- 1.4 The Estimators.- 1.5 Some Inferential Problems under Fixed Population Set-Up.- 1.6 Plan of the Book.- 2 Inference under Frequentist Theory Approach.- 2.1 Introduction.- 2.2 Principles of Inference Based on Theory of Prediction.- 2.3 Robustness of Model-Dependent Optimal Strategies.- 2.4 A Class of Predictors under Model ?(X, v).- 2.5 Asymptotic Unbiased Estimation of Design-Variance of $${ {\hat{T}}_{ {GR}}}$$.- 3 Bayes and Empirical Bayes Prediction of a Finite Population Total.- 3.1 Introduction.- 3.2 Bayes and Minimax Prediction of Finite Population Parameters.- 3.3 Bayes Prediction of a Finite Population Total under Normal Regression Model.- 3.4 Bayes Prediction under an Asymmetric Loss Function.- 3.5 James-Stein Estimator and Associated Estimators.- 3.6 Empirical Bayes Prediction of Population Total under Simple Location Model.- 3.7 EB-Prediction under Normal Model using Covariates.- 3.8 Applications in Small Area Estimation.- 3.9 Bayes Prediction under Random Error Variance Model.- 3.10 Exercises.- 4 Modifications of Bayes Procedure.- 4.1 Introduction.- 4.2 Linear Bayes Prediction.- 4.3 Restricted Linear Bayes Prediction.- 4.4 Constrained Bayes Prediction.- 4.5 Bayesian Robustness under a Class of Alternative Models.- 4.6 Robust Bayes Estimation under Contaminated Priors.- 4.7 Exercises.- 5 Estimation of Finite Population Variance, Regression Coefficient.- 5.1 Introduction.- 5.2 Design-Based Estimation of a Finite Population Variance.- 5.3 Model-Based Prediction of V.- 5.4 Bayes Prediction of V(y).- 5.5 Asymptotic Properties of Sample Regression Coefficient.- 5.6 PM-Unbiased Estimation of Slope Parameters in the Linear Regression Model.- 5.7 Optimal Prediction of Finite Population Regression Coefficient under Multiple Regression Model.- 5.8 Exercises.- 6 Estimation of a Finite Population Distribution Function.- 6.1 Introduction.- 6.2 Design-Based Estimators.- 6.3 Model-Based Predictors.- 6.4 Conditional Approach.- 6.5 Asymptotic Properties of the Estimators.- 6.6 Non-Parametric Kernel Estimators.- 6.7 Desirable Properties of an Estimator.- 6.8 Empirical Studies.- 6.9 Best Unbiased Prediction (BUP) under Gaussian Superpopulation Model.- 6.10 Estimation of Median.- 7 Prediction in Finite Population under Measurement Error Models.- 7.1 Introduction.- 7.2 Additive Measurement Error Models.- 7.3 Prediction under Multiplicative Error-in-Variables Model.- 7.4 Exercises.- 8 Miscellaneous Topics.- 8.1 Introduction.- 8.2 Calibration Estimators.- 8.3 Post-Stratification.- 8.4 Design-Based Conditional Unbiasedness.- 8.5 Exercises.- References.- Author Index.

On the Performance of the Ordinary Least Squares Method under an Error Component Model.

Abstract: Starting from the one-dimensional results by Wang et al (1994) we consider the performance of the ordinary least squares estimator in comparison to the best linear unbiased estimator under an error component model with random effects in units and time. Upper bounds are derived for the first-order approximation to the difference between both estimators and for the spectral norm of the difference between their dispersion matrices.

On the Performance of the Ordinary Least Squares Method Under an Error Component Model

Abstract: Starting from the one-dimensional results by Wang et al (1994) we consider the performance of the ordinary least squares estimator in comparison to the best linear unbiased estimator under an error component model with random effects in units and tons. Upper bounds are derived for the first-order approximation to the difference between both estimators and for the spectral norm of the difference between their dispersion matrices.

Estimation of Finite Population Variance, Regression Coefficient

Abstract: In this chapter we consider estimation of a finite population variance and regression coefficient. The estimation of population variance is of considerable importance in many circumstances. The geneticsts often classify their population according to population variance [Thompson and Thoday (1979)]. In allocating sample size in a stratified random sampling according to optimum allocation rules, the stratum standard deviations are required to be estimated. Sections 5.2 through 5.4 consider design-based, model-based and Bayes prediction of a finite population variance. Section 5.5 considers some asymptotic properties of a sample regression coefficient. The next section considers pm-unbiased prediction of the slope parameter in the linear regression model. The concluding section addresses optimal prediction of the finite population regression coefficient under multiple regression model.

Sampling with Unequal Probabilities without Replacement: A Review

Abstract: Summary This paper deals with a review of sampling with unequal probabilities without replacement. In Section 2 of this paper, a list of selection Procedures along with their properties (in brief) is given. Section 3 deals with the comparison of the Horvitz-Thompson Estimator and an approximate formula for the estimation of variance which does not involve 7,, is presented and comparison of special estimators is made in Section 4.

Improved Estimation of Finite Population Variance Using Auxiliary Information

Abstract: This article addresses the problem of estimating of finite population variance using auxiliary information in simple random sampling. A ratio-cum-difference type class of estimators for population variance has been suggested with its properties under large sample approximation. It has been shown that the suggested class of estimators is more efficient than usual unbiased, difference, Das and Tripathi (1978), Isaki (1983), Singh et al. (1988), Kadilar and Cingi (2006), and other estimators/classes of estimators. In addition, we support this theoretical result with the aid of a empirical study.