1 edition of **Robust empirical Bayes estimation in finite population sampling** found in the catalog.

- 341 Want to read
- 17 Currently reading

Published
**1986** .

Written in English

- Bayesian statistical decision theory,
- Estimation theory,
- Sampling (Statistics)

**Edition Notes**

Statement | by Parhasarathi Lahiri |

The Physical Object | |
---|---|

Pagination | vii, 118 leaves : |

Number of Pages | 118 |

ID Numbers | |

Open Library | OL25923674M |

OCLC/WorldCa | 15300469 |

Topics in survey sampling. [Parimal Mukhopadhyay] -- "This book provides a review of topics in survey sampling that have not been covered in detail in other books and indicates new research areas. {{GR}}}$$.- 3 Bayes and Empirical Bayes Prediction of a Finite Population Total.- Introduction.- Bayes and Minimax Prediction of. A finite population correction factor is needed in computing the standard deviation of the sampling distribution of sample means Answers: a. whenever the sample size is more than 5% of the population size b. whenever the sample size is less than 5% of the population size c. whenever the population is infinite d. In this book we have made an attempt to discuss popular sampling designs and estimation methods that can be used for estimation of population characteristics. This book presents two popular sampling design categories, namely the sampling of units and the sampling . Title of Document: HIERARCHICAL BAYES ESTIMATION AND EMPIRICAL BEST PREDICTION OF SMALL-AREA PROPORTIONS Benmei Liu, Doctor of Philosophy, Directed By: Professor Partha Lahiri Joint Program in Survey Methodology Estimating proportions of units with a given characteristic for small areas.

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Empirical Bayes Estimation in Finite Population Sampling. Journal of the American Statistical Association: Vol. 81, No.pp. Cited by: The paper proposes some robust estimators of the finite population mean.

Such estimators are particularly suitable in the presence of some outlying observations. Book Description Assuming a basic knowledge of the frequentist approach to finite population sampling, Bayesian Methods for Finite Population Sampling describes Bayesian and predictive approaches to inferential problems with an emphasis on the likelihood principle.

This article introduces a new empirical Bayes approach in model-based finite population sampling theory. One example in which empirical Bayes methods would be ap-propriate is described in the abstract preceding this article.

A second example concerns small-area estimation. Agen-cies of the U.S. government have been involved in obtain. Ghosh and Meeden () considered empirical Bayes estimation of the finite population mean assuming a normal superpopulation model.

Their estimators generalize automatically when one estimates a vector of stratum by: Robust Bayesian Inference in Finite Population Sampling under Balanced Loss Function M. and Lahiri, P. Robust empirical Bayes estimation of means from stratified samples, Journal of the American Statistical Association, 82, Empirical Bayes estimation in finite population sampling, Journal of the American Statistical.

Robust estimation in ﬁnite population sampling using the conditional bias of a unit David Haziza Universit´e de Montr´eal joint work with J.-F. Beaumont (Statistics Canada) and A. Ruiz-Gazen (Universit´e de Toulouse 1) Colloque en l’honneur de Jean-Claude Deville Neuchˆatel JuneDavid Haziza Robust estimation in sample surveys.

Balanced samples and robust Bayesian inference in finite population sampling RICHARD M. ROYALL. Balanced samples and robust Bayesian inference in finite population sampling, Biometrika, Vol Issue 2, AugustEmpirical likelihood test for a large-dimensional mean vectorCited by: Balanced Samples and Robust Bayesian Inference in Finite Population Sampling Article (PDF Available) in Biometrika 69(2) August with 39 Reads How we measure 'reads'.

testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is File Size: 6MB. In the linear empirical Bayes approach to estimation of finite population means considered in Ghosh and Lahiri () one is faced with m populations.

The j th population consists of Nj measurements Xj1, XjNj and a sample of size nj Cited by: 1. Empirical Bayes (EB) and related techniques have been found to be quite useful in estimation problems of population characteristics of a finite population sampling theory.

Two very concrete examples are given in Nandram and Sedransk (, hereafter NS) Cited by: 4. Empirical Bayes Estimation of Finite Population Means from Complex Surveys.

Journal of the American Statistical Association: Vol. 92, No.pp. Cited by: * Empirical and hierarchical Bayes procedures * Sequential estimation in finite population sampling * Reliability estimation and capture-recapture methodologies leading to sequential tagging schemes An indispensable resource for researchers in sequential analysis, Sequential Estimation is an ideal graduate-level text as well.

T1 - Empirical Bayes estimation in finite population sampling. AU - Ghosh, Malay. AU - Meeden, Glen. PY - Y1 - N2 - Empirical Bayes methods are becoming increasingly popular in statistics. Robbins () introduced the method in the context of nonparametric estimation of a completely unspecified prior by: The proposed book aims at making a comprehensive review of applications of Bayes procedures, Empirical Bayes procedures and their ramifications (like linear Bayes estimation, restricted Bayes least square prediction, constrained Bayes estimation, Bayesian robustness) in making inference from a finite population : Springer-Verlag New York.

Abstract. Small area estimation is becoming increasingly popular in survey sampling. Agencies of the Federal Government have been involved in obtaining estimates of population counts, unemployment rates, per capita income, crop yields and Cited by: We obtain a limit of a hierarchical Bayes estimator of a finite population mean when the sample size is large.

The limit is in the sense of ordinary calculus, where the sample observations are treated as fixed quantities. Ghosh, M. and Lahiri, P. Robust empirical Bayes estimation of means from stratified samples.

Amer. Statist Cited by: 6. Ghosh, M. and Meeden, G. Empirical Bayes estimation in finite population sampling, J. Amer.81, – zbMATH CrossRef MathSciNet Google Author: Wen-Tao Huang, Yao-Tsung Lai. Assuming a basic knowledge of the frequentist approach to finite population sampling, Bayesian Methods for Finite Population Sampling describes Bayesian and predictive approaches to inferential problems with an emphasis on the likelihood principle.

The authors demonstrate that a variety of levels of prior information can be used in survey Cited by: Sinha & Rao () proposed estimation procedures designed for small-area means, based on robustified maximum likelihood estimators and robust empirical best linear unbiased predictors.

Their methods are of the plug-in type and may be by: Datta and Ghosh () provided a uniﬁed approach to the Bayesian estimation of diﬀerent strata variances in ﬁnite population sampling under stratiﬁed simple random sampling.

The Bayes and the empirical Bayes estimation of strata variances are con- sidered. avor. Empirical Bayes calculations are inherently fraught with di culties, making both of the modeling strategies useful. An excellent review of empirical Bayes methodology appears in Chapter 3 of Carlin and Louis ().

Empirical Bayes analyses often produce impressive-looking estimates of posterior distri-butions. Bayesian Methods for Finite Population Sampling - CRC Press Book Assuming a basic knowledge of the frequentist approach to finite population sampling, Bayesian Methods for Finite Population Sampling describes Bayesian and predictive approaches to inferential problems with an emphasis on the likelihood principle.

Bayesian inference for ﬁnite population parameters in multistage cluster sampling. Bayesian Methods for Finite Population Sampling. D AVIS,W.W.a n dC AO, X. Empirical Bayes estimation in ﬁnite population sampling. ().Author: P Lahiri and Kanchan Mukherjee. In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss).Equivalently, it maximizes the posterior expectation of a utility function.

An alternative way of formulating an estimator within Bayesian statistics is maximum a posteriori estimation. Abstract: We consider the problem of unbiased estimation of a finite population proportion and compare the unequal probability sampling strategies due to Hansen-Hurwitz [9], Horvitz-Thompson [12], Rao-Hartley-Cochran [18] and Midzuno-Sen [16,20] under a super-population model.

It is shown that the model expected variance is least for the. A design-based approximation to the Bayes Information Criterion in finite population sampling In this article, various issues related to the implementation of the usual Bayesian Information Criterion (BIC) are critically examined in the context of modelling a finite population.

We discuss the connections between our methods and existing approaches, especially empirical Bayes and James–Stein estimation. Article information Source Bayesian Anal., Vol Number 4 (), Cited by: Assuming a basic knowledge of the frequentist approach to finite population sampling, Bayesian Methods for Finite Population Sampling describes Bayesian and predictive approaches to inferential problems with an emphasis on the likelihood principle.

The authors demonstrate that a variety of Price: $ Robust and E cient Methods for Bayesian Finite Population Inference by Xi Xia A dissertation submitted in partial ful llment of the requirements for the degree of Doctor of Philosophy (Biostatistics) in The University of Michigan Doctoral Committee: Professor Michael R.

Elliott, Chair Professor Richard D. Gonzalez Professor Timothy D. Johnson. EMPIRICAL BAYES AND BAYES PREDICTION OF FINITE POPULATION TOTAL USING AUXILIARY INFORMATION Mark S. Hamnera, John W. Seaman, Jr.b, Dean M. Youngb Abstract: The focus of this paper is on the prediction of a finite population total T by taking a sample of size n from a population of size N : Mark S.

Hamner, Dean M. Young. Get this from a library. Bayesian methods for finite population sampling. [Malay Ghosh; G Meeden] -- Bayesian methods are finding increasing acceptance in the theory and practice of statistics. Bayesian Methods in Finite Population Sampling discusses some of these methods in the context of finite.

Both approaches also require some type of model of the population - e.g. the population is Normal, or the population has a finite mean. However, in frequentist statistics, probabilities are assigned only as the frequency of an event occurring when sampling from the population. Empirical Bayes.

a weighted average of the sample estimate. by the sampling design with the population item values held fixed. Standard text books on sampling (e.g. Cochran, ; Thompson, ; Lohr, ) provide extensive accounts of design-based direct estimation.

In recent years, demand for reliable estimates for small domains (small areas) has greatly. The usefulness of EBE of η is manifold. Individual PK/PD parameters and accordingly individual drug concentrations or effects are described using EBE [].EBE can be used for screening covariates for the structural model development [].If relationships between covariates and EBEs do not exist, EBE theoretically should have no trend with the by: 7.

DOI: /pjsor.v12i Corpus ID: Hierarchical Bayes Small Area Estimation under a Unit Level Model with Applications in Agriculture @article{NazirHierarchicalBS, title={Hierarchical Bayes Small Area Estimation under a Unit Level Model with Applications in Agriculture}, author={Nageena Nazir and Shakeel Ahmad Mir.

The proposed book aims at making a comprehensive review of applications of Bayes procedures, Empirical Bayes procedures and their ramifications (like linear Bayes estimation, restricted Bayes least square prediction, constrained Bayes estimation, Bayesian robustness) in making inference from a finite population sampling.

AN EMPIRICAL BAYES PREDICTION INTERVAL FOR THE FINITE POPULATION MEAN OF A SMALL AREA Balgobin Nandram Worcester Polytechnic Institute Abstract: We construct an empirical Bayes (EB) prediction interval for the ﬁnite population mean of a small area when data are available from many similar small areas.

Key words: Con dence interval; Empirical Bayes; Finite population; Mean squared error; Random e ect; Small area estimation. 1 and Rao () and Chambers et al. () among others proposed robust methods against outliers under normality assumptions. However, these methods basically aims struct an empirical Bayes con dence interval of.

According to our current on-line database, Parthasarathi Lahiri has 10 students and 11 descendants. We welcome any additional information. If you have additional information or corrections regarding this mathematician, please use the update submit students of this mathematician, please use the new data form, noting this mathematician's MGP ID of .Berger, J.

and M. Berliner,Robust Bayes and empirical Bayes analysis with epsilon-contaminated priors, Annals of Statistics, 14, 2, Bresson, G. and C. Hsiao,A functional connectivity approach for modeling cross-sectional dependence with an application to the estimation of hedonic housing prices in Paris, Advances in.Empirical Bayes and Full Bayes for Signal Estimation Yanting Ma, Jin Tan, Nikhil Krishnan, and Dror Baron Department of Electrical and Computer Engineering North Carolina State University Raleigh, NCUSA Email: fyma7, jtan, nkrishn, [email protected] Abstract—We consider signals that follow a parametric dis-File Size: KB.