A New Concept for Tuning Design Weights in Survey Sampling: Jackknifing in Theory and Practice

by Sarjinder Singh, Stephen A. Sedory, Maria Del Mar Rueda, Antonio Arcos, Raghunath Arnab

Overview

A New Concept for Tuning Design Weights in Survey Sampling: Jackknifing in Theory and Practice introduces the new concept of tuning design weights in survey sampling by presenting three concepts: calibration, jackknifing, and imputing where needed. This new methodology allows survey statisticians to develop statistical software for analyzing data in a more precisely and friendly way than with existing techniques.

Product Details

ISBN-13:	9780081005941
Publisher:	Elsevier Science
Publication date:	11/26/2015
Pages:	316
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Sarjinder Singh has a Ph.D. degree in statistics specializing in the field of survey sampling. Associate professor of mathematics and statistics, Texas A&M University – Kingsville (h index 11). He is a founder of higher order calibration technique in survey sampling. His first paper on this topic was published in the journal Survey Methodology, Statistics Canada, during 1998. Later he published numerous papers on calibration technique, and this monograph is also based on calibration techniques but with a different aspect. He is also pioneer founder of a dual problem of calibration published in highly respectable journal Statistics-A Journal of Theoretical and Applied Statistics. He also introduced the pioneering idea of calibration using displacement function and published in an prestigious journal, Metrika. He has published over 150 research papers in the field of survey sampling.

Stephen A. Sedory has a Ph.D. degree in Mathematics, and has over 20 years of teaching and research experience at graduate and undergraduate level (Associate Professor of Mathematics, Department of Mathematics, Texas A&M University-Kingsville. Although his previous work is in the field of Topology, he has recently been working in the field of survey sampling. He has introduced the idea of two-step calibration and calibrated maximum likelihood calibration weights jointly with the first author.

Maria Del Mar Rueda is a full-Professor and Director of a research group focusing on design and analysis of sample surveys at the University of Granada, Spain.

Antonio Arcos is an Assistant Professor of Statistics, University of Granada, Spain, and is also working in the same areas of survey sampling. Together with Maria, Antonio is not only an expert in survey sampling, but also in writing codes in R language. All R-codes in this monographs are written by Maria and Antonio. In addition, both have contributed several papers on the calibration technique in survey sampling.

Raghunath Arnab has a Ph.D. in statistics with specialization in survey sampling from the Indian Statistical Institute. He is based at the Dept of Statistics, University of Botswana. He has published very good quality papers in the field of complex survey sampling. His major contribution in this monograph is to check all the theoretical derivations of the results.

Table of Contents

1 Problem of Estimation

2 Tuning of Jackknife Estimator

3 Model Assisted Tuning of Estimators

4 Tuned Estimators of Finite Population Variance

5 Tuned Estimators of Finite Population Correlation Coefficient

6 Tuning of Multi-Character Survey Estimators

7 Tuning of the Horvitz-Thompson Estimator

8 Tuning in Stratified Sampling

9 Tuning using Multiauxiliary Information

10 A Brief Review of Related Work

Bibliography

Handy Subject Index

Author Index

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From the Publisher

This book introduces a new statistical survey methodology that can be used to estimate population parameters, presenting three concepts for discussion, including calibration, jackknifing, and imputing where needed.