A New Concept for Tuning Design Weights in Survey Sampling - 1st Edition - ISBN: 9780081005941, 9780081005958

A New Concept for Tuning Design Weights in Survey Sampling

1st Edition

Jackknifing in Theory and Practice

Authors: Sarjinder Singh Stephen Sedory Maria Rueda Antonio Arcos Raghunath Arnab
eBook ISBN: 9780081005958
Hardcover ISBN: 9780081005941
Imprint: Academic Press
Published Date: 11th November 2015
Page Count: 316
Tax/VAT will be calculated at check-out Price includes VAT (GST)
30% off
30% off
30% off
30% off
30% off
20% off
20% off
30% off
30% off
30% off
30% off
30% off
20% off
20% off
30% off
30% off
30% off
30% off
30% off
20% off
20% off
149.95
104.97
104.97
104.97
104.97
104.97
119.96
119.96
95.00
66.50
66.50
66.50
66.50
66.50
76.00
76.00
108.00
75.60
75.60
75.60
75.60
75.60
86.40
86.40
Unavailable
Price includes VAT (GST)
× DRM-Free

Easy - Download and start reading immediately. There’s no activation process to access eBooks; all eBooks are fully searchable, and enabled for copying, pasting, and printing.

Flexible - Read on multiple operating systems and devices. Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle.

Open - Buy once, receive and download all available eBook formats, including PDF, EPUB, and Mobi (for Kindle).

Institutional Access

Secure Checkout

Personal information is secured with SSL technology.

Free Shipping

Free global shipping
No minimum order.

Description

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.

Key Features

  • Explains how to calibrate design weights in survey sampling
  • Discusses how Jackknifing is needed in design weights in survey sampling
  • Describes how design weights are imputed in survey sampling

Readership

Graduates undertaking a Ph.D. or MS in statistics especially in survey sampling. Govt. organizations and private organizations. All Survey Methodologists could benefit.

Table of Contents

  • 1: Problem of estimation
    • Abstract
    • 1.1 Introduction
    • 1.2 Estimation problem and notation
    • 1.3 Modeling of jumbo pumpkins
    • 1.4 The concept of jackknifing
    • 1.5 Jackknifing the sample mean
    • 1.6 Doubly jackknifed sample mean
    • 1.7 Jackknifing a sample proportion
    • 1.8 Jackknifing of a double suffix variable sum
    • 1.9 Frequently asked questions
    • 1.10 Exercises
  • 2: Tuning of jackknife estimator
    • Abstract
    • 2.1 Introduction
    • 2.2 Notation
    • 2.3 Tuning with a chi-square type distance function
    • 2.4 Tuning with dell function
    • 2.5 An important remark
    • 2.6 Exercises
  • 3: Model assisted tuning of estimators
    • Abstract
    • 3.1 Introduction
    • 3.2 Model assisted tuning with a chi-square distance function
    • 3.3 Model assisted tuning with a dual-to-empirical log-likelihood (dell) function
    • 3.4 Exercises
  • 4: Tuned estimators of finite population variance
    • Abstract
    • 4.1 Introduction
    • 4.2 Tuned estimator of finite population variance
    • 4.3 Tuning with a chi-square distance
    • 4.4 Tuning of estimator of finite population variance with a dual-to-empirical log-likelihood (dell) function
    • 4.5 Alternative tuning with a chi-square distance
    • 4.6 Alternative tuning with a dell function
    • 4.7 Exercises
  • 5: Tuned estimators of correlation coefficient
    • Abstract
    • 5.1 Introduction
    • 5.2 Correlation coefficient
    • 5.3 Tuned estimator of correlation coefficient
    • 5.4 Exercises
  • 6: Tuning of multicharacter survey estimators
    • Abstract
    • 6.1 Introduction
    • 6.2 Transformation on selection probabilities
    • 6.3 Tuning with a chi-square distance function
    • 6.4 Tuning of the multicharacter estimator of population total with dual-to-empirical log-likelihood function
    • 6.5 Exercises
  • 7: Tuning of the Horvitz–Thompson estimator
    • Abstract
    • 7.1 Introduction
    • 7.2 Jackknifed weights in the Horvitz–Thompson estimator
    • 7.3 Tuning with a chi-square distance function while using jackknifed sample means
    • 7.4 Tuning of the Horvitz–Thompson estimator with a displacement function
    • 7.5 Exercises
  • 8: Tuning in stratified sampling
    • Abstract
    • 8.1 Introduction
    • 8.2 Stratification
    • 8.3 Tuning with a chi-square distance function using stratum-level known population means of an auxiliary variable
    • 8.4 Tuning with dual-to-empirical log-likelihood function using stratum-level known population means of an auxiliary variable
    • 8.5 Exercises
  • 9: Tuning using multiauxiliary information
    • Abstract
    • 9.1 Introduction
    • 9.2 Notation
    • 9.3 Tuning with a chi-square distance function
    • 9.4 Tuning with empirical log-likelihood function
    • 9.5 Exercises
  • 10: A brief review of related work
    • Abstract
    • 10.1 Introduction
    • 10.2 Calibration
    • 10.3 Jackknifing
  • Bibliography
  • Author Index

Details

No. of pages:
316
Language:
English
Copyright:
© Academic Press 2016
Published:
Imprint:
Academic Press
eBook ISBN:
9780081005958
Hardcover ISBN:
9780081005941

About the Author

Sarjinder Singh

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.

Affiliations and Expertise

Texas A&M University – Kingsville, USA

Stephen Sedory

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.

Affiliations and Expertise

Associate Professor of Mathematics, Department of Mathematics, Texas A&M University-Kingsville, USA

Maria Rueda

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.

Affiliations and Expertise

University of Granada, Spain

Antonio Arcos

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.

Affiliations and Expertise

Assistant Professor of Statistics, University of Granada, Spain

Raghunath Arnab

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.

Affiliations and Expertise

Dept of Statistics, University of Botswana, Botswana