Advanced Sampling Theory with Applications: How Michael’ selected’ Amy, Volume 1
Advanced Sampling Theory with Applications: How Michael "selected" Amy is a comprehensive exposť of basic and advanced sampling techniques along with their applications in the diverse fields of science and technology.
This book is a multi-purpose document. It can be used as a text by teachers, as a reference manual by researchers, and as a practical guide by statisticians. It covers 1165 references from different research journals through almost 1900 citations across 1194 pages, a large number of complete proofs of theorems, important results such as corollaries, and 324 unsolved exercises from several research papers. It includes 159 solved, data-based, real life numerical examples in disciplines such as Agriculture, Demography, Social Science, Applied Economics, Engineering, Medicine, and Survey Sampling. These solved examples are very useful for an understanding of the applications of advanced sampling theory in our daily life and in diverse fields of science. An additional 173 unsolved practical problems are given at the end of the chapters. University and college professors may find these useful when assigning exercises to students. Each exercise gives exposure to several complete research papers for researchers/students.
The data-based problems show statisticians how to select a sample and obtain estimates of parameters from a given population by using different sampling strategies, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Derivations of calibration weights from the design weights under single phase and two-phase sampling have been provided for simple numerical examples. These examples will be useful to understand the meaning of benchmarks to improve the design weights. These examples also explain the background of well-known scientific computer packages like CALMAR, GES, SAS, STATA, and SUDAAN etc., used to generate calibration weights by most organizations in the public and private sectors. The ideas of hot deck, cold deck, mean method of imputation, ratio method of imputation, compromised imputation, and multiple imputations have been explained with very simple numerical examples. Simple examples are also provided to understand Jackknife variance estimation under single phase, two-phase [or random non-response by following Sitter (1997)] and multi-stage stratified designs.
This book also covers, in a very simple and compact way, many new topics not yet available in any book on the international market. A few of these interesting topics are: median estimation under single phase and two-phase sampling, difference between low level and higher level calibration approach, calibration weights and design weights, estimation of parametric functions, hidden gangs in finite populations, compromised imputation, variance estimation using distinct units, general class of estimators of population mean and variance, wider class of estimators of population mean and variance, power transformation estimators, estimators based on the mean of non-sampled units of the auxiliary character, ratio and regression type estimators for estimating finite population variance similar to proposed by Isaki (1982), unbiased estimators of mean and variance under Midzuno's scheme of sampling, usual and modified jackknife variance estimator, estimation of regression coefficient, concept of revised selection probabilities, multi-character surveys sampling, overlapping, adaptive, and post cluster sampling, new techniques in systematic sampling, successive sampling, small area estimation, continuous populations, and estimation of measurement errors.
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Probability of selecting a sample
BASIC CONCEPTS AND MATHEMATICAL
Examples of populations and samples
Difference between study variable and auxiliary variable
STRATIFIED AND POSTSTRATIFIED SAMPLING
Amount in 000 of agricultural loans
State population projections 1995 and 2000 Number in thousands
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Advanced Sampling Theory with Applications: How Michael 'selected' Amy, Volume 2
No preview available - 2003
Assume auxiliary variable bias calibrated weights class of estimators cluster sampling confidence interval estimate Construct a 95 correlation coefficient defined denote distribution estate farm loans estimate the average estimate the variance estimator for estimating estimator of population estimator of variance Example Exercise expected values following theorem Hence the theorem Hint Horvitz and Thompson ies2 known linear regression estimator mean squared error method nonreal estate farm number of fish obtained optimum value order inclusion probabilities order of approximation parameter population mean population total PPSWR Proof proportional allocation random number random variable randomization device real estate farm relative efficiency respect respondent sample mean sampling design sampling scheme second phase sample Select a sample simple random sampling Singh SRSWOR sampling strata stratified sampling stratum study variable superpopulation model survey survey sampling systematic sampling tobacco crop total number two-phase sampling unbiased estimator