Advanced Sampling Theory with Applications: How Michael 'selected' Amy, Volumen 2

"Advanced Sampling Theory with Applications: How Michael"selected" Amy" is a comprehensive expose of basic andadvanced sampling techniques along with their applications in thediverse fields of science and technology.This book is a multi-purpose document. It can be used as a text byteachers, as a reference manual by researchers, and as a practicalguide by statisticians. It covers 1165 references from differentresearch journals through almost 1900 citations across 1194 pages, alarge number of complete proofs of theorems, important results such ascorollaries, and 324 unsolved exercises from several research papers.It includes 159 solved, data-based, real life numerical examples indisciplines such as Agriculture, Demography, Social Science, AppliedEconomics, Engineering, Medicine, and Survey Sampling. These solvedexamples are very useful for an understanding of the applications ofadvanced sampling theory in our daily life and in diverse fields ofscience. An additional 173 unsolved practical problems are given atthe end of the chapters. University and college professors may findthese useful when assigning exercises to students. Each exercise givesexposure to several complete research papers for researchers/students.The data-based problems show statisticians how to select a sample andobtain estimates of parameters from a given population by usingdifferent sampling strategies, systematic sampling, stratifiedsampling, cluster sampling, and multi-stage sampling. Derivations ofcalibration weights from the design weights under single phase andtwo-phase sampling have been provided for simple numerical examples.These examples will be useful to understand the meaning of benchmarksto improve thedesign weights. These examples also explain thebackground of well-known scientific computer packages like CALMAR, GES, SAS, STATA, and SUDAAN etc., used to generate calibration weightsby most organizations in the public and private sectors. The ideas ofhot deck, cold deck, mean method of imputation, ratio method ofimputation, compromised imputation, and multiple imputations have beenexplained with very simple numerical examples. Simple examples arealso provided to understand Jackknife variance estimation under singlephase, 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 newtopics not yet available in any book on the international market. Afew of these interesting topics are: median estimation under singlephase and two-phase sampling, difference between low level and higherlevel calibration approach, calibration weights and design weights, estimation of parametric functions, hidden gangs in finitepopulations, compromised imputation, variance estimation usingdistinct units, general class of estimators of population mean andvariance, wider class of estimators of population mean and variance, power transformation estimators, estimators based on the mean ofnon-sampled units of the auxiliary character, ratio and regressiontype estimators for estimating finite population variance similar toproposed by Isaki (1982), unbiased estimators of mean and varianceunder Midzuno's scheme of sampling, usual and modified jackknifevariance estimator, estimation of regression coefficient, concept ofrevised selection probabilities, multi-character surveys sampling, overlapping, adaptive, and postcluster sampling, new techniques insystematic sampling, successive sampling, small area estimation, continuous populations, and estimation of measurement errors.