Starting from the preliminaries and ending with live examples, Modern Survey Sampling details what a sample can communicate about an unknowable aggregate in a real situation. The author lucidly develops and presents numerous approaches. He details recent developments and explores fresh and unseen problems, hitting upon possible solutions. The text covers current research output in a student-friendly manner with attractive illustrations. It introduces sampling and discusses how to select a sample for which a selection-probability is specified to prescribe its performance characteristics. The author then explains how to examine samples with varying probabilities to derive profits. He then examines how to use partial segments to make reasonable guesses about a sample's behavior and assess the elements of discrepancies. Including case studies, exercises, and solutions, the book highlights special survey techniques needed to capture trustworthy data and put it to intelligent use. It then discusses the model-assisted approach and network sampling, before moving on to speculating about random processes. The author draws on his extensive teaching experience to create a textbook that gives your students a thorough grounding in the technologies of survey sampling and modeling and also provides you with the tools to teach them.
Exposure to Sampling Abstract Introduction Concepts of Population, Sample, and Sampling Initial Ramifications Abstract Introduction Sampling Design, Sampling Scheme Random Numbers and Their Uses in Simple RandomSampling (SRS) Drawing Simple Random Samples with and withoutReplacement Estimation of Mean, Total, Ratio of Totals/Means:Variance and Variance Estimation Determination of Sample Sizes A.2 Appendix to Chapter 2 A.More on Equal Probability Sampling A.Horvitz-Thompson Estimator A.Sufficiency A.Likelihood A.Non-Existence Theorem More Intricacies Abstract Introduction Unequal Probability Sampling Strategies PPS Sampling Exploring Improved Ways Abstract Introduction Stratified Sampling Cluster Sampling Multi-Stage Sampling Multi-Phase Sampling: Ratio and RegressionEstimation viiviii ContentsControlled Sampling Modeling Introduction Super-Population Modeling Prediction Approach Model-Assisted Approach Bayesian Methods Spatial Smoothing Sampling on Successive Occasions: Panel Rotation Non-Response and Not-at-Homes Weighting Adjustments and Imputation 5.10 Time Series Approach in Repeated Sampling Stigmatizing Issues Abstract Introduction Early Growth of RR and the Current Status Optional Randomized Response Techniques Indirect Questioning Developing Small Domain Statistics Abstract Introduction Some Details Network and Adaptive Procedures Abstract Introduction Estimation by Network Sampling and Estimationby Adaptive Sampling Constraining Network Sampling and ConstrainingAdaptive Sampling Analytical Methods Abstract Analytical Surveys: Contingency Tables A.1 Reviews and Further Openings A.2 Case Studies A.3 Exercises and Solutions Supplementaries References Author Index Subject Index