Basic theory: simple random sampling; Sampling with varying probabilities; Stratified sampling; Ratio method of estimation; Regression method of estimation; Choice of sampling unit; Sub-sampling; Systematic sampling; Non-sampling errors.

Survey Sampling Theory and Applications offers a comprehensive overview of survey sampling, including the basics of sampling theory and practice, as well as research-based topics and examples of emerging trends. The text is useful for basic and advanced survey sampling courses. Many other books available for graduate students do not contain material on recent developments in the area of survey sampling. The book covers a wide spectrum of topics on the subject, including repetitive sampling over two occasions with varying probabilities, ranked set sampling, Fays method for balanced repeated replications, mirror-match bootstrap, and controlled sampling procedures. Many topics discussed here are not available in other text books. In each section, theories are illustrated with numerical examples. At the end of each chapter theoretical as well as numerical exercises are given which can help graduate students. Covers a wide spectrum of topics on survey sampling and statistics Serves as an ideal text for graduate students and researchers in survey sampling theory and applications Contains material on recent developments in survey sampling not covered in other books Illustrates theories using numerical examples and exercises

Basic theory: simple random sampling. Sampling with varying probabilities. Stratified sampling. Ratio method of estimation. Regression method estimation. Choice of sampling unit. Sub-sampling. Systematic sampling. Non-sampling errors.

Sample surveys is the most important branch of statistics. Without sample surveys there is no data, and without data there is no statistics. This book is the culmination of the lecture notes developed by the authors. The approach is theoretical in the sense that it gives mathematical proofs of the results in sample surveys. Intended as a textbook for a one-semester course for undergraduate seniors or first-year graduate students, a prerequisite basic knowledge of algebra, calculus, and statistical theory is required to master the techniques described in this book.

Basic theory: simple random sampling; Sampling with varying probabilities; Stratified sampling; Ratio method of estimation; Regression method of estimation; Choice of sampling unit; Sub-sampling; Systematic sampling; Non-sampling errors.