Introduction to Variance Estimation [Paperback]

Now available in paperback, this book is organized in a way that emphasizes both the theory and applications of the various variance estimating techniques. Results are often presented in the form of theorems; proofs are deleted when trivial or when a reference is readily available. It applies to large, complex surveys; and to provide an easy reference for the survey researcher who is faced with the problem of estimating variances for real survey data.

The Method of Random Groups.- Variance Estimation Based on Balanced Half-Samples.- The Jackknife Method.- The Bootstrap Method.- Taylor Series Methods.- Generalized Variance Functions.- Variance Estimation for Systematic Sampling.- Summary of Methods for Complex Surveys.- Hadamard Matrices.- Asymptotic Theory of Variance Estimators.- Transformations.- The Effect of Measurement Errors on Variance Estimation.- Computer Software for Variance Estimation.- The Effect of Imputation on Variance Estimation.

From the reviews:

The main purpose of this book is to describe a number of techniques for variance estimation that have been suggested in recent years, and to demonstrate how they may be used in the context of modern complex sample surveys. The various techniques to be described are widely scattered through the statistical literature; currently, there is no systematic treatment of this methodology that brings together the state of the art in one manuscript. The book accomplishes this objective. (Dmitry Ostrouchov, Zentralblatt MATH, Vol. 1050, 2005)

We live in the information age. Statistical surveys are used every day to determine or evaluate public policy and to make important business decisions. Correct methods for computing the precision of the survey data and for making inferences to the target population are absolutely essential to sound decision making. Now in its second edition, Introduction to Variance Estimationló,