(Ebook) Sampling Statistics (Wiley Series in Survey Methodology) by Wayne A. Fuller ISBN 9780470454602, 0470454601

(Ebook) Sampling Statistics (Wiley Series in Survey Methodology) by Wayne A. Fuller ISBN 9780470454602, 0470454601

This book developed out of a desire for a sampling course that would fit
easily into a graduate program in statistics. Survey sampling is a relatively
young discipline, achieving acceptance in the 1940’s and 1950’s, primarily for
official statistics. As the discipline has matured, analytic use of survey data
has increased inside and outside government. Also statistical models, such as
those for nonresponse and for small area estimation, are now considered part
of survey methodology. As a result, the overlap between survey sampling and
other areas of statistics has increased, and the mutual dependence makes it
important that survey sampling be an integral part of statistics.
Originally, survey sampling was differentiated from other areas by the size
of the data sets and by the number of estimates produced. In such a setting
survey statisticians prefer techniques with broad applicability, and that require
a minimum of assumptions. Procedures are sought that are nearly design
unbiased, but no claim of optimality is made for a particular statistic. These
standard survey techniques are introduced in Chapter One. I have adopted a
tenor and notation similar to statistics texts in other specialities to make the
material more accessible to those with limited exposure to survey sampling.
Some of the technical material in Section 1.3 can be omitted or covered at
a later point. Basic sampling concepts are introduced in a way to facilitate
application of model based procedures to survey samples. Likewise, models
are used in constructing estimators and in discussions of designs in Chapter
Two and Chapter Three, respectively. Chapter Five is devoted to procedures,
such as nonresponse adjustment and small area estimation, where models play
a central role. To be comfortable with the material the reader should have
completed courses in the theory of statistics and in linear regression.