Small Area Estimation von J. N. K. Rao | ISBN 9780471431626

Small Area Estimation

von J. N. K. Rao
Buchcover Small Area Estimation | J. N. K. Rao | EAN 9780471431626 | ISBN 0-471-43162-1 | ISBN 978-0-471-43162-6
Leseprobe

„... impressive and elegantly written... maintains a high level ofmathematical rigour and depth... lucid, self-contained andwell-organized...“ (Zentralblatt Math, Vol. 1026, 2004)
„This pioneering work, in which Rao provides acomprehensive and up-to-date treatment of small area estimation, will become a classic... I believe that it has the potential to turnsmall area estimation... into a larger area of importance to bothresearchers and practitioners.“ (Journal of the AmericanStatistical Association, March 2004)
„The book is a systematic and economical account of thedevelopment of the subject by one of its foremostcontributors.“ (Short Book Reviews, 2003)
„This book will help to advance the subject and be a valuableresource for practitioners and theorists.“ (Statistics inTransition, November 2003)
"This book is essential to any basic library on smallestimation. Both theoretical researchers and practitioners in thissubject will certainly appreciate the themes treated in Rao'sbook.„ (Journal of Official Statistics, 2003)
“... a textbook on small area estimation, probably the only onepresently available on this topic... easy to read and each chapteris illustrated by practical examples.„ (MathematicalReviews, 2003j)
“... an authoritative and comprehensive account of methodsfor producing small area estimates by using not conventional directestimates, but indirect, model-dependent estimates..."(Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003)

Small Area Estimation

von J. N. K. Rao
An accessible introduction to indirect estimation methods, bothtraditional and model-based. Readers will also find the latestmethods for measuring the variability of the estimates as well asthe techniques for model validation.
* Uses a basic area-level linear model to illustrate themethods
* Presents the various extensions including binary response datathrough generalized linear models and time series data throughlinear models that combine cross-sectional and time seriesfeatures
* Provides recent applications of SAE including several in U. S. Federal programs
* Offers a comprehensive discussion of the design issues thatimpact SAE