Robust Estimates of Location : : Survey and Advances / / David F. Andrews, Frank R. Hampel.

• Superior document: Title is part of eBook package: De Gruyter Princeton Legacy Lib. eBook Package 1931-1979
• VerfasserIn: Andrews, David F.,; Hampel, Frank R.,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2015]; ©1972
• Year of Publication: 2015
• Language: English
• Series: Princeton Legacy Library ; 1280
• Physical Description: 1 online resource (384 p.)

Because estimation involves inferring information about an unknown quantity on the basis of available data, the selection of an estimator is influenced by its ability to perform well under the conditions that are assumed to underlie the data. Since these conditions are never known exactly, the estimators chosen must be robust; i.e., they must be able to perform well under a variety of underlying conditions. The theory of robust estimation is based on specified properties of specified estimators under specified conditions. This book was written as the result of a study undertaken to establish the interaction of these three components over as large a range as possible.Originally published in 1972.The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

Table of Contents

Frontmatter -- Preface -- Table of Contents -- 1. Introduction -- 2. Estimates -- 3. Asymptotic Characteristics of the Estimates -- 4. Finite-Sample Calculations -- 5. Properties -- 6. A Detailed Analysis of the Variances -- 7. General Discussion -- Appendices -- 11. Programs of the Estimates -- 12. Random Number Generation-Details -- 13. Dual-Criterion Problems in Estimation -- 14. Integration Formulas, More or Less Replacement for Monte Carlo -- 15. Monte Carlo for Contaminated Gaussians -- References