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.
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- 384 pages
- English
- PDF
- Available on iOS & Android
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Information
Publisher
Princeton University PressYear
2015Print ISBN
9780691619767
9780691081137
eBook ISBN
9781400867011
Table of contents
- Cover
- Table of Contents
- 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
- 11 Programs of the Estimates
- 12 Random Number Generations - Details
- 13 Dual-Criterion Problems in Estimation
- 14 Integration Formulas, More or Less Replacement for Monte Carlo
- 15 Monte Carlo for Contaminated Gaussians
- References
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Yes, you can access Robust Estimates of Location by David F. Andrews,Frank R. Hampel in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over 1.5 million books available in our catalogue for you to explore.