7 edition of Pitman"s measure of closeness found in the catalog.
Includes bibliographical references (p. 211-222) and index.
|Statement||Jerome P. Keating, Robert L. Mason, Pranab K. Sen.|
|Contributions||Mason, Robert Lee, 1946-, Sen, Pranab Kumar, 1937-, Society of Industrial Mathematics.|
|LC Classifications||QA276.8 .K43 1993|
|The Physical Object|
|Pagination||xv, 226 p. :|
|Number of Pages||226|
|LC Control Number||93003059|
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Pitman's Measure of Closeness (PMC) is simply an idea whose time has come. Certainly there are many different ways to estimate unknown parameters, but which method should you use. Posed as an alternative to the concept of mean-squared-error, PMC is based on the probabilities of the closeness of competing estimators to an unknown by: Pitman's Measure of Closeness by Jerome P.
Keating,available at Book Depository with free delivery worldwide.4/5(1). In statistical theory, the Pitman closeness criterion, named after E.
Pitman, is a way of comparing two candidate estimators for the same parameter. Under this criterion, estimator A is preferred to estimator B if the probability that estimator A is closer to the true value than estimator B is greater than one half.
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Pitman's Measure of Closeness (PMC) is simply an idea whose time has come. Certainly there are many different ways to estimate unknown parameters, but which method should you use. Posed as an alternative to the concept of mean-squared-error, PMC is based on the probabilities of the closeness of competing estimators to an unknown parameter.
On the nearness of record values to order statistics from Pitman measure of closeness. Metrika –[CrossRef], [Web of Science ®]View all references can be. InferenceNo. 1, 13–40 (; Zbl )]) based on different sample sizes for all symmetric stable distributions with exponent α (0.
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Preface This book presents a unified development of the origins, nature, methods, and applications of Pitman's measure of closeness (PMC) as a criterion in estimation. PMC is based on the probabilities of the closeness of competing estimators to an unknown parameter.
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