1.

Record Nr.

UNINA9910273520803321

Titolo

Bookmarks

Pubbl/distr/stampa

San Mateo, CA, : Phillips & Nelson Media, 2002-

Berkeley, CA, : Bookmarks Publishing, LLC

ISSN

1546-0657

Descrizione fisica

volumes : illustration (some col.), ports. ; ; 28 cm

Disciplina

011

Soggetti

Books

Books - United States

Books and reading

Books and reading - United States

Periodicals.

Reviews.

United States

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Periodico

Note generali

"For everyone who hasn't read everything."

Title from cover.

Published: Mill Valley, Calif., 2003-



2.

Record Nr.

UNINA9910254274903321

Autore

Akahira Masafumi

Titolo

Statistical Estimation for Truncated Exponential Families / / by Masafumi Akahira

Pubbl/distr/stampa

Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2017

ISBN

981-10-5296-4

Edizione

[1st ed. 2017.]

Descrizione fisica

1 online resource (XI, 122 p. 10 illus.)

Collana

JSS Research Series in Statistics, , 2364-0065

Disciplina

519.544

Soggetti

Statistics

Mathematical statistics - Data processing

Statistical Theory and Methods

Statistics and Computing

Statistics in Business, Management, Economics, Finance, Insurance

Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references at the end of each chapters and index.

Sommario/riassunto

This book presents new findings on nonregular statistical estimation. Unlike other books on this topic, its major emphasis is on helping readers understand the meaning and implications of both regularity and irregularity through a certain family of distributions. In particular, it focuses on a truncated exponential family of distributions with a natural parameter and truncation parameter as a typical nonregular family. This focus includes the (truncated) Pareto distribution, which is widely used in various fields such as finance, physics, hydrology, geology, astronomy, and other disciplines. The family is essential in that it links both regular and nonregular distributions, as it becomes a regular exponential family if the truncation parameter is known. The emphasis is on presenting new results on the maximum likelihood estimation of a natural parameter or truncation parameter if one of them is a nuisance parameter. In order to obtain more information on the truncation, the Bayesian approach is also considered. Further, the



application to some useful truncated distributions is discussed. The illustrated clarification of the nonregular structure provides researchers and practitioners with a solid basis for further research and applications.