Posted by **libr** at Jan. 23, 2016

English | 2010 | ISBN: 1597180785 | ISBN-13: 9781597180788 | 352 pages | PDF | 1,9 MB

Posted by **tanas.olesya** at Oct. 18, 2015

English | 29 Nov. 2010 | ISBN: 1597180785 | 376 Pages | PDF | 1 MB

Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines.

Posted by **fdts** at March 1, 2015

by Russell B. Millar

English | 2011 | ISBN: 0470094826 | 357 pages | PDF | 3.25 MB

Posted by **ChrisRedfield** at Dec. 15, 2014

Published: 2012-05-02 | ISBN: 1584886323 | PDF | 391 pages | 3 MB

Posted by **interes** at April 10, 2014

English | 2010 | ISBN: 1597180785 | ISBN-13: 9781597180788 | 352 pages | PDF | 1,9 MB

Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata.

Posted by **interes** at Oct. 1, 2013

English | 2012 | ISBN: 1584886323 | 391 pages | PDF | 3,1 MB

Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates.

Posted by **parvathareddyrs** at March 21, 2009

Sage Publications | 1993-08-09 | ISBN: 0803941072 | 96 pages | PDF | 1.5 mb

Eliason reveals to the reader the underlying logic and practice of maximum likelihood (ML) estimation by providing a general modeling framework that utilizes the tools of ML methods. This framework offers readers a flexible modeling strategy since it accommodates cases from the simplest linear models (such as the normal error regression model) to the most complex nonlinear models that link a system of endogenous and exogenous variables with non-normal distributions. Using examples to illustrate the techniques of finding ML estimators and estimates, Eliason discusses what properties are desirable in an estimator, basic techniques for finding maximum likelihood solutions, the general form of the covariance matrix for ML estimates, the sampling distribution of ML estimators; the use of ML in the normal as well as other distributions, and some useful illustrations of likelihoods.

Posted by **step778** at May 15, 2018

2004 | pages: 354 | ISBN: 8122415563 | PDF | 2,2 mb

Posted by **AvaxGenius** at April 14, 2018

English | PDF | 2017 | 133 Pages | ISBN : 9811052956 | 2.33 MB

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.

Posted by **hill0** at Jan. 22, 2018

German | 8 Feb. 2013 | ISBN: 3642347851 | 372 Pages | PDF | 4.38 MB

Beispielreich baut das Buch Schritt für Schritt die statistischen Grundlagen moderner Datenanalysen für Anwender auf. Dabei wird besonderer Wert auf einen roten Faden gelegt, der alle Methoden