Maximum Likelihood Estimation

Maximum Likelihood Estimation with Stata, Fourth Edition by Jeffrey Pitblado [Repost]

Maximum Likelihood Estimation with Stata, Fourth Edition by Jeffrey Pitblado
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.
Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB

Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB
by Russell B. Millar
English | 2011 | ISBN: 0470094826 | 357 pages | PDF | 3.25 MB

Maximum Likelihood Estimation with Stata, Fourth Edition  

Posted by interes at April 10, 2014
Maximum Likelihood Estimation with Stata, Fourth Edition

Maximum Likelihood Estimation with Stata, Fourth Edition by William Gould, Jeffrey Pitblado and Brian Poi
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.

Maximum Likelihood Estimation: Logic and Practice  

Posted by parvathareddyrs at March 21, 2009
Maximum Likelihood Estimation: Logic and Practice

Scott R. Eliason,"Maximum Likelihood Estimation: Logic and Practice"
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.
Maximum Likelihood Estimation with Stata, Fourth Edition (repost)

Maximum Likelihood Estimation with Stata, Fourth Edition by William Gould, Jeffrey Pitblado and Brian Poi
English | 2010 | ISBN: 1597180785 | ISBN-13: 9781597180788 | 352 pages | PDF | 1,9 MB

Maximum Likelihood Estimation for Sample Surveys [Repost]  

Posted by ChrisRedfield at Dec. 15, 2014
Maximum Likelihood Estimation for Sample Surveys [Repost]

Raymond L. Chambers, David G. Steel, Suojin Wang, Alan Welsh - Maximum Likelihood Estimation for Sample Surveys
Published: 2012-05-02 | ISBN: 1584886323 | PDF | 391 pages | 3 MB

Maximum Likelihood Estimation for Sample Surveys  

Posted by interes at Oct. 1, 2013
Maximum Likelihood Estimation for Sample Surveys

Maximum Likelihood Estimation for Sample Surveys by Raymond L. Chambers, David G. Steel, Suojin Wang and Alan Welsh
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.
Econometric Modelling with Time Series: Specification, Estimation and Testing

Econometric Modelling with Time Series: Specification, Estimation and Testing (Themes in Modern Econometrics) by Vance Martin, Stan Hurn and David Harris
English | 2012 | ISBN: 0521139813 , 0521196604 | 937 pages | PDF | 7,5 MB

This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation.
Parametrische Statistik: Verteilungen, Maximum Likelihood und GLM in R (Repost)

Parametrische Statistik: Verteilungen, Maximum Likelihood und GLM in R By Carsten Dormann
2013 | 362 Pages | ISBN: 3642347851 | PDF | 4 MB

Econometric Applications of Maximum Likelihood Methods  

Posted by step778 at June 2, 2014
Econometric Applications of Maximum Likelihood Methods

Jan Salomon Cramer, "Econometric Applications of Maximum Likelihood Methods"
1986 | pages: 222 | ISBN: 0521253179, 0521378575 | PDF | 5,9 mb