Essential Statistical Inference: Theory And Methods

Essential Statistical Inference: Theory and Methods (repost)

Essential Statistical Inference: Theory and Methods by Dennis D. Boos and Leonard A . Stefanski
English | 2013-02-06 | ISBN: 1461448174 | PDF | 587 pages | 5,5 MB

Essential Statistical Inference: Theory and Methods (Repost)  

Posted by Specialselection at Jan. 11, 2014
Essential Statistical Inference: Theory and Methods (Repost)

Dennis D. Boos, Leonard A . Stefanski, "Essential Statistical Inference: Theory and Methods"
English | 2013-02-06 | ISBN: 1461448174 | 566 pages | PDF | 5.5 mb

Essential Statistical Inference: Theory and Methods [Repost]  

Posted by ChrisRedfield at Aug. 3, 2013
Essential Statistical Inference: Theory and Methods [Repost]

Dennis D. Boos, ‎L. A. Stefanski - Essential Statistical Inference: Theory and Methods
Published: 2013-02-06 | ISBN: 1461448174 | PDF | 587 pages | 5 MB
Models for Probability and Statistical Inference: Theory and Applications

Models for Probability and Statistical Inference: Theory and Applications
by James H. Stapleton
English | 2007 | ISBN: 0470073721 | 464 pages | PDF | 28.95 MB

Critically Reading the Theory and Methods of Archaeology: An Introductory Guide  eBooks & eLearning

Posted by nebulae at Nov. 26, 2016
Critically Reading the Theory and Methods of Archaeology: An Introductory Guide

Guy Gibbon, "Critically Reading the Theory and Methods of Archaeology: An Introductory Guide"
English | ISBN: 0759123403, 0759123411 | 2013 | 255 pages | PDF | 5 MB

Optimization Theory and Methods {Repost}  eBooks & eLearning

Posted by tanas.olesya at Sept. 21, 2016
Optimization Theory and Methods {Repost}

Optimization Theory and Methods: Nonlinear Programming by Wenyu Sun
English | 24 May 2006 | ISBN: 0387249753 | 688 Pages | PDF | 3 MB

Optimization Theory and Methods can be used as a textbook for an optimization course for graduates and senior undergraduates.

A Course in Stochastic Processes: Stochastic Models and Statistical Inference  eBooks & eLearning

Posted by fdts at Sept. 14, 2016
A Course in Stochastic Processes: Stochastic Models and Statistical Inference

A Course in Stochastic Processes: Stochastic Models and Statistical Inference (Theory and Decision Library B)
by Denis Bosq (Author), Hung T. Nguyen
English | 1996 | ISBN: 9048147131 | 354 pages | PDF | 6.23 MB
Statistical Analysis of Reliability and Life-Testing Models: Theory and Methods, Second Edition,

Statistical Analysis of Reliability and Life-Testing Models: Theory and Methods, Second Edition, (Statistics: A Series of Textbooks and Monographs) by Lee Bain
English | Mar. 29, 1991 | ISBN: 0824785061 | 512 Pages | PDF | 15.06 MB

Textbook for a methods course or reference for an experimenter who is mainly interested in data analyses rather than in the mathematical development of the procedures.

Statistical Matching: Theory and Practice  

Posted by fdts at Oct. 27, 2014
Statistical Matching: Theory and Practice

Statistical Matching: Theory and Practice
by Marcello D'Orazio, Marco Di Zio, Mauro Scanu
English | 2006 | ISBN: 0470023538 | 268 pages | PDF | 3.6 MB

Applied Statistical Inference: Likelihood and Bayes  

Posted by AlenMiler at Aug. 19, 2014
Applied Statistical Inference: Likelihood and Bayes

Applied Statistical Inference: Likelihood and Bayes by
November 25, 2013 | ISBN: 3642378862 | Pages: 376 | PDF | 8 MB

This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective.

A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.