ASYMPTOTIC DISTRIBUTION AND APPLICATIONS OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE INDEPENDENT NOT IDENTICALLY DISTRIBUTED CASE.. PDF Download
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Author: LIH-WEN HUANG
Publisher:
ISBN:
Category :
Languages : en
Pages : 59
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Author: LIH-WEN HUANG
Publisher:
ISBN:
Category :
Languages : en
Pages : 59
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Book Description
Author: A. N. Philippou
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
In the paper, the authors assume the existence and consistency of the maximum likelihood estimate (MLE) in the independent not identically distributed (i.n.i.d.) case and the authors establish its asymptotic normality. The regularity conditions employed do not involve the third order derivatives of the underlying probability density functions (p.d.f.'s). (Author).
Author: Andreas N. Philippou
Publisher:
ISBN:
Category :
Languages : en
Pages : 37
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Author: Andreas N. Philippou
Publisher:
ISBN:
Category :
Languages : en
Pages : 350
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Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 748
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Book Description
Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.
Author: Wilfried Grossmann
Publisher: Springer Science & Business Media
ISBN: 9789027725479
Category : Mathematics
Languages : en
Pages : 482
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Book Description
Proceedings of the 5th Pannonian Symposium, Visegrad, Hungary, May 20-24, 1985
Author: I.A. Ibragimov
Publisher: Springer
ISBN:
Category : Mathematics
Languages : en
Pages : 464
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Book Description
when certain parameters in the problem tend to limiting values (for example, when the sample size increases indefinitely, the intensity of the noise ap proaches zero, etc.) To address the problem of asymptotically optimal estimators consider the following important case. Let X 1, X 2, ... , X n be independent observations with the joint probability density !(x,O) (with respect to the Lebesgue measure on the real line) which depends on the unknown patameter o e 9 c R1. It is required to derive the best (asymptotically) estimator 0:( X b ... , X n) of the parameter O. The first question which arises in connection with this problem is how to compare different estimators or, equivalently, how to assess their quality, in terms of the mean square deviation from the parameter or perhaps in some other way. The presently accepted approach to this problem, resulting from A. Wald's contributions, is as follows: introduce a nonnegative function w(0l> ( ), Ob Oe 9 (the loss function) and given two estimators Of and O! n 2 2 the estimator for which the expected loss (risk) Eown(Oj, 0), j = 1 or 2, is smallest is called the better with respect to Wn at point 0 (here EoO is the expectation evaluated under the assumption that the true value of the parameter is 0). Obviously, such a method of comparison is not without its defects.
Author: A A Borokov
Publisher: Routledge
ISBN: 1351433105
Category : Mathematics
Languages : en
Pages : 592
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Book Description
A wide-ranging, extensive overview of modern mathematical statistics, this work reflects the current state of the field while being succinct and easy to grasp. The mathematical presentation is coherent and rigorous throughout. The author presents classical results and methods that form the basis of modern statistics, and examines the foundations o
Author: Madan L. Puri
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110942003
Category : Mathematics
Languages : en
Pages : 456
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Book Description
No detailed description available for "Asymptotics in Statistics and Probability".
Author: Jeffrey M. Wooldridge
Publisher: MIT Press
ISBN: 0262296799
Category : Business & Economics
Languages : en
Pages : 1095
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Book Description
The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
