Asymptotic Variance as an Approximation to Expected Loss for Maximum Likelihood Estimates PDF Download
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Author: William Dollard Commins (Jr.)
Publisher:
ISBN:
Category : Asymptotes
Languages : en
Pages : 116
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Book Description
Author: William Dollard Commins (Jr.)
Publisher:
ISBN:
Category : Asymptotes
Languages : en
Pages : 116
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Book Description
Author: Barbara Bacchielli
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Herman Chernoff
Publisher: SIAM
ISBN: 9781611970593
Category : Technology & Engineering
Languages : en
Pages : 124
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Book Description
An exploration of the interrelated fields of design of experiments and sequential analysis with emphasis on the nature of theoretical statistics and how this relates to the philosophy and practice of statistics.
Author: Lucien Marie Le Cam
Publisher:
ISBN:
Category : Asymptotic expansions
Languages : en
Pages : 64
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Book Description
Author: Goodwin
Publisher: Academic Press
ISBN: 0080956459
Category : Computers
Languages : en
Pages : 303
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Book Description
Dynamic System Identification: Experiment Design and Data Analysis
Author:
Publisher:
ISBN:
Category : Science
Languages : en
Pages : 156
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Book Description
Author:
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 604
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Book Description
Author: Isabel Elaine Allen
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 512
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Book Description
Author: Stuart A. Klugman
Publisher: John Wiley & Sons
ISBN: 1119523737
Category : Business & Economics
Languages : en
Pages : 554
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Book Description
A guide that provides in-depth coverage of modeling techniques used throughout many branches of actuarial science, revised and updated Now in its fifth edition, Loss Models: From Data to Decisions puts the focus on material tested in the Society of Actuaries (SOA) newly revised Exams STAM (Short-Term Actuarial Mathematics) and LTAM (Long-Term Actuarial Mathematics). Updated to reflect these exam changes, this vital resource offers actuaries, and those aspiring to the profession, a practical approach to the concepts and techniques needed to succeed in the profession. The techniques are also valuable for anyone who uses loss data to build models for assessing risks of any kind. Loss Models contains a wealth of examples that highlight the real-world applications of the concepts presented, and puts the emphasis on calculations and spreadsheet implementation. With a focus on the loss process, the book reviews the essential quantitative techniques such as random variables, basic distributional quantities, and the recursive method, and discusses techniques for classifying and creating distributions. Parametric, non-parametric, and Bayesian estimation methods are thoroughly covered. In addition, the authors offer practical advice for choosing an appropriate model. This important text: • Presents a revised and updated edition of the classic guide for actuaries that aligns with newly introduced Exams STAM and LTAM • Contains a wealth of exercises taken from previous exams • Includes fresh and additional content related to the material required by the Society of Actuaries (SOA) and the Canadian Institute of Actuaries (CIA) • Offers a solutions manual available for further insight, and all the data sets and supplemental material are posted on a companion site Written for students and aspiring actuaries who are preparing to take the SOA examinations, Loss Models offers an essential guide to the concepts and techniques of actuarial science.
Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 556
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Book Description
The problem considered is the estimation of the parameters in the mixed model of the analysis of variance, assuming normality of the random effects and errors. Both asymptotic properties of such estimates as the size of the design increases and numerical procedures for their calculation are discussed. Estimation is carried out by the method of maximum likelihood. It is shown that there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal and asymptotically efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix as the size of the design increases. This is accomplished using a Taylor series expansion of the log-likelihood. (Modified author abstract).
