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Author: Ishwar V. Basawa
Publisher:
ISBN: 9783540908104
Category : Asymptotic efficiencies (Statistics)
Languages : en
Pages : 170
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Book Description
Author: Ishwar V. Basawa
Publisher:
ISBN: 9783540908104
Category : Asymptotic efficiencies (Statistics)
Languages : en
Pages : 170
Get Book Here
Book Description
Author: I. V. Basawa
Publisher: Springer Science & Business Media
ISBN: 1461255058
Category : Mathematics
Languages : en
Pages : 183
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Book Description
This monograph contains a comprehensive account of the recent work of the authors and other workers on large sample optimal inference for non-ergodic models. The non-ergodic family of models can be viewed as an extension of the usual Fisher-Rao model for asymptotics, referred to here as an ergodic family. The main feature of a non-ergodic model is that the sample Fisher information, appropriately normed, converges to a non-degenerate random variable rather than to a constant. Mixture experiments, growth models such as birth processes, branching processes, etc. , and non-stationary diffusion processes are typical examples of non-ergodic models for which the usual asymptotics and the efficiency criteria of the Fisher-Rao-Wald type are not directly applicable. The new model necessitates a thorough review of both technical and qualitative aspects of the asymptotic theory. The general model studied includes both ergodic and non-ergodic families even though we emphasise applications of the latter type. The plan to write the monograph originally evolved through a series of lectures given by the first author in a graduate seminar course at Cornell University during the fall of 1978, and by the second author at the University of Munich during the fall of 1979. Further work during 1979-1981 on the topic has resolved many of the outstanding conceptual and technical difficulties encountered previously. While there are still some gaps remaining, it appears that the mainstream development in the area has now taken a more definite shape.
Author: I. V Basawa
Publisher:
ISBN: 9781461255062
Category :
Languages : en
Pages : 188
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Book Description
Author: Sun Young Hwang
Publisher:
ISBN:
Category :
Languages : en
Pages : 230
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Book Description
Author: A. C. Davison
Publisher: Cambridge University Press
ISBN: 1139437410
Category : Mathematics
Languages : en
Pages : 1026
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Book Description
Models and likelihood are the backbone of modern statistics. This 2003 book gives an integrated development of these topics that blends theory and practice, intended for advanced undergraduate and graduate students, researchers and practitioners. Its breadth is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics such as likelihood and linear and generalized linear models. Each chapter contains a wide range of problems and exercises. Practicals in the S language designed to build computing and data analysis skills, and a library of data sets to accompany the book, are available over the Web.
Author: Martin T. Wells
Publisher: Springer Science & Business Media
ISBN: 3790826286
Category : Mathematics
Languages : en
Pages : 326
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Book Description
The present volume consists of papers written by students, colleagues and collaborators of Sreenivasa Rao Jammalamadaka from various countries, and covers a variety of research topics which he enjoys and contributed immensely to.
Author: Narahari Umanath Prabhu
Publisher: American Mathematical Soc.
ISBN: 0821850873
Category : Mathematics
Languages : en
Pages : 406
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Book Description
Comprises the proceedings of the AMS-IMS-SIAM Summer Research Conference on Statistical Inference from Stochastic Processes, held at Cornell University in August 1987. This book provides students and researchers with a familiarity with the foundations of inference from stochastic processes and intends to provide a knowledge of the developments.
Author: Jaya P. N. Bishwal
Publisher: Springer
ISBN: 3540744487
Category : Mathematics
Languages : en
Pages : 271
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Book Description
Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
Author: Shun-ichi Amari
Publisher: Springer Science & Business Media
ISBN: 1461250560
Category : Mathematics
Languages : en
Pages : 302
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Book Description
From the reviews: "In this Lecture Note volume the author describes his differential-geometric approach to parametrical statistical problems summarizing the results he had published in a series of papers in the last five years. The author provides a geometric framework for a special class of test and estimation procedures for curved exponential families. ... ... The material and ideas presented in this volume are important and it is recommended to everybody interested in the connection between statistics and geometry ..." #Metrika#1 "More than hundred references are given showing the growing interest in differential geometry with respect to statistics. The book can only strongly be recommended to a geodesist since it offers many new insights into statistics on a familiar ground." #Manuscripta Geodaetica#2
Author: Jens Breckling
Publisher: Springer Science & Business Media
ISBN: 1461236886
Category : Mathematics
Languages : en
Pages : 236
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Book Description
Given a series of wind speeds and directions from the port of Fremantle the aim of this monograph is to detect general weather patterns and seasonal characteristics. To separate the daily land and sea breeze cycle and other short-term disturbances from the general wind, the series is divided into a daily and a longer term, synoptic component. The latter is related to the atmospheric pressure field, while the former is studied in order i) to isolate particular short-term events such as calms, storms and oscillating winds, and ii) to determine the land and sea breeze cycle which dominates the weather pattern for most of the year. All these patterns are described in detail and are related to the synoptic component of the data. Two time series models for directional data and a new measure of angular association are introduced to provide the basis for certain parts of the analysis.
