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Book Non Regular Statistical Estimation

Download or read book Non Regular Statistical Estimation written by Masafumi Akahira and published by Springer. This book was released on 1995-08-18 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to obtain many of the classical results in the theory of statistical estimation, it is usual to impose regularity conditions on the distributions under consideration. In small sample and large sample theories of estimation there are well established sets of regularity conditions, and it is worth while to examine what may follow if any one of these regularity conditions fail to hold. "Non-regular estimation" literally means the theory of statistical estimation when some or other of the regularity conditions fail to hold. In this monograph, the authors present a systematic study of the meaning and implications of regularity conditions, and show how the relaxation of such conditions can often lead to surprising conclusions. Their emphasis is on considering small sample results and to show how pathological examples may be considered in this broader framework.

Book Non Regular Statistical Estimation

Download or read book Non Regular Statistical Estimation written by Masafumi Akahira and published by . This book was released on 1995-08-18 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Non Regular Statistical Estimation

Download or read book Non Regular Statistical Estimation written by Masafumi Akahira and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to obtain many of the classical results in the theory of statistical estimation, it is usual to impose regularity conditions on the distributions under consideration. In small sample and large sample theories of estimation there are well established sets of regularity conditions, and it is worth while to examine what may follow if any one of these regularity conditions fail to hold. "Non-regular estimation" literally means the theory of statistical estimation when some or other of the regularity conditions fail to hold. In this monograph, the authors present a systematic study of the meaning and implications of regularity conditions, and show how the relaxation of such conditions can often lead to surprising conclusions. Their emphasis is on considering small sample results and to show how pathological examples may be considered in this broader framework.

Book Statistical Estimation for Truncated Exponential Families

Download or read book Statistical Estimation for Truncated Exponential Families written by Masafumi Akahira and published by Springer. This book was released on 2017-07-26 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents new findings on nonregular statistical estimation. Unlike other books on this topic, its major emphasis is on helping readers understand the meaning and implications of both regularity and irregularity through a certain family of distributions. In particular, it focuses on a truncated exponential family of distributions with a natural parameter and truncation parameter as a typical nonregular family. This focus includes the (truncated) Pareto distribution, which is widely used in various fields such as finance, physics, hydrology, geology, astronomy, and other disciplines. The family is essential in that it links both regular and nonregular distributions, as it becomes a regular exponential family if the truncation parameter is known. The emphasis is on presenting new results on the maximum likelihood estimation of a natural parameter or truncation parameter if one of them is a nuisance parameter. In order to obtain more information on the truncation, the Bayesian approach is also considered. Further, the application to some useful truncated distributions is discussed. The illustrated clarification of the nonregular structure provides researchers and practitioners with a solid basis for further research and applications.

Book Contributions to Statistical Estimation in Non regular Models

Download or read book Contributions to Statistical Estimation in Non regular Models written by Alireza Arasteh and published by . This book was released on 1989 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Asymptotic Theory of Statistical Estimation in Non regular Cases

Download or read book Asymptotic Theory of Statistical Estimation in Non regular Cases written by and published by . This book was released on 1979 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Non regular statistical estimation 2

Download or read book Non regular statistical estimation 2 written by and published by . This book was released on 1986 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Asymptotic theory of statistical estimation in non regular cases

Download or read book Asymptotic theory of statistical estimation in non regular cases written by Masafumi Akahira and published by . This book was released on 1979 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Estimation and Inferential Statistics

Download or read book Estimation and Inferential Statistics written by Pradip Kumar Sahu and published by Springer. This book was released on 2015-11-03 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the meaning of statistical inference and estimation. Statistical inference is concerned with the problems of estimation of population parameters and testing hypotheses. Primarily aimed at undergraduate and postgraduate students of statistics, the book is also useful to professionals and researchers in statistical, medical, social and other disciplines. It discusses current methodological techniques used in statistics and related interdisciplinary areas. Every concept is supported with relevant research examples to help readers to find the most suitable application. Statistical tools have been presented by using real-life examples, removing the “fear factor” usually associated with this complex subject. The book will help readers to discover diverse perspectives of statistical theory followed by relevant worked-out examples. Keeping in mind the needs of readers, as well as constantly changing scenarios, the material is presented in an easy-to-understand form.

Book STATISTICAL INFERENCE FOR NON REGULAR FAMILY OF DISTRIBUTIONS  UNIFIED THEORY

Download or read book STATISTICAL INFERENCE FOR NON REGULAR FAMILY OF DISTRIBUTIONS UNIFIED THEORY written by Milind B. Bhatt and published by Lulu.com. This book was released on with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Statistical Estimation

Download or read book Statistical Estimation written by I.A. Ibragimov and published by Springer. This book was released on 1981-06-30 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Non standard Parametric Statistical Inference

Download or read book Non standard Parametric Statistical Inference written by Russell Cheng and published by Oxford University Press. This book was released on 2017 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research monograph gives a unified view of non-standard estimation problems. It provides an overall mathematical framework, but also draws together and studies in detail a large number of practical problems, previously only treated separately, offering solution methods and numerical procedures for each.

Book Joint Statistical Papers Of Akahira And Takeuchi

Download or read book Joint Statistical Papers Of Akahira And Takeuchi written by Masafumi Akahira and published by World Scientific. This book was released on 2003-08-13 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: Masafumi Akahira and Kei Takeuchi have collaborated in research on mathematical statistics for nearly thirty years and have published many articles and papers. This volume is a collection of their papers, some published in well-known and others in lesser-known journals. The papers cover various fields, but the main subject is the theory of estimation — asymptotic, non-regular, sequential, etc. All the papers are theoretical in nature, but have implications for applied problems.

Book Robust and Non robust Models in Statistics

Download or read book Robust and Non robust Models in Statistics written by Lev Borisovich Klebanov and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book the authors consider so-called ill-posed problems and stability in statistics. Ill-posed problems are certain results where arbitrary small changes in the assumptions lead to unpredictable large changes in the conclusions. In a companion problem published by Nova, the authors explain that ill-posed problems are not a mere curiosity in the field of contemporary probability. The same situation holds in statistics. The objective of the authors of this book is to (1)identify statistical problems of this type, (2) find their stable variant, and (3)propose alternative versions of numerous theorems in mathematical statistics. The layout of the book is as follows. The authors begin by reviewing the central pre-limit theorem, providing a careful definition and characterisation of the limiting distributions. Then, they consider pre-limiting behaviour of extreme order statistics and the connection of this theory to survival analysis. A study of statistical applications of the pre-limit theorems follows. Based on these theorems, the authors develop a correct version of the theory of statistical estimation, and show its connection with the problem of the choice of an appropriate loss function. As It turns out, a loss function should not be chosen arbitrarily. As they explain, the availability of certain mathematical conveniences (including the correctness of the formulation of the problem estimation) leads to rigid restrictions on the choice of the loss function. The questions about the correctness of incorrectness of certain statistical problems may be resolved through appropriate choice of the loss function and/or metric on the space of random variables and their characteristics (including distribution functions, characteristic functions, and densities). Some auxiliary results from the theory of generalised functions are provided in an appendix.

Book Learning Statistics with R

Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

Book Asymptotic Efficiency of Statistical Estimators  Concepts and Higher Order Asymptotic Efficiency

Download or read book Asymptotic Efficiency of Statistical Estimators Concepts and Higher Order Asymptotic Efficiency written by Masafumi Akahira and published by Springer. This book was released on 2011-11-22 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is a collection of results recently obtained by the authors. Most of these have been published, while others are awaitlng publication. Our investigation has two main purposes. Firstly, we discuss higher order asymptotic efficiency of estimators in regular situa tions. In these situations it is known that the maximum likelihood estimator (MLE) is asymptotically efficient in some (not always specified) sense. However, there exists here a whole class of asymptotically efficient estimators which are thus asymptotically equivalent to the MLE. It is required to make finer distinctions among the estimators, by considering higher order terms in the expansions of their asymptotic distributions. Secondly, we discuss asymptotically efficient estimators in non regular situations. These are situations where the MLE or other estimators are not asymptotically normally distributed, or where l 2 their order of convergence (or consistency) is not n / , as in the regular cases. It is necessary to redefine the concept of asympto tic efficiency, together with the concept of the maximum order of consistency. Under the new definition as asymptotically efficient estimator may not always exist. We have not attempted to tell the whole story in a systematic way. The field of asymptotic theory in statistical estimation is relatively uncultivated. So, we have tried to focus attention on such aspects of our recent results which throw light on the area.