Автор: Paul P. Eggermont; Vincent N. LaRiccia Название: Maximum Penalized Likelihood Estimation ISBN: 0387402675 ISBN-13(EAN): 9780387402673 Издательство: Springer Рейтинг: Цена: 19056 р. Наличие на складе: Поставка под заказ.
Описание: This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in statistics,
operations research and applied mathematics, as well as for researchers and practitioners in the field. The present volume deals with nonparametric regression.
in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. Smoothing splines and local polynomials are
studied in the context of reproducing kernel Hilbert spaces. The connection between smoothing splines and reproducing kernels is of course well-known.
The new twist is
that letting the innerproduct depend on the smoothing parameter opens up new possibilities. It leads to asymptotically equivalent reproducing kernel estimators (without qualifications),
and thence, via uniform error bounds for kernel estimators, to uniform
rror bounds for smoothing splines and via strong approximations, to confidence bands for the unknown regression function. The reason for studying smoothing splines of arbitrary order is
that one wants to use them for data analysis.
Regarding the actual computation, the usual scheme based on spline interpolation is useful for cubic smoothing splines only.
For splines of arbitrary order, the Kalman filter is the most important method, the intricacies of which are explained in full. The authors also discuss simulation results for smoothing splines
and local and global polynomials for a variety of test problems as well as results on confidence bands for the unknown regression function based on undersmoothed quintic smoothing
splines with remarkably good coverage probabilities.
Автор: Owen Название: Empirical Likelihood ISBN: 1584880716 ISBN-13(EAN): 9781584880714 Издательство: Taylor&Francis Рейтинг: Цена: р. Наличие на складе: Невозможна поставка.
Описание: One of the first books published on the subject, Empirical Likelihood offers an in-depth treatment of this method for constructing confidence regions and testing hypotheses. The author applies the method to a range of problems, from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data. Numerous examples from a variety of disciplines and detailed descriptions of algorithms-also posted on a companion Web site-illustrate the methods in practice.
Автор: Ahmed S.E., Reid N. Название: Empirical Bayes and Likelihood Inference ISBN: 0387950184 ISBN-13(EAN): 9780387950181 Издательство: Springer Рейтинг: Цена: 11549 р. Наличие на складе: Поставка под заказ.
Описание: Bayesian and likelihood approaches to inference have a number of points of close contact, especially from an asymptotic point of view. Both approaches emphasize the construction of interval estimates of unknown parameters. Empirical Bayes methods have historically emphasized instead the construction of point estimates. In this volume researchers present recent work on several aspects of Bayesian, likelihood and empirical Bayes methods, presented at a workshop held in Montreal, Canada. The goal of the workshop was to explore the linkages among the methods, and to suggest new directions for research in the theory of inference.
Описание: This book represents the refereed proceedings of the Fifth International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing which was held at the National University of Singapore in the year 2002. An important feature are invited surveys of the state of the art in key areas such as multidimensional numerical integration, low-discrepancy point sets, computational complexity, finance, and other applications of Monte Carlo and quasi-Monte Carlo methods. These proceedings also include carefully selected contributed papers on all aspects of Monte Carlo and quasi-Monte Carlo methods. The reader will be informed about current research in this very active area.
Описание: Quasi-Monte Carlo methods have become an increasingly popular alternative to Monte Carlo methods over the years. This book presents essential tools for using quasi-Monte Carlo sampling in practice. It is suitable for graduate students in statistics, management science, operations research, engineering, and applied mathematics.
This book presents the refereed proceedings of the Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing that was held at the University of Leuven (Belgium) in April 2014. These biennial conferences are major events for Monte Carlo and quasi-Monte Carlo researchers. The proceedings include articles based on invited lectures as well as carefully selected contributed papers on all theoretical aspects and applications of Monte Carlo and quasi-Monte Carlo methods. Offering information on the latest developments in these very active areas, this book is an excellent reference resource for theoreticians and practitioners interested in solving high-dimensional computational problems, arising, in particular, in finance, statistics and computer graphics.
Описание: This book shows how techniques from the perturbation theory of operators, applied to a quasi-compact positive kernel, may be used to obtain limit theorems for Markov chains or to describe stochastic properties of dynamical systems.A general framework for this method is given and then applied to treat several specific cases. An essential element of this work is the description of the peripheral spectra of a quasi-compact Markov kernel and of its Fourier-Laplace perturbations. This is first done in the ergodic but non-mixing case. This work is extended by the second author to the non-ergodic case.The only prerequisites for this book are a knowledge of the basic techniques of probability theory and of notions of elementary functional analysis.
Автор: Loader Название: Local Regression and Likelihood ISBN: 0387987754 ISBN-13(EAN): 9780387987750 Издательство: Springer Рейтинг: Цена: 19056 р. Наличие на складе: Поставка под заказ.
Описание: Gives you 2,000 problems in discrete mathematics. This guide helps you to master various types of problems you will face on your tests, from simple questions on set theory to complex Boolean algebra, logic gates, and the use of propositional calculus. Smoothing methods play an important role in many areas of statistics. This book explains how to implement these methods in several popular statistical programs including S-PLUS.
Описание: Professor Johansen gives a detailed mathematical and statistical analysis of the co-integrated vector autoregressive model in a self-contained presentation for graduate students and researchers with a good knowledge of multivariate regression analysis and likelihood methods. Many exercises are provided.
Автор: Schweder Название: Confidence, Likelihood, Probability ISBN: 0521861608 ISBN-13(EAN): 9780521861601 Издательство: Cambridge Academ Рейтинг: Цена: 9311 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. The generous mixture of theory, illustrations, applications and exercises is suitable for statisticians at all levels of experience, as well as for data-oriented scientists. Some confidence distributions are less dispersed than their competitors. This concept leads to a theory of risk functions and comparisons for distributions of confidence. Neyman–Pearson type theorems leading to optimal confidence are developed and richly illustrated. Exact and optimal confidence distribution is the gold standard for inferred epistemic distributions. Confidence distributions and likelihood functions are intertwined, allowing prior distributions to be made part of the likelihood. Meta-analysis in likelihood terms is developed and taken beyond traditional methods, suiting it in particular to combining information across diverse data sources.
Автор: Pawitan Yudi Название: In All Likelihood ISBN: 0199671222 ISBN-13(EAN): 9780199671229 Издательство: Oxford Academ Рейтинг: Цена: 6230 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces likelihood as a unifying concept in statistical modelling and inference. The complete range of concepts and applications are covered, from very simple to very complex studies. It relies on realistic examples, and presents the main results using heuristic rather than formal mathematical arguments.
Описание: Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently. This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style, and have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience. The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a Bayesian perspective. An effort has been made to relate biological to statistical parameters throughout, and examples are used profusely to motivate the developments. Daniel Sorensen is a Research Professor in Statistical Genetics, at the Department of Animal Breeding and Genetics in the Danish Institute of Agricultural Sciences. Daniel Gianola is Professor in the Animal Sciences, Biostatistics and Medical Informatics, and Dairy Science Departments of the University of Wisconsin-Madison. Gianola and Sorensen pioneered the introduction of Bayesian and MCMC methods in animal breeding. The authors have published and lectured extensively in applications of statistics to quantitative genetics.
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