New York: Springer, 1981. — 252 p. 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...
New York: Springer, 1995. — 191 p. 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...
Chapman & Hall/CRC, 2012. — 356 p. — ISBN: 978-1584886327, e-ISBN: 978-1420011357. Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and...
Routledge, 2024. — 611 p. This fully revised and updated second edition is an essential introduction to inferential statistics. It is the first introductory statistics text to use an estimation approach from the start and also to explain the new and exciting Open Science practices, which encourage replication and enhance the trustworthiness of research. The estimation approach,...
Springer, 2021. — 540 p. — ISBN 978-981-15-9002-3. The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed,...
New York: Springer, 2016. — 222 p. This book brings together selected peer-reviewed contributions from various research fields in statistics, and highlights the diverse approaches and analyses related to real-life phenomena. Major topics covered in this volume include, but are not limited to, bayesian inference, likelihood approach, pseudo-likelihoods, regression, time series,...
Springer, 1999. — 423 p. Appropriate for a one-semester course, this self-contained book is an introduction to nonparametric curve estimation theory. It may be used for teaching graduate students in statistics (in this case an intermediate course in statistical inference, on the level of the book by Casella and Berger (1990), is the prerequisite) as well as for diverse classes...
Springer, 2001. — 514 p. This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. We cover both theory and practice of nonparametric estimation. The text is novel in its use of maximum penalized likelihood estimation, and the theory of convex minimization problems (fully developed in the text)...
Hoboken: Wiley-Interscience, 2006. — 657 p. Theory of Preliminary Test and Stein-Type Estimation with Applications provides a com-prehensive account of the theory and methods of estimation in a variety of standard models used in applied statistical inference. It is an in-depth introduction to the estimation theory for graduate students, practitioners, and researchers in various...
New York: Academic Press, 1967. — 396 pages. The theory of games is a part of the rich mathematical legacy left by John von Neumann, one of the outstanding mathematicians of our era. Although others — notably Emil Borel — preceded him in formulating a theory of games, it was von Neumann who with the publication in 1927 of a proof of the minimax theorem for finite games laid the...
Wiesbaden: Springer, 2013. — 130 p. Paola Gloria Ferrario develops and investigates several methods of nonparametric local variance estimation. The first two methods use regression estimations (plug-in), achieving least squares estimates as well as local averaging estimates (partitioning or kernel type). Furthermore, the author uses a partitioning method for the estimation of the...
Springer, 2018. — 339 p. — (Springer Series in Statistics). — ISBN: 303002184X. This book provides a coherent framework for understanding shrinkage estimation in statistics. The term refers to modifying a classical estimator by moving it closer to a target which could be known a priori or arise from a model. The goal is to construct estimators with improved statistical...
FOA Repro, 1971. — 287 p. Introduction Graphs Stochastic Graphs Inference From Sampled Subgraphs Inference From Sampled Partial Graphs Inference From Flow Measurements Inference From Stochastic Contact Graphs Inference From Stochastic Preference Graphs References Author Index Subject Index
Hoboken: Wiley, 1997. — 504 p. The only comprehensive guide to the theory and practice of one of today's most important probabilistic techniquesThe past 15 years have witnessed many significant advances in sequential estimation, especially in the areas of three-stage and nonparametric methodology. Yet, until now, there were no references devoted exclusively to this rapidly...
New York: Springer, 2020. — 167 p. This book offers a modern and accessible introduction to Statistical Inference, the science of inferring key information from data. Aimed at beginning undergraduate students in mathematics, it presents the concepts underpinning frequentist statistical theory. Written in a conversational and informal style, this concise text concentrates on ideas...
Princeton, 2017. — 336 p. — ISBN: 978-0691174129 Multimethod research has become indispensable to doing social science, and is essential to anyone who conducts large-scale research projects in political science, sociology, education, comparative law, or business. This authoritative and accessible book offers the first truly comprehensive approach to multimethod and case-study...
Springer International Publishing AG, 2018. — 197 p. — (Studies in Big Data 37) — ISBN: 3319716875. This book describes computational problems related to kernel density estimation (KDE) - one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented....
Cambridge: Cambridge University Press, 2014. — 428 p. — ISBN: 978-0-521-86401-5. This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Among the topics treated are current status and interval censoring models, competing risk models, and deconvolution. Methods of order...
N.-Y.: Elsevier Inc, Academic Press, 1990. — 320 p. — ISBN: 0-12-304752-8. An examination of mathematical formulations of ridge-regression-type estimators points to a curious observation: estimators can be derived by both Bayesian and Frequentist methods. In this updated and expanded edition of his 1990 treatise on the subject, Marvin H. J. Gruber presents, compares, and...
Wiley, 2012. – 400 p. – ISBN: 0470621702, 9780470621707 A practical approach to estimating and tracking dynamic systems in real–worl applications Much of the literature on performing estimation for non–Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on...
New York: John Wiley & sons, 1984. — 339 p. A coherent, unified set of statistical methods, based on ranks, for analyzing data resulting from various experimental designs. Uses MINITAB, a statistical computing system for the implementation of the methods. Assesses the statistical and stability properties of the methods through asymptotic efficiency and influence curves and...
Springer, 1997. — 236 p. — (Springer Series in Statistics). — ISBN13: 978-0387982250; ISBN10: 0387982256. This book is concerned with the general theory of optimal estimation of parameters in systems subject to random effects and with the application of this theory. The focus is on choice of families of estimating functions, rather than the estimators derived therefrom, and on...
Springer, 1999. — 383 p. — ISBN: 978-1-85233-133-7. This book provides an introductory, yet comprehensive, treatment of both Wiener and Kalman filtering, along with a development of least-squares estimation, maximum likelihood estimation, and maximum a posteriori estimation based on discrete-time measurements. A good deal of emphasis is placed in the text on showing how these...
New York: Wiley-Interscience, 1997. — 378 p. Differential geometry provides an aesthetically appealing and often revealing view of statistical inference. Beginning with an elementary treatment of one-parameter statistical models and ending with an overview of recent developments, this is the first book to provide an introduction to the subject that is largely accessible to...
N.-Y.: Springer, 2012. - 73p. Explosive growth in the size of spatial databases has highlighted the need for spatial data mining techniques to mine the interesting but implicit spatial patterns within these large databases. This book explores computational structure of the exact and approximate spatial autoregression (SAR) model solutions. Estimation of the parameters of the...
New Delhi: NIPA, 2020. — 432 p. Preface Point Estimation Methods of Estimation Interval Estimation Test of Hypothesis More on Testing of Hypothesis Sequential Analysis Decision Theory Linear Estimation Distribution of Order Statistics Bibliography
2nd Edition. — Springer, 1998. — 590 p. — ISBN: 0387985026. A comprehensive account of point estimation in Euclidean sample space: Written by an acknowledged authority in the field, Theory of Point Estimation covers numerous applications to exponential and group families and offers a systematic discussion of the rich body of statistical problems relevant to these subjects. This...
World Scientific Publishing Co. Pte. Ltd., 2025. — 274 р. — ISBN 978-9811293344. This book provides an overview of recently developed models for trending time series and introduces new nonlinear, nonparametric, and semiparametric methods. Specifically, it offers practical approaches to address the problem of endogeneity in linear trending regression models where the trend...
New York: Springer, 2005. — 357 p. — ISBN: 9781852337605. This book develops methods for two key problems in the analysis of large-scale surveys: dealing with incomplete data and making inferences about sparsely represented subdomains. The presentation is committed to two particular methods, multiple imputation for missing data and multivariate composition for small-area...
Los Angeles: SAGE Publications, 2022. — 274 p. An experienced author in the field of data analytics and statistics, John Macinnes has produced a straight-forward text that breaks down the complex topic of inferential statistics with accessible language and detailed examples. It covers a range of topics, including: Probability and Sampling distributions Inference and regression...
N.-Y.: Springer, 1986. - 146p. The Basic Model and The Estimation Problem Basic Linear Technique Linearization of the Basic Model The Ordinary Least Squares Estimates The Seely-Zyskind Results The General Solution to Optimal Unbiased Estimation Background from Algebra The Structure of Semisimple Associative and Jordan Algebras The Algebraic Structure of Variance Components...
Springer, 2023. — 138 p. This book provides a self-contained introduction of Stein/shrinkage estimation for the mean vector of a multivariate normal distribution. The book begins with a brief discussion of basic notions and results from decision theory such as admissibility, minimaxity, and (generalized) Bayes estimation. It also presents Stein's unbiased risk estimator and the...
Springer, 2021. — 619 p. — ISBN 973030637569. This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new...
New York: R. E. Krieger Pub. Co, 1976. — 312 p. A Mahtematical Survey Matrix Algebra and Quadratic Forms Matrices Matrix Operations Eigenvalues and Eigenfunctions Matrix Rank and Linear Dependence Inner and Outer Vector Products Symmetric Matrices Matrix Diagonalization Quadratic Forms Matrix Functions Scalar Functions of a Square Matrix Gradient Vector The Cayley-Hamilton...
Toronto: University of Toronto, 1993. — 144 p. Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. Related problems in other fields...
Springer, 2024. — 153 p. This book focuses exclusively on the domain of parametric inference and that, too, from a reader’s perspective, i.e., covering only point estimation of parameter(s). It covers those topics in parametric inference which need clarity of exposure to students, researchers, and teachers alike; mere statements of theorems and proofs may not always reveal the...
2nd ed. — Singapore: World Scientific Publishing Company, 2023. — 258 p. — ISBN 9811272832. Nonparametric kernel estimators apply to the statistical analysis of independent or dependent sequences of random variables and for samples of continuous or discrete processes. The optimization of these procedures is based on the choice of a bandwidth that minimizes an estimation error...
World Scientific Publishing Company – 2011, 212 pages ISBN: 9814343730, 9789814343732 This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of...
Hackensack: World Scientific Publishing, 2019. — 304 p. The approximation and the estimation of nonparametric functions by projections on an orthonormal basis of functions are useful in data analysis. This book presents series estimators defined by projections on bases of functions, they extend the estimators of densities to mixture models, deconvolution and inverse problems,...
2nd Edition. — Wiley, 2015. — 451 p. — (Wiley Series in Survey Methodology). — ISBN-13 978-1-118-73578-7. O проблемах получения надежных оценок при опросах Direct Domain Estimation Indirect Domain Estimation Small Area Models Empirical Best Linear Unbiased Prediction (EBLUP): Theory Empirical Best Linear Unbiased Prediction (EBLUP): Basic Area Level Model Basic Unit Level Model...
New York: Dover Publications, 2003. — 962 p. Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Probability Model. Probability...
Springer – 2010, 226 pages ISBN: 1441959408, 9781441959416 This monograph contributes to the area of comparative statistical inference. Attention is restricted to the important subfield of statistical estimation. The book is intended for an audience having a solid grounding in probability and statistics at the level of the year-long undergraduate course taken by statistics and...
Springer, 2013. — 77 p. This book aims to provide an overview of some adaptive techniques used in estimating parameters for finite populations where the sampling at any stage depends on the sampling information obtained to date. The sample adapts to new information as it comes in. These methods are especially used for sparse and clustered populations. Written by two acknowledged...
New York: Chapman and Hall/CRC, 1975. — 190 p. Statistics is a subject with a vast field of application, involving problems which vary widely in their character and complexity.However, in tackling these, we use a relatively small core of central ideas and methods. This book attempts to concentrateattention on these ideas: they are placed in a general settingand illustrated by...
2nd Edition. — Cambridge University Press, 2019. — 788 p. — ISBN: 978-1-107-18514-2. Doubt over the trustworthiness of published empirical results is not unwarranted and is often a result of statistical mis-specification: invalid probabilistic assumptions imposed on data. Now in its second edition, this bestselling textbook offers a comprehensive course in empirical research...
Singapore: Springer, 2023. — 127 p. This book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular, it focuses on both introducing classical theory and reviewing the latest methods. First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic...
Springer, 2009. — 221 p. The tradition of considering the problem of statistical estimation as that of estimation of a finite number of parameters goes back to Fisher. However, parametric models provide only an approximation, often imprecise, of the underlying statistical structure. Statistical models that explain the data in a more consistent way are often more complex:...
CreateSpace Independent Publishing Platform, 2013. — 101 p. Using partial moments, the authors introduce a new toolbox of statistical tools. The advantage of using partial moments is that it is nonparametric and does not require the knowledge of the underlying probability function nor does it require a “goodness of fit” analysis. Partial moments provide us with cumulative...
N.-Y.: Springer, 2007. — 462 p. We live in the information age. Statistical surveys are used every day to determine or evaluate public policy and to make important business decisions. Correct methods for computing the precision of the survey data and for making inferences to the target population are absolutely essential to sound decision making. Now in its second edition,...
Пер. с англ. В.С. Дуженко, Е.С. Фоминой, под ред. и с предисловием В.Г. Горского. — М.: Статистика, 1979. — 349 с. — (Математико-статистические методы за рубежом). В книге освещается новое направление в развитии статистических методов. Это, по существу, первая систематическая монография по нелинейному оцениванию параметров. В ней рассматриваются метод наименьших квадратов,...
Киев : Изд-во при Киев. ун-те, 1982. — 192 с. Невычитанный OCR. Монография посвящена изложению математической теории статистических оценок параметров для широкого класса случайных процессов. Для ряда новых моделей процессов, имеющих важные приложения в теории оптимального управления, радиотехнике, экономике и т. п., указаны границы применимости статистических методов и изучены...
М.: Статистика, 1976. — 598 с. Материал изложен по схеме от простого к сложному. Вначале рассматриваются элементарные понятия теории вероятностей и описательной статистики. Этот материал занимает почти треть книги и представляет собой хороший вводный курс прикладной статистики для начинающих. Затем приводятся многочисленные примеры различных постановок статистических задач,...
М.: Наука, 1979. — 528 с. — (Теория вероятностей и математическая статистика).
До недавнего времени теория оценивания в больших выборках содержала лишь разрозненные факты о состоятельности и асимптотической нормальности некоторых конкретных оценок. Однако в результате ряда исследований последних лет ситуация изменилась — появились общие методы изучения свойств широкого класса...
М.: Наука, 1991. — 448 с. — ISBN: 5-02-013941-6. Имя крупного американского ученого Э. Лемана хорошо известно читателям по классическому руководству "Проверка статистических гипотез", выдержавшему два издания на русском языке. Новая книга посвящается изложению теории статистических оценок. Рассматриваются оптимальные статистические оценки, полученные при различного рода...
М.: Финансы и статистика, 1982. — 245 с.
Книга представляет собой строгое и последовательное изложение математической статистики применительно к выборочному наблюдению. В первой части рассматриваются принципы теории вероятностей, статистические распределения и их применение в выборочных исследованиях, статистическое оценивание и проверка гипотез. Во второй части рассматриваются...
Монография. — М.: Наука, Главная редакция физико-математической литературы, 1982. — 432 с.: ил. — (Теоретические основы технической кибернетики). В монографии излагается метод оценивания случайных процессов и полей, основанный на аппроксимации в ортогональном базисе. Такая аппроксимация приводит к простому общему решению задачи оценивания с интегральным оператором. Синтез тех...
М.: Физматлит, 2005. — 512 с. — ISBN: 5-9221-0660-0. В монографии изложен подход к установлению принципа больших уклонений вероятностных мер, основанный на аналогии с понятием слабой сходимости. Представлена теория идемпотентной вероятности и идемпотентных процессов. Получены общие результаты об асимптотике больших уклонений для семимартингалов. Рассматриваются приложения к...
Монография. — Курган: Курганский государственный университет (КГУ), 2019. — 241 с. — ISBN 978-5-4217-0496-6. В монографии рассматриваются адаптивные оценки и доверительные интервалы параметров в условиях априорной статистической неопределенности на актуальных для практики классах полупараметрических и полунепараметрических задач робастной статистики. С единых позиций...
Перевод с немецкого Я.Ш. Паппэ. Под редакцией И.Г. Венецкого и В.М. Ивановой. Москва, Статистика. 1978. — 213 с. Настоящая книга полезна всем, кто применяет статистические методы для заключения о свойствах совокупности с помощью выборки (т.е. исследуется только часть объектов совокупности). Книга предназначена не для математиков, интересующихся теорией выборочного метода, а для...
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