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
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...
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,...
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...
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...
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...
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, 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...
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...
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...
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...
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...
Hoboken: Wiley, 2023. — 387 p. M-statistics. A comprehensive resource providing new statistical methodologies and demonstrating how new approaches work for applications M-statistics introduces a new approach to statistical inference, redesigning the fundamentals of statistics, and improving on the classical methods we already use. This book targets exact optimal statistical...
Springer, 2023. — 210 p. What does this p-value actually mean ? And what is a significant result ? This book provides a compact and comprehension-oriented introduction to inferential statistics and answers questions like these. One focus is on the logic underlying inferential statistics and hypothesis testing: Readers learn the most commonly used procedures (t-test, analysis of...
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...
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