## A First Course on Parametric Inference

**Author**: Balvant Keshav Kale

**Publisher:**Alpha Science Int'l Ltd.

**ISBN:**9781842652190

**Category:**Business & Economics

**Page:**295

**View:**4790

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Starting with the basic concept of sufficient statistics, this text uses the classical approach based on minimum variance to provide an understanding of unbiased estimation.

## A First Course in Statistics for Signal Analysis

**Author**: Wojbor A. Woyczynski

**Publisher:**Springer Science & Business Media

**ISBN:**9780817681012

**Category:**Mathematics

**Page:**261

**View:**7851

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This self-contained and user-friendly textbook is designed for a first, one-semester course in statistical signal analysis for a broad audience of students in engineering and the physical sciences. The emphasis throughout is on fundamental concepts and relationships in the statistical theory of stationary random signals, which are explained in a concise, yet rigorous presentation. With abundant practice exercises and thorough explanations, A First Course in Statistics for Signal Analysis is an excellent tool for both teaching students and training laboratory scientists and engineers. Improvements in the second edition include considerably expanded sections, enhanced precision, and more illustrative figures.

## STATISTICAL INFERENCE : THEORY OF ESTIMATION

**Author**: MANOJ KUMAR SRIVASTAVA,ABDUL HAMID KHAN,NAMITA SRIVASTAVA

**Publisher:**PHI Learning Pvt. Ltd.

**ISBN:**812034930X

**Category:**Mathematics

**Page:**808

**View:**1338

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This book is sequel to a book Statistical Inference: Testing of Hypotheses (published by PHI Learning). Intended for the postgraduate students of statistics, it introduces the problem of estimation in the light of foundations laid down by Sir R.A. Fisher (1922) and follows both classical and Bayesian approaches to solve these problems. The book starts with discussing the growing levels of data summarization to reach maximal summarization and connects it with sufficient and minimal sufficient statistics. The book gives a complete account of theorems and results on uniformly minimum variance unbiased estimators (UMVUE)—including famous Rao and Blackwell theorem to suggest an improved estimator based on a sufficient statistic and Lehmann-Scheffe theorem to give an UMVUE. It discusses Cramer-Rao and Bhattacharyya variance lower bounds for regular models, by introducing Fishers information and Chapman, Robbins and Kiefer variance lower bounds for Pitman models. Besides, the book introduces different methods of estimation including famous method of maximum likelihood and discusses large sample properties such as consistency, consistent asymptotic normality (CAN) and best asymptotic normality (BAN) of different estimators. Separate chapters are devoted for finding Pitman estimator, among equivariant estimators, for location and scale models, by exploiting symmetry structure, present in the model, and Bayes, Empirical Bayes, Hierarchical Bayes estimators in different statistical models. Systematic exposition of the theory and results in different statistical situations and models, is one of the several attractions of the presentation. Each chapter is concluded with several solved examples, in a number of statistical models, augmented with exposition of theorems and results. KEY FEATURES • Provides clarifications for a number of steps in the proof of theorems and related results., • Includes numerous solved examples to improve analytical insight on the subject by illustrating the application of theorems and results. • Incorporates Chapter-end exercises to review student’s comprehension of the subject. • Discusses detailed theory on data summarization, unbiased estimation with large sample properties, Bayes and Minimax estimation, separately, in different chapters.

## A First Course in Linear Model Theory

**Author**: Nalini Ravishanker,Dipak K. Dey

**Publisher:**CRC Press

**ISBN:**9781584882473

**Category:**Mathematics

**Page:**496

**View:**2015

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This innovative, intermediate-level statistics text fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. With an innovative approach, the author's introduces students to the mathematical and statistical concepts and tools that form a foundation for studying the theory and applications of both univariate and multivariate linear models A First Course in Linear Model Theory systematically presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective. Through the concepts and tools of matrix and linear algebra and distribution theory, it provides a framework for understanding classical and contemporary linear model theory. It does not merely introduce formulas, but develops in students the art of statistical thinking and inspires learning at an intuitive level by emphasizing conceptual understanding. The authors' fresh approach, methodical presentation, wealth of examples, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models.

## Examples in Parametric Inference with R

**Author**: Ulhas Jayram Dixit

**Publisher:**Springer

**ISBN:**9811008892

**Category:**Mathematics

**Page:**423

**View:**2185

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This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory course in probability, will greatly benefit from this book. Students are expected to know matrix algebra, calculus, probability and distribution theory before beginning this course. Presenting a wealth of relevant solved and unsolved problems, the book offers an excellent tool for teachers and instructors who can assign homework problems from the exercises, and students will find the solved examples hugely beneficial in solving the exercise problems.

## A First Course in Order Statistics

**Author**: Barry C. Arnold,N. Balakrishnan,H. N. Nagaraja

**Publisher:**SIAM

**ISBN:**0898716489

**Category:**Mathematics

**Page:**279

**View:**3506

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This updated classic text will aid readers in understanding much of the current literature on order statistics: a flourishing field of study that is essential for any practising statistician and a vital part of the training for students in statistics. Written in a simple style that requires no advanced mathematical or statistical background, the book introduces the general theory of order statistics and their applications. The book covers topics such as distribution theory for order statistics from continuous and discrete populations, moment relations, bounds and approximations, order statistics in statistical inference and characterisation results, and basic asymptotic theory. There is also a short introduction to record values and related statistics. The authors have updated the text with suggestions for further reading that may be used for self-study. Written for advanced undergraduate and graduate students in statistics and mathematics, practising statisticians, engineers, climatologists, economists, and biologists.

## A First Course in Multivariate Statistics

**Author**: Bernard Flury

**Publisher:**Springer Science & Business Media

**ISBN:**1475727658

**Category:**Mathematics

**Page:**715

**View:**5818

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A comprehensive and self-contained introduction to the field, carefully balancing mathematical theory and practical applications. It starts at an elementary level, developing concepts of multivariate distributions from first principles. After a chapter on the multivariate normal distribution reviewing the classical parametric theory, methods of estimation are explored using the plug-in principles as well as maximum likelihood. Two chapters on discrimination and classification, including logistic regression, form the core of the book, followed by methods of testing hypotheses developed from heuristic principles, likelihood ratio tests and permutation tests. Finally, the powerful self-consistency principle is used to introduce principal components as a method of approximation, rounded off by a chapter on finite mixture analysis.

## Experimental Design and Statistics for Psychology

*A First Course*

**Author**: Fabio Sani,John Todman

**Publisher:**John Wiley & Sons

**ISBN:**1405150386

**Category:**Psychology

**Page:**240

**View:**503

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Experimental Design and Statistics for Psychology: A First Course is a concise, straighforward and accessible introduction to the design of psychology experiments and the statistical tests used to make sense of their results. Makes abundant use of charts, diagrams and figures. Assumes no prior knowledge of statistics. Invaluable to all psychology students needing a firm grasp of the basics, but tackling of some of the topic’s more complex, controversial issues will also fire the imagination of more ambitious students. Covers different aspects of experimental design, including dependent versus independent variables, levels of treatment, experimental control, random versus systematic errors, and within versus between subjects design. Provides detailed instructions on how to perform statistical tests with SPSS. Downloadable instructor resources to supplement and support your lectures can be found at www.blackwellpublishing.com/sani and include sample chapters, test questions, SPSS data sets, and figures and tables from the book.

## An Introduction to Stochastic Process

**Author**: Adhir K. Basu

**Publisher:**CRC Press

**ISBN:**9780849309915

**Category:**Mathematics

**Page:**224

**View:**3977

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Designed for college mathematics students at all levels, this book grew from the author's lectures for advanced undergraduate courses at Canadian and United States universities, and from a postgraduate course at Calcutta University. It introduces discrete time Markov chain and second order stochastic analysis, and includes discussions of renewal theory, time series analysis, queuing theory, Brownian motions, and martingale theorems.

## A Course in Stochastic Processes

*Stochastic Models and Statistical Inference*

**Author**: Denis Bosq,Hung T. Nguyen

**Publisher:**Springer

**ISBN:**9789401587709

**Category:**Mathematics

**Page:**354

**View:**7146

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This text is an Elementary Introduction to Stochastic Processes in discrete and continuous time with an initiation of the statistical inference. The material is standard and classical for a first course in Stochastic Processes at the senior/graduate level (lessons 1-12). To provide students with a view of statistics of stochastic processes, three lessons (13-15) were added. These lessons can be either optional or serve as an introduction to statistical inference with dependent observations. Several points of this text need to be elaborated, (1) The pedagogy is somewhat obvious. Since this text is designed for a one semester course, each lesson can be covered in one week or so. Having in mind a mixed audience of students from different departments (Math ematics, Statistics, Economics, Engineering, etc.) we have presented the material in each lesson in the most simple way, with emphasis on moti vation of concepts, aspects of applications and computational procedures. Basically, we try to explain to beginners questions such as "What is the topic in this lesson?" "Why this topic?", "How to study this topic math ematically?". The exercises at the end of each lesson will deepen the stu dents' understanding of the material, and test their ability to carry out basic computations. Exercises with an asterisk are optional (difficult) and might not be suitable for homework, but should provide food for thought.

## Journal of the American Statistical Association

**Author**: N.A

**Publisher:**N.A

**ISBN:**N.A

**Category:**Statistics

**Page:**N.A

**View:**5049

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## A first course in mathematical statistics

**Author**: George G. Roussas

**Publisher:**N.A

**ISBN:**N.A

**Category:**Mathematics

**Page:**506

**View:**4861

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Includes tables, answers to selected exercises, index.

## The British National Bibliography

**Author**: Arthur James Wells

**Publisher:**N.A

**ISBN:**N.A

**Category:**English literature

**Page:**N.A

**View:**7425

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## Sequential Analysis

**Author**: Abraham Wald

**Publisher:**Courier Corporation

**ISBN:**9780486439129

**Category:**Mathematics

**Page:**212

**View:**4485

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The inventor of a statistical inference system book describes his system and its applications. Discusses the general theory of the sequential probability ratio test, with comparisons to traditional statistical inference systems; applications that illustrate the general theory and of theoretical interest specific to these applications; possible approaches to the problem of sequential multi-valued decisions and estimation.

## Distributionen Und Hilbertraumoperatoren

*Mathematische Methoden Der Physik*

**Author**: Philippe Blanchard,Erwin Brüning

**Publisher:**Springer

**ISBN:**9783211825075

**Category:**Science

**Page:**375

**View:**3997

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Das Buch bietet eine Einführung in die zum Studium der Theoretischen Physik notwendigen mathematischen Grundlagen. Der erste Teil des Buches beschäftigt sich mit der Theorie der Distributionen und vermittelt daneben einige Grundbegriffe der linearen Funktionalanalysis. Der zweite Teil baut darauf auf und gibt eine auf das Wesentliche beschränkte Einführung in die Theorie der linearen Operatoren in Hilbert-Räumen. Beide Teile werden von je einer Übersicht begleitet, die die zentralen Ideen und Begriffe knapp erläutert und den Inhalt kurz beschreibt. In den Anhängen werden einige grundlegende Konstruktionen und Konzepte der Funktionalanalysis dargestellt und wichtige Konsequenzen entwickelt.

## Mathematical Reviews

**Author**: N.A

**Publisher:**N.A

**ISBN:**N.A

**Category:**Mathematics

**Page:**N.A

**View:**7951

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## Elementare Wahrscheinlichkeitstheorie und stochastische Prozesse

**Author**: Kai L. Chung

**Publisher:**Springer-Verlag

**ISBN:**3642670334

**Category:**Mathematics

**Page:**346

**View:**989

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Aus den Besprechungen: "Unter den zahlreichen Einführungen in die Wahrscheinlichkeitsrechnung bildet dieses Buch eine erfreuliche Ausnahme. Der Stil einer lebendigen Vorlesung ist über Niederschrift und Übersetzung hinweg erhalten geblieben. In jedes Kapitel wird sehr anschaulich eingeführt. Sinn und Nützlichkeit der mathematischen Formulierungen werden den Lesern nahegebracht. Die wichtigsten Zusammenhänge sind als mathematische Sätze klar formuliert." #FREQUENZ#1

## All of Statistics

*A Concise Course in Statistical Inference*

**Author**: Larry Wasserman

**Publisher:**Springer Science & Business Media

**ISBN:**0387217363

**Category:**Mathematics

**Page:**442

**View:**7928

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Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.