## A First Course on Parametric Inference

**Author**: Balvant Keshav Kale

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

**ISBN:**9781842652190

**Category:**Business & Economics

**Page:**295

**View:**1895

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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 on Parametric Inference

**Author**: B. K. Kale

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

**ISBN:**9788173191961

**Category:**Mathematics

**Page:**268

**View:**4606

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Starting with the basic concept of sufficient statistics, the approach based on minimum variance unbiased estimation is presented, in detail, in this text.

## All of Statistics

*A Concise Course in Statistical Inference*

**Author**: Larry Wasserman

**Publisher:**Springer Science & Business Media

**ISBN:**0387217363

**Category:**Mathematics

**Page:**442

**View:**4247

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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.

## A First Course in Statistics for Signal Analysis

**Author**: Wojbor A. Woyczynski

**Publisher:**Springer Science & Business Media

**ISBN:**9780817681012

**Category:**Mathematics

**Page:**261

**View:**5040

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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.

## Fundamentals of Probability: A First Course

**Author**: Anirban DasGupta

**Publisher:**Springer Science & Business Media

**ISBN:**1441957804

**Category:**Mathematics

**Page:**450

**View:**7096

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Probability theory is one branch of mathematics that is simultaneously deep and immediately applicable in diverse areas of human endeavor. It is as fundamental as calculus. Calculus explains the external world, and probability theory helps predict a lot of it. In addition, problems in probability theory have an innate appeal, and the answers are often structured and strikingly beautiful. A solid background in probability theory and probability models will become increasingly more useful in the twenty-?rst century, as dif?cult new problems emerge, that will require more sophisticated models and analysis. Thisisa text onthe fundamentalsof thetheoryofprobabilityat anundergraduate or ?rst-year graduate level for students in science, engineering,and economics. The only mathematical background required is knowledge of univariate and multiva- ate calculus and basic linear algebra. The book covers all of the standard topics in basic probability, such as combinatorial probability, discrete and continuous distributions, moment generating functions, fundamental probability inequalities, the central limit theorem, and joint and conditional distributions of discrete and continuous random variables. But it also has some unique features and a forwa- looking feel.

## Chance Encounters

*A First Course in Data Analysis and Inference*

**Author**: C. J. Wild,George A. F. Seber

**Publisher:**Wiley

**ISBN:**9780471329367

**Category:**Mathematics

**Page:**632

**View:**8671

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A text for the non-majors introductory statistics service course. The chapters--including Web site material--can be organized for one or two semester sequences; algrebra is the mathematics prerequisite. Web site chapters on quality control, time series, plus business applications regularly throughout the work make it suitable for business statistics courses on some campuses. The text combines lucid and statistically engaging exposition, graphic and poignantly applied examples, realistic exercise settings to take student past the mechanics of introductory-level statistical techniques into the realm of practical data analysis and inference-based problem solving.

## Modes of Parametric Statistical Inference

**Author**: Seymour Geisser,Wesley O. Johnson

**Publisher:**John Wiley & Sons

**ISBN:**0471743127

**Category:**Mathematics

**Page:**192

**View:**6465

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A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode's strengths and weaknesses. The book begins with fascinating highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples and counterexamples of situations and datasets where the modes yield both similar and dissimilar results, including a violation of the likelihood principle in which Bayesian and likelihood methods differ from frequentist methods. Each example is followed by a detailed discussion of why the results may have varied from one mode to another, helping the reader to gain a greater understanding of each mode and how it works. Moreover, the author provides considerable mathematical detail on certain points to highlight key aspects of theoretical development. The author's writing style and use of examples make the text clear and engaging. This book is fundamental reading for graduate-level students in statistics as well as anyone with an interest in the foundations of statistics and the principles underlying statistical inference, including students in mathematics and the philosophy of science. Readers with a background in theoretical statistics will find the text both accessible and absorbing.

## Introduction to Empirical Processes and Semiparametric Inference

**Author**: Michael R. Kosorok

**Publisher:**Springer Science & Business Media

**ISBN:**9780387749785

**Category:**Mathematics

**Page:**483

**View:**6834

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Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.

## A First Course in Order Statistics

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

**Publisher:**SIAM

**ISBN:**0898716489

**Category:**Mathematics

**Page:**279

**View:**6660

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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 History of Parametric Statistical Inference from Bernoulli to Fisher, 1713-1935

**Author**: Anders Hald

**Publisher:**Springer Science & Business Media

**ISBN:**0387464093

**Category:**Mathematics

**Page:**225

**View:**464

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This book offers a detailed history of parametric statistical inference. Covering the period between James Bernoulli and R.A. Fisher, it examines: binomial statistical inference; statistical inference by inverse probability; the central limit theorem and linear minimum variance estimation by Laplace and Gauss; error theory, skew distributions, correlation, sampling distributions; and the Fisherian Revolution. Lively biographical sketches of many of the main characters are featured throughout, including Laplace, Gauss, Edgeworth, Fisher, and Karl Pearson. Also examined are the roles played by DeMoivre, James Bernoulli, and Lagrange.

## Nonparametric Statistical Inference, Fifth Edition

**Author**: Jean Dickinson Gibbons,Subhabrata Chakraborti

**Publisher:**Chapman and Hall/CRC

**ISBN:**9781420077612

**Category:**Mathematics

**Page:**650

**View:**9492

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Proven Material for a Course on the Introduction to the Theory and/or on the Applications of Classical Nonparametric Methods Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material. New to the Fifth Edition Updated and revised contents based on recent journal articles in the literature A new section in the chapter on goodness-of-fit tests A new chapter that offers practical guidance on how to choose among the various nonparametric procedures covered Additional problems and examples Improved computer figures This classic, best-selling statistics book continues to cover the most commonly used nonparametric procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXact and compares these examples with corresponding hand calculations. The appendix includes a collection of tables required for solving the data-oriented problems. Nonparametric Statistical Inference, Fifth Edition provides in-depth yet accessible coverage of the theory and methods of nonparametric statistical inference procedures. It takes a practical approach that draws on scores of examples and problems and minimizes the theorem-proof format. Jean Dickinson Gibbons was recently interviewed regarding her generous pledge to Virginia Tech.

## Examples in Parametric Inference with R

**Author**: Ulhas Jayram Dixit

**Publisher:**Springer

**ISBN:**9811008892

**Category:**Mathematics

**Page:**423

**View:**7171

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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 Bayesian Statistical Methods

**Author**: Peter D. Hoff

**Publisher:**Springer Science & Business Media

**ISBN:**9780387924076

**Category:**Mathematics

**Page:**272

**View:**4840

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A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.

## A First Course in Multivariate Statistics

**Author**: Bernard Flury

**Publisher:**Springer Science & Business Media

**ISBN:**1475727658

**Category:**Mathematics

**Page:**715

**View:**6251

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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.

## Statistical Theory and Inference

**Author**: David Olive

**Publisher:**Springer

**ISBN:**3319049720

**Category:**Mathematics

**Page:**434

**View:**1066

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This text is for a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square error, uniform minimum variance estimators and the Cramer-Rao lower bound, an introduction to large sample theory, likelihood ratio tests and uniformly most powerful tests and the Neyman Pearson Lemma. A major goal of this text is to make these topics much more accessible to students by using the theory of exponential families. Exponential families, indicator functions and the support of the distribution are used throughout the text to simplify the theory. More than 50 ``brand name" distributions are used to illustrate the theory with many examples of exponential families, maximum likelihood estimators and uniformly minimum variance unbiased estimators. There are many homework problems with over 30 pages of solutions.

## A Course in Statistics with R

**Author**: Prabhanjan N. Tattar,Suresh Ramaiah,B. G. Manjunath

**Publisher:**John Wiley & Sons

**ISBN:**1119152755

**Category:**Computers

**Page:**696

**View:**7483

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Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets

## All of Nonparametric Statistics

**Author**: Larry Wasserman

**Publisher:**Springer Science & Business Media

**ISBN:**9780387306230

**Category:**Mathematics

**Page:**270

**View:**9212

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This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.

## Life Testing and Reliability Estimation

**Author**: S. Snehesh Kumar Sinha,Kale, B. K. (Balvant Keshav)

**Publisher:**New York : Wiley

**ISBN:**N.A

**Category:**Failure time data analysis

**Page:**196

**View:**9616

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## Computer Age Statistical Inference

*Algorithms, Evidence, and Data Science*

**Author**: Bradley Efron,Trevor Hastie

**Publisher:**Cambridge University Press

**ISBN:**1108107958

**Category:**Mathematics

**Page:**N.A

**View:**2323

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The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.