## An introduction to probability theory and mathematical statistics

**Author**: V. K. Rohatgi

**Publisher:**John Wiley & Sons Inc

**ISBN:**N.A

**Category:**Mathematics

**Page:**684

**View:**5750

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Probability; Random variables and their probability distributions; Moments and generating functions; Random vectors; Some special distributions; Limit theorems; Sample moments and their distributions; The theory of point estimation; Neyman-Pearson theory of testing of hypotheses; Some further results on hypotheses testing; Confidence estimation; The general linear hypothesis; Nonparametric statistical inference; Sequential statistical inference.

## An Introduction to Probability and Statistics

**Author**: Vijay K. Rohatgi,A.K. Md. Ehsanes Saleh

**Publisher:**John Wiley & Sons

**ISBN:**1118799682

**Category:**Mathematics

**Page:**728

**View:**2201

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A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics. An Introduction to Probability and Statistics, Third Edition includes: A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.

## An introduction to probability theory and its applications

**Author**: William Feller

**Publisher:**N.A

**ISBN:**N.A

**Category:**Mathematical statistics

**Page:**419

**View:**2696

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## Introduction to Probability and Mathematical Statistics

**Author**: Lee J. Bain,Max Engelhardt

**Publisher:**Duxbury Press

**ISBN:**9780534380205

**Category:**Mathematics

**Page:**644

**View:**6144

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The Second Edition of INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS focuses on developing the skills to build probability (stochastic) models. Lee J. Bain and Max Engelhardt focus on the mathematical development of the subject, with examples and exercises oriented toward applications.

## An Introduction to Probability and Mathematical Statistics

**Author**: Howard G. Tucker

**Publisher:**Academic Press

**ISBN:**1483225143

**Category:**Mathematics

**Page:**240

**View:**2316

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An Introduction to Probability and Mathematical Statistics provides information pertinent to the fundamental aspects of probability and mathematical statistics. This book covers a variety of topics, including random variables, probability distributions, discrete distributions, and point estimation. Organized into 13 chapters, this book begins with an overview of the definition of function. This text then examines the notion of conditional or relative probability. Other chapters consider Cochran's theorem, which is of extreme importance in that part of statistical inference known as analysis of variance. This book discusses as well the fundamental principles of testing statistical hypotheses by providing the reader with an idea of the basic problem and its relation to practice. The final chapter deals with the problem of estimation and the Neyman theory of confidence intervals. This book is a valuable resource for undergraduate university students who are majoring in mathematics. Students who are majoring in physics and who are inclined toward abstract mathematics will also find this book useful.

## An Introduction to Probability Theory and Its Applications

**Author**: William Feller

**Publisher:**John Wiley & Sons

**ISBN:**N.A

**Category:**Mathematics

**Page:**704

**View:**2965

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The exponential and the uniform densities; Special densities. Randomization; Densities in higher dimensions. Normal densities and processes; Probability measures and spaces; Probability distributions in Rr; A survey of some important distributions and processes; Laws of large numbers. Aplications in analysis; The basic limit theorems; Infinitely divisible distributions and semi-groups; Markov processes and semi-groups; Renewal theory; Random walks in R1; Laplace transforms. Tauberian theorems. Resolvents; Aplications of Laplace transforms; Characteristic functions; Expansions related to the central limit theorem; Infinitely divisible distributions; Applications of Fourier methods to ramdom walks; harmonic analysis; Answers to problems.

## Probability and Mathematical Statistics

*An Introduction*

**Author**: Eugene Lukacs

**Publisher:**Academic Press

**ISBN:**1483269205

**Category:**Mathematics

**Page:**254

**View:**1652

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Probability and Mathematical Statistics: An Introduction provides a well-balanced first introduction to probability theory and mathematical statistics. This book is organized into two sections encompassing nine chapters. The first part deals with the concept and elementary properties of probability space, and random variables and their probability distributions. This part also considers the principles of limit theorems, the distribution of random variables, and the so-called student’s distribution. The second part explores pertinent topics in mathematical statistics, including the concept of sampling, estimation, and hypotheses testing. This book is intended primarily for undergraduate statistics students.

## An Introduction to Mathematical Statistics and Its Applications

**Author**: Richard J. Larsen,Morris L. Marx

**Publisher:**Pearson College Division

**ISBN:**9780139223037

**Category:**Mathematics

**Page:**790

**View:**4218

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Using high-quality, real-world case studies and examples, this introduction to mathematical statistics shows how to use statistical methods and when to use them. This book can be used as a brief introduction to design of experiments. This successful, calculus-based book of probability and statistics, was one of the first to make real-world applications an integral part of motivating discussion. The number of problem sets has increased in all sections. Some sections include almost 50% new problems, while the most popular case studies remain. For anyone needing to develop proficiency with Mathematical Statistics.

## Introduction to Probability with Statistical Applications

**Author**: Géza Schay

**Publisher:**Birkhäuser

**ISBN:**3319306200

**Category:**Mathematics

**Page:**385

**View:**6767

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Now in its second edition, this textbook serves as an introduction to probability and statistics for non-mathematics majors who do not need the exhaustive detail and mathematical depth provided in more comprehensive treatments of the subject. The presentation covers the mathematical laws of random phenomena, including discrete and continuous random variables, expectation and variance, and common probability distributions such as the binomial, Poisson, and normal distributions. More classical examples such as Montmort's problem, the ballot problem, and Bertrand’s paradox are now included, along with applications such as the Maxwell-Boltzmann and Bose-Einstein distributions in physics. Key features in new edition: * 35 new exercises * Expanded section on the algebra of sets * Expanded chapters on probabilities to include more classical examples * New section on regression * Online instructors' manual containing solutions to all exercises“/p> Advanced undergraduate and graduate students in computer science, engineering, and other natural and social sciences with only a basic background in calculus will benefit from this introductory text balancing theory with applications. Review of the first edition: This textbook is a classical and well-written introduction to probability theory and statistics. ... the book is written ‘for an audience such as computer science students, whose mathematical background is not very strong and who do not need the detail and mathematical depth of similar books written for mathematics or statistics majors.’ ... Each new concept is clearly explained and is followed by many detailed examples. ... numerous examples of calculations are given and proofs are well-detailed." (Sophie Lemaire, Mathematical Reviews, Issue 2008 m)

## An introduction to mathematical statistics

**Author**: F. Bijma,M. Jonker,A. van der Vaart

**Publisher:**Amsterdam University Press

**ISBN:**9048536111

**Category:**Mathematics

**Page:**N.A

**View:**7854

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Statistics is the science that focuses on drawing conclusions from data, by modeling and analyzing the data using probabilistic models. In An Introduction to Mathematical Statistics the authors describe key concepts from statistics and give a mathematical basis for important statistical methods. Much attention is paid to the sound application of those methods to data. The three main topics in statistics are estimators, tests, and confidence regions. The authors illustrate these in many examples, with a separate chapter on regression models, including linear regression and analysis of variance. They also discuss the optimality of estimators and tests, as well as the selection of the best-fitting model. Each chapter ends with a case study in which the described statistical methods are applied. This book assumes a basic knowledge of probability theory, calculus, and linear algebra.

## An introduction to mathematical statistics

**Author**: H. D. Brunk

**Publisher:**Xerox College Pub

**ISBN:**N.A

**Category:**Mathematics

**Page:**457

**View:**5985

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## An Introduction to Probability and Statistical Inference

**Author**: George G. Roussas

**Publisher:**Academic Press

**ISBN:**0128004371

**Category:**Mathematics

**Page:**624

**View:**5639

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An Introduction to Probability and Statistical Inference, Second Edition, guides you through probability models and statistical methods and helps you to think critically about various concepts. Written by award-winning author George Roussas, this book introduces readers with no prior knowledge in probability or statistics to a thinking process to help them obtain the best solution to a posed question or situation. It provides a plethora of examples for each topic discussed, giving the reader more experience in applying statistical methods to different situations. This text contains an enhanced number of exercises and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities. Reorganized material is included in the statistical portion of the book to ensure continuity and enhance understanding. Each section includes relevant proofs where appropriate, followed by exercises with useful clues to their solutions. Furthermore, there are brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises are available to instructors in an Answers Manual. This text will appeal to advanced undergraduate and graduate students, as well as researchers and practitioners in engineering, business, social sciences or agriculture. Content, examples, an enhanced number of exercises, and graphical illustrations where appropriate to motivate the reader and demonstrate the applicability of probability and statistical inference in a great variety of human activities Reorganized material in the statistical portion of the book to ensure continuity and enhance understanding A relatively rigorous, yet accessible and always within the prescribed prerequisites, mathematical discussion of probability theory and statistical inference important to students in a broad variety of disciplines Relevant proofs where appropriate in each section, followed by exercises with useful clues to their solutions Brief answers to even-numbered exercises at the back of the book and detailed solutions to all exercises available to instructors in an Answers Manual

## An Introduction to Measure-Theoretic Probability

**Author**: George G. Roussas

**Publisher:**Academic Press

**ISBN:**0128002905

**Category:**Mathematics

**Page:**426

**View:**5292

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An Introduction to Measure-Theoretic Probability, Second Edition, employs a classical approach to teaching the basics of measure theoretic probability. This book provides in a concise, yet detailed way, the bulk of the probabilistic tools that a student working toward an advanced degree in statistics, probability and other related areas should be equipped with. This edition requires no prior knowledge of measure theory, covers all its topics in great detail, and includes one chapter on the basics of ergodic theory and one chapter on two cases of statistical estimation. Topics range from the basic properties of a measure to modes of convergence of a sequence of random variables and their relationships; the integral of a random variable and its basic properties; standard convergence theorems; standard moment and probability inequalities; the Hahn-Jordan Decomposition Theorem; the Lebesgue Decomposition T; conditional expectation and conditional probability; theory of characteristic functions; sequences of independent random variables; and ergodic theory. There is a considerable bend toward the way probability is actually used in statistical research, finance, and other academic and nonacademic applied pursuits. Extensive exercises and practical examples are included, and all proofs are presented in full detail. Complete and detailed solutions to all exercises are available to the instructors on the book companion site. This text will be a valuable resource for graduate students primarily in statistics, mathematics, electrical and computer engineering or other information sciences, as well as for those in mathematical economics/finance in the departments of economics. Provides in a concise, yet detailed way, the bulk of probabilistic tools essential to a student working toward an advanced degree in statistics, probability, and other related fields Includes extensive exercises and practical examples to make complex ideas of advanced probability accessible to graduate students in statistics, probability, and related fields All proofs presented in full detail and complete and detailed solutions to all exercises are available to the instructors on book companion site Considerable bend toward the way probability is used in statistics in non-mathematical settings in academic, research and corporate/finance pursuits.

## Mathematical Statistics with Applications in R

**Author**: Kandethody M. Ramachandran,Chris P. Tsokos

**Publisher:**Elsevier

**ISBN:**012417132X

**Category:**Mathematics

**Page:**826

**View:**6537

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Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner. This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students. Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods

## Mathematical Statistics

*An Introduction to Likelihood Based Inference*

**Author**: Richard J. Rossi

**Publisher:**John Wiley & Sons

**ISBN:**1118771168

**Category:**Mathematics

**Page:**448

**View:**2778

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Presents a unified approach to parametric estimation, confidence intervals, hypothesis testing, and statistical modeling, which are uniquely based on the likelihood function This book addresses mathematical statistics for upper-undergraduates and first year graduate students, tying chapters on estimation, confidence intervals, hypothesis testing, and statistical models together to present a unifying focus on the likelihood function. It also emphasizes the important ideas in statistical modeling, such as sufficiency, exponential family distributions, and large sample properties. Mathematical Statistics: An Introduction to Likelihood Based Inference makes advanced topics accessible and understandable and covers many topics in more depth than typical mathematical statistics textbooks. It includes numerous examples, case studies, a large number of exercises ranging from drill and skill to extremely difficult problems, and many of the important theorems of mathematical statistics along with their proofs. In addition to the connected chapters mentioned above, Mathematical Statistics covers likelihood-based estimation, with emphasis on multidimensional parameter spaces and range dependent support. It also includes a chapter on confidence intervals, which contains examples of exact confidence intervals along with the standard large sample confidence intervals based on the MLE's and bootstrap confidence intervals. There’s also a chapter on parametric statistical models featuring sections on non-iid observations, linear regression, logistic regression, Poisson regression, and linear models. Prepares students with the tools needed to be successful in their future work in statistics data science Includes practical case studies including real-life data collected from Yellowstone National Park, the Donner party, and the Titanic voyage Emphasizes the important ideas to statistical modeling, such as sufficiency, exponential family distributions, and large sample properties Includes sections on Bayesian estimation and credible intervals Features examples, problems, and solutions Mathematical Statistics: An Introduction to Likelihood Based Inference is an ideal textbook for upper-undergraduate and graduate courses in probability, mathematical statistics, and/or statistical inference.

## Elements of Probability and Statistics

*An Introduction to Probability with de Finetti’s Approach and to Bayesian Statistics*

**Author**: Francesca Biagini,Massimo Campanino

**Publisher:**Springer

**ISBN:**3319072544

**Category:**Mathematics

**Page:**246

**View:**5911

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This book provides an introduction to elementary probability and to Bayesian statistics using de Finetti's subjectivist approach. One of the features of this approach is that it does not require the introduction of sample space – a non-intrinsic concept that makes the treatment of elementary probability unnecessarily complicate – but introduces as fundamental the concept of random numbers directly related to their interpretation in applications. Events become a particular case of random numbers and probability a particular case of expectation when it is applied to events. The subjective evaluation of expectation and of conditional expectation is based on an economic choice of an acceptable bet or penalty. The properties of expectation and conditional expectation are derived by applying a coherence criterion that the evaluation has to follow. The book is suitable for all introductory courses in probability and statistics for students in Mathematics, Informatics, Engineering, and Physics.

## Wahrscheinlichkeitsrechnung und mathematische Statistik

**Author**: Marek Fisz

**Publisher:**N.A

**ISBN:**N.A

**Category:**Mathematical statistics

**Page:**777

**View:**7270

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## Einführung in Die Mathematische Statistik

**Author**: Leopold Schmetterer

**Publisher:**Springer-Verlag

**ISBN:**3662259338

**Category:**Mathematics

**Page:**597

**View:**6210

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Die Frage nach dem Aufgabenkreis der Statistik im allgemeinen kann nicht mit wenigen Worten umrissen werden. Wenn man etwas näher auf die geschichtliche Entwicklung des Begriffes Statistik eingeht\ so findet man, daß lange Zeit darunter nur die Beschrei bung von "Staatsmerkwürdigkeiten" (wie Bevölkerungszahl, Bo denbeschaffenheit, Sammlung wirtschaftlicher Daten) verstanden wurde. Erst in neuerer Zeit drang die statistische Betrachtungsweise auch in die Naturwissenschaften ein (BOLTZMANN, GIBBS, MAx WELL). Fußend auf dem Boden der seit Beginn dieses Jahrhunderts sich rasch entwickelnden Wahrscheinlichkeitstheorie hat dann ins besondere in den letzten dreißig Jahren auch die mathematische Statistik einen unerhörten Aufschwung genommen und die Metho den der statistischen Analyse mit einer kaum zu übersehenden Fülle von Gedanken bereichert. Statistische Überlegungen treten heute in den verschiedensten Wissensgebieten auf. Es genügt, wenn wir neben den Wirtschaftswissenschaften als Beispiele die Astronomie, die Biologie, die Medizin, die Psychologie, die Physik und die Soziologie anführen. Wenn es also, wie gesagt, nicht leicht ist, den allgemeinen Be griff der Statistik kurz zu charakterisieren, so geht man doch wohl nicht fehl, wenn man feststellt, daß sich die Statistik mit dem Studium von Erscheinungen befaßt, die entweder eine große Zahl von Individuen betreffen, oder sonst in irgendeiner Weise eine Viel falt von Einzelerscheinungen zusammenfassen. Man kann somit als ein Charakteristikum der Statistik das Studium der Massen erscheinungen betrachten. Es ist eine Erfahrungstatsache, daß bei Massenerscheinungen Gesetzmäßigkeiten nachgewiesen werden können, die bei Einzelerscheinungen kein Gegenstück haben. Das 1 Vgl. W. WrNKLER, Grundriß der Statistik I, 2.

## Probability Theory, Random Processes and Mathematical Statistics

**Author**: Y. Rozanov

**Publisher:**Springer Science & Business Media

**ISBN:**9401104492

**Category:**Mathematics

**Page:**259

**View:**6860

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Probability Theory, Theory of Random Processes and Mathematical Statistics are important areas of modern mathematics and its applications. They develop rigorous models for a proper treatment for various 'random' phenomena which we encounter in the real world. They provide us with numerous tools for an analysis, prediction and, ultimately, control of random phenomena. Statistics itself helps with choice of a proper mathematical model (e.g., by estimation of unknown parameters) on the basis of statistical data collected by observations. This volume is intended to be a concise textbook for a graduate level course, with carefully selected topics representing the most important areas of modern Probability, Random Processes and Statistics. The first part (Ch. 1-3) can serve as a self-contained, elementary introduction to Probability, Random Processes and Statistics. It contains a number of relatively sim ple and typical examples of random phenomena which allow a natural introduction of general structures and methods. Only knowledge of elements of real/complex analysis, linear algebra and ordinary differential equations is required here. The second part (Ch. 4-6) provides a foundation of Stochastic Analysis, gives information on basic models of random processes and tools to study them. Here a familiarity with elements of functional analysis is necessary. Our intention to make this course fast-moving made it necessary to present important material in a form of examples.