## An Introduction to Mathematical Statistics and Its Applications

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

**Publisher:**Pearson

**ISBN:**0134114248

**Category:**Mathematics

**Page:**768

**View:**705

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This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For courses in Mathematical Statistics Introducing the principles of statistics and data modeling Introduction to Mathematical Statistics and Its Applications , 6th Edition is a high-level calculus student’s first exposure to mathematical statistics. This book provides students who have already taken three or more semesters of calculus with the background to apply statistical principles. Meaty enough to guide a two-semester course, the book touches on both statistics and experimental design, which teaches students various ways to analyze data. It gives computational-minded students a necessary and realistic exposure to identifying data models.

## An Introduction to Mathematical Statistics and Its Applications

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

**Publisher:**Pearson College Division

**ISBN:**9780321693945

**Category:**Mathematics

**Page:**757

**View:**8563

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Noted for its integration of real-world data and case studies, this text offers sound coverage of the theoretical aspects of mathematical statistics. The authors demonstrate how and when to use statistical methods, while reinforcing the calculus that students have mastered in previous courses. Throughout the Fifth Edition, the authors have added and updated examples and case studies, while also refining existing features that show a clear path from theory to practice.

## An Introduction to Mathematical Statistics and Its Applications

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

**Publisher:**Pearson Educacion

**ISBN:**9780321766564

**Category:**Mathematical statistics

**Page:**757

**View:**1438

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Offering comprehensive coverage of the theoretical aspects of mathematical statistics, this text demonstrates how and when to use statistical methods, while reinforcing the calculus that students will have mastered in previous courses.

## An Introduction to Mathematical Statistics and Its Applications

*Student solutions manual*

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

**Publisher:**N.A

**ISBN:**9780130310156

**Category:**Mathematical statistics

**Page:**111

**View:**7613

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## An Introduction to Statistical Learning

*with Applications in R*

**Author**: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani

**Publisher:**Springer Science & Business Media

**ISBN:**1461471389

**Category:**Mathematics

**Page:**426

**View:**3847

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

## An Introduction to Kolmogorov Complexity and Its Applications

**Author**: Ming Li,Paul Vitanyi

**Publisher:**Springer Science & Business Media

**ISBN:**9780387948683

**Category:**Mathematics

**Page:**637

**View:**821

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Briefly, we review the basic elements of computability theory and prob ability theory that are required. Finally, in order to place the subject in the appropriate historical and conceptual context we trace the main roots of Kolmogorov complexity. This way the stage is set for Chapters 2 and 3, where we introduce the notion of optimal effective descriptions of objects. The length of such a description (or the number of bits of information in it) is its Kolmogorov complexity. We treat all aspects of the elementary mathematical theory of Kolmogorov complexity. This body of knowledge may be called algo rithmic complexity theory. The theory of Martin-Lof tests for random ness of finite objects and infinite sequences is inextricably intertwined with the theory of Kolmogorov complexity and is completely treated. We also investigate the statistical properties of finite strings with high Kolmogorov complexity. Both of these topics are eminently useful in the applications part of the book. We also investigate the recursion theoretic properties of Kolmogorov complexity (relations with Godel's incompleteness result), and the Kolmogorov complexity version of infor mation theory, which we may call "algorithmic information theory" or "absolute information theory. " The treatment of algorithmic probability theory in Chapter 4 presup poses Sections 1. 6, 1. 11. 2, and Chapter 3 (at least Sections 3. 1 through 3. 4).

## An Introduction to Probability and Statistics

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

**Publisher:**John Wiley & Sons

**ISBN:**1118799658

**Category:**Mathematics

**Page:**728

**View:**2692

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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:**John Wiley & Sons

**ISBN:**9780471257080

**Category:**Mathematics

**Page:**528

**View:**3043

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Major changes in this edition include the substitution of probabilistic arguments for combinatorial artifices, and the addition of new sections on branching processes, Markov chains, and the De Moivre-Laplace theorem.

## Introduction to Mathematical Statistics and Its Applications: Pearson New International Edition

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

**Publisher:**Pearson Higher Ed

**ISBN:**1292036729

**Category:**Mathematics

**Page:**744

**View:**5229

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Noted for its integration of real-world data and case studies, this text offers sound coverage of the theoretical aspects of mathematical statistics. The authors demonstrate how and when to use statistical methods, while reinforcing the calculus that students have mastered in previous courses. Throughout theFifth Edition, the authors have added and updated examples and case studies, while also refining existing features that show a clear path from theory to practice.

## Introduction to Stochastic Integration

**Author**: K.L. Chung,R.J. Williams

**Publisher:**Springer Science & Business Media

**ISBN:**1461495873

**Category:**Mathematics

**Page:**276

**View:**1551

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A highly readable introduction to stochastic integration and stochastic differential equations, this book combines developments of the basic theory with applications. It is written in a style suitable for the text of a graduate course in stochastic calculus, following a course in probability. Using the modern approach, the stochastic integral is defined for predictable integrands and local martingales; then It’s change of variable formula is developed for continuous martingales. Applications include a characterization of Brownian motion, Hermite polynomials of martingales, the Feynman–Kac functional and the Schrödinger equation. For Brownian motion, the topics of local time, reflected Brownian motion, and time change are discussed. New to the second edition are a discussion of the Cameron–Martin–Girsanov transformation and a final chapter which provides an introduction to stochastic differential equations, as well as many exercises for classroom use. This book will be a valuable resource to all mathematicians, statisticians, economists, and engineers employing the modern tools of stochastic analysis. The text also proves that stochastic integration has made an important impact on mathematical progress over the last decades and that stochastic calculus has become one of the most powerful tools in modern probability theory. —Journal of the American Statistical Association An attractive text...written in [a] lean and precise style...eminently readable. Especially pleasant are the care and attention devoted to details... A very fine book. —Mathematical Reviews

## An Introduction to Probability and Mathematical Statistics

**Author**: Howard G. Tucker

**Publisher:**Academic Press

**ISBN:**1483225143

**Category:**Mathematics

**Page:**240

**View:**5405

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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.

## Introduction to Mathematical Statistics

**Author**: Richard J. Larsen,3rd Edition Larsen and Marx,Morris L. Marx,Cram101 Textbook Reviews

**Publisher:**Academic Internet Pub Incorporated

**ISBN:**9781428814431

**Category:**Mathematics

**Page:**92

**View:**3678

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Never HIGHLIGHT a Book Again! Virtually all of the testable terms, concepts, persons, places, and events from the textbook are included. Cram101 Just the FACTS101 studyguides give all of the outlines, highlights, notes, and quizzes for your textbook with optional online comprehensive practice tests. Only Cram101 is Textbook Specific. Accompanys: 9780139223037 .

## Martingale Limit Theory and Its Application

**Author**: P. Hall,C. C. Heyde

**Publisher:**Academic Press

**ISBN:**1483263223

**Category:**Mathematics

**Page:**320

**View:**8603

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Martingale Limit Theory and Its Application discusses the asymptotic properties of martingales, particularly as regards key prototype of probabilistic behavior that has wide applications. The book explains the thesis that martingale theory is central to probability theory, and also examines the relationships between martingales and processes embeddable in or approximated by Brownian motion. The text reviews the martingale convergence theorem, the classical limit theory and analogs, and the martingale limit theorems viewed as the rate of convergence results in the martingale convergence theorem. The book explains the square function inequalities, weak law of large numbers, as well as the strong law of large numbers. The text discusses the reverse martingales, martingale tail sums, the invariance principles in the central limit theorem, and also the law of the iterated logarithm. The book investigates the limit theory for stationary processes via corresponding results for approximating martingales and the estimation of parameters from stochastic processes. The text can be profitably used as a reference for mathematicians, advanced students, and professors of higher mathematics or statistics.

## Modern Probability Theory and Its Applications

**Author**: Emanuel Parzen

**Publisher:**N.A

**ISBN:**N.A

**Category:**Probabilities

**Page:**464

**View:**6185

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## An introduction to probability and its applications

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

**Publisher:**Prentice Hall

**ISBN:**N.A

**Category:**Business & Economics

**Page:**404

**View:**8856

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Drawing heavily on real-world examples and case studies, this volume offers a calculus-based, non-measure theoretic, problem-solving-oriented introduction to probability.

## Statistics and Finance

*An Introduction*

**Author**: David Ruppert

**Publisher:**Springer Science & Business Media

**ISBN:**9780387202709

**Category:**Business & Economics

**Page:**473

**View:**3132

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This textbook emphasizes the applications of statistics and probability to finance. It reviews the basics and advanced topics are introduced, including behavioral finance. The book serves as a text in courses, and those in the finance industry can use it for self-study.

## Introduction to Probability and Mathematical Statistics

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

**Publisher:**Duxbury Press

**ISBN:**9780534380205

**Category:**Mathematics

**Page:**644

**View:**8618

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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 theory and mathematical statistics

**Author**: V. K. Rohatgi

**Publisher:**John Wiley & Sons Inc

**ISBN:**N.A

**Category:**Mathematics

**Page:**684

**View:**4243

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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.

## Mathematical Statistics and Data Analysis

**Author**: John A. Rice

**Publisher:**Cengage Learning

**ISBN:**0534399428

**Category:**Mathematics

**Page:**688

**View:**4242

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This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts that are set in abstract settings. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

## An Introduction to Stochastic Orders

**Author**: Felix Belzunce,Carolina Martinez Riquelme,Julio Mulero

**Publisher:**Academic Press

**ISBN:**0128038268

**Category:**Mathematics

**Page:**174

**View:**9786

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An Introduction to Stochastic Orders discusses this powerful tool that can be used in comparing probabilistic models in different areas such as reliability, survival analysis, risks, finance, and economics. The book provides a general background on this topic for students and researchers who want to use it as a tool for their research. In addition, users will find detailed proofs of the main results and applications to several probabilistic models of interest in several fields, and discussions of fundamental properties of several stochastic orders, in the univariate and multivariate cases, along with applications to probabilistic models. Introduces stochastic orders and its notation Discusses different orders of univariate stochastic orders Explains multivariate stochastic orders and their convex, likelihood ratio, and dispersive orders