Articles by "Statistics"

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Mathematical Statistics with Applications in R 3rd Edition

Kandethody M. Ramachandran, Chris P. Tsokos ... 704 pages - Publisher: Academic Press; 3rd edition (June, 2020) ... Language: English - ISBN-10: 0128178159 - ISBN-13: 978-0128178157.

Mathematical Statistics with Applications in R, Third 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 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. Step-by-step procedure to solve real problems make the topics very accessible.

Probability: Theory and Examples 5th Edition

Rick Durrett ... 430 pages - Publisher: Cambridge Univ. Press; 5th edition (May, 2019) ... Language: English - ISBN-10: 1108473687 - ISBN-13: 978-1108473682.

This lively introduction to measure-theoretic probability theory covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. Concentrating on results that are the most useful for applications, this comprehensive treatment is a rigorous graduate text and reference. Operating under the philosophy that the best way to learn probability is to see it in action, the book contains extended examples that apply the theory to concrete applications. This fifth edition contains a new chapter on multidimensional Brownian motion and its relationship to partial differential equations (PDEs), an advanced topic that is finding new applications. Setting the foundation for this expansion, Chapter 7 now features a proof of Itô's formula. Key exercises that previously were simply proofs left to the reader have been directly inserted into the text as lemmas. The new edition re-instates discussion about the central limit theorem for martingales and stationary sequences.

Introduction to Mathematical Statistics 8th Edition

Robert V. Hogg, Joseph W. McKean, Allen T. Craig ... 768 pages - Publisher: Pearson; 8th edition (January, 2018) ... Language: English - ISBN-10: 0134686993 - ISBN-13: 978-0134686998.

Comprehensive coverage of mathematical statistics — with a proven approach: Introduction to Mathematical Statistics by Hogg, McKean, and Craig enhances student comprehension and retention with numerous, illustrative examples and exercises. Classical statistical inference procedures in estimation and testing are explored extensively, and the text’s flexible organization makes it ideal for a range of mathematical statistics courses. Substantial changes to the 8th Edition — many based on user feedback — help students appreciate the connection between statistical theory and statistical practice, while other changes enhance the development and discussion of the statistical theory presented.

Data Analysis with Stata by Prasad Kothari

Prasad Kothari ... 176 pages - Publisher: Packt Publishing; (October, 2015) ... Language: English - AmazonSIN: B013R02BOC.

STATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) [and custom programming. This book covers data management, graphs visualization, and programming in STATA. Starting with an introduction to STATA and data analytics you'll move on to STATA programming and data management. Next, the book takes you through data visualization and all the important statistical tests in STATA. Linear and logistic regression in STATA is also covered. As you progress through the book, you will explore a few analyses, including the survey analysis, time series analysis, and survival analysis in STATA. You'll also discover different types of statistical modelling techniques and learn how to implement these techniques in STATA.

Predictive Statistics Analysis and Inference Beyond Models

Bertrand S. Clarke, Jennifer L. Clarke ... 656 pages - Publisher: Cambridge Univ. Press; (April, 2018) ... Language: English - ISBN-10: 1107028280 - ISBN-13: 978-1107028289.

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science

Bradley Efron, Trevor Hastie ... 495 pages - Publisher: Cambridge Univ. Press; (July, 2016) ... Language: English - ISBN-10: 1107149894 - ISBN-13: 978-1107149892.

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.

An Introduction to Statistics: An Active Learning Approach 2nd Edition

Kieth A. Carlson, Jennifer R. Winquist ... 656 pages - Publisher: SAGE Publications, Inc; 2nd edition (February, 2017) ... Language: English - ISBN-10: 148337873X - ISBN-13: 978-1483378732.

An Introduction to Statistics: An Active Learning Approach, Second Edition by Kieth A. Carlson and Jennifer R. Winquist takes a unique, active approach to teaching and learning introductory statistics that allows students to discover and correct their misunderstandings as chapters progress rather than at their conclusion. Empirically-developed, self-correcting activities reinforce and expand on fundamental concepts, targeting and holding students’ attention. Based on contemporary memory research, this learner-centered approach leads to better long-term retention through active engagement while generating explanations. Along with carefully placed reading questions, this edition includes learning objectives, realistic research scenarios, practice problems, self-test questions, problem sets, and practice tests to help students become more confident in their ability to perform statistics.

Introduction to Probability and Statistics 2nd Edition

Narayan C. Giri ... 537 pages - Publisher: CRC Press; 2nd edition (January, 2019) ... Language: English - AmazonSIN: B07M7XVF7J.

Beginning with the historical background of probability theory, this thoroughly revised text examines all important aspects of mathematical probability - including random variables, probability distributions, characteristic and generating functions, stochatic convergence, and limit theorems - and provides an introduction to various types of statistical problems, covering the broad range of statistical inference.; Requiring a prerequisite in calculus for complete understanding of the topics discussed, the Second Edition contains new material on: univariate distributions; multivariate distributions; large-sample methods; decision theory; and applications of ANOVA.; A primary text for a year-long undergraduate course in statistics (but easily adapted for a one-semester course in probability only), Introduction to Probability and Statistics is for undergraduate students in a wide range of disciplines-statistics, probability, mathematics, social science, economics, engineering, agriculture, biometry, and education.

All of Statistics: A Concise Course in Statistical Inference

Larry Wasserman ... 442 pages - Publisher: Springer; (September, 2004) ... Language: English - ISBN-10: 0387402721 - ISBN-13: 978-0387402727.

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.

Introduction to Multivariate Analysis

Chris Chatfield, A. Collins ... 248 pages - Publisher: Chapman and Hall/CRC; (May, 1981) ... Language: English - ISBN-10: 9780412160400 - ISBN-13: 978-0412160400.

This book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis,traditional normal theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research,soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysis.

Introduction to Multivariate Analysis: Linear and Nonlinear Modeling

Sadanori Konishi ... 338 pages - Publisher: Chapman and Hall/CRC; (June, 2014) ... Language: English - ISBN-10: 1466567287 - ISBN-13: 978-1466567283.

Introduction to Multivariate Analysis: Linear and Nonlinear Modeling shows how multivariate analysis is widely used for extracting useful information and patterns from multivariate data and for understanding the structure of random phenomena. Along with the basic concepts of various procedures in traditional multivariate analysis, the book covers nonlinear techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination, dimension reduction, and clustering. The text thoroughly explains the concepts and derivations of the AIC, BIC, and related criteria and includes a wide range of practical examples of model selection and evaluation criteria. To estimate and evaluate models with a large number of predictor variables, the author presents regularization methods, including the L1 norm regularization that gives simultaneous model estimation and variable selection. For advanced undergraduate and graduate students in statistical science, this text provides a systematic description of both traditional and newer techniques in multivariate analysis and machine learning. It also introduces linear and nonlinear statistical modeling for researchers and practitioners in industrial and systems engineering, information science, life science, and other areas.

Handbook of Quantile Regression by R. Koenker

Roger Koenker, Victor Chernozhukov, Xuming He, Limin Peng ... 483 pages - Publisher: Chapman and Hall/CRC; (October, 2017) ... Language: English - AmazonSIN: B076DG4VR4.

Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments.

The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines.

Applied Multivariate Statistical Analysis 5th Edition

Wolfgang Karl Härdle, Léopold Simar ... 558 pages - Publisher: Springer; 5th edition(November, 2019) ... Language: English - ISBN-10: 3030260054 - ISBN-13: 978-3030260057.

This textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners. All chapters include practical exercises that highlight applications in different multivariate data analysis fields, and all the examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis.

For this new edition, the book has been updated and extensively revised and now includes an extended chapter on cluster analysis. All solutions to the exercises are supplemented by R and MATLAB or SAS computer code and can be downloaded from the Quantlet platform. Practical exercises from this book and their solutions can also be found in the accompanying Springer book by W.K. Härdle and Z. Hlávka: Multivariate Statistics - Exercises and Solutions.

High-Dimensional Probability: An Introduction with Applications in Data Science

Roman Vershynin ... 296 pages - Publisher: Cambridge Univ.Press; (September, 2018) ... Language: English - ISBN-10: 1108415199 - ISBN-13: 978-1108415194.

High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.

Applied Multivariate Statistical Concepts by Debbie L. Hahs-Vaughn

Debbie L. Hahs-Vaughn ... 662 pages - Publisher: Routledge; (November, 2016) ... Language: English - ISBN-10: 0415842360 - ISBN-13: 978-0415842365.

More comprehensive than other texts, this new book covers the classic and cutting edge multivariate techniques used in today’s research. Ideal for courses on multivariate statistics/analysis/design, advanced statistics or quantitative techniques taught in psychology, education, sociology, and business, the book also appeals to researchers with no training in multivariate methods. Through clear writing and engaging pedagogy and examples using real data, Hahs-Vaughn walks students through the most used methods to learn why and how to apply each technique. A conceptual approach with a higher than usual text-to-formula ratio helps reader’s master key concepts so they can implement and interpret results generated by today’s sophisticated software. Annotated screenshots from SPSS and other packages are integrated throughout. Designed for course flexibility, after the first 4 chapters, instructors can use chapters in any sequence or combination to fit the needs of their students. Each chapter includes a ‘mathematical snapshot’ that highlights the technical components of each procedure, so only the most crucial equations are included.

Highlights include: -Outlines, key concepts, and vignettes related to key concepts preview what’s to come in each chapter. -Examples using real data from education, psychology, and other social sciences illustrate key concepts. -Extensive coverage of assumptions including tables, the effects of their violation, and how to test for each technique. -Conceptual, computational, and interpretative problems mirror the real-world problems students encounter in their studies and careers. -A focus on data screening and power analysis with attention on the special needs of each particular method. -Instructions for using SPSS via screenshots and annotated output along with HLM, Mplus, LISREL, and G*Power where appropriate, to demonstrate how to interpret results. -Templates for writing research questions and APA-style write-ups of results which serve as models. -Propensity score analysis chapter that demonstrates the use of this increasingly popular technique. -A review of matrix algebra for those who want an introduction (prerequisites include an introduction to factorial ANOVA, ANCOVA, and simple linear regression, but knowledge of matrix algebra is not assumed).

Applied Linear Regression 4th Edition

Sanford Weisberg ... 368 pages - Publisher: Wiley; 4th edition (December, 2013) ... Language: English - ISBN-10: 9781118386088 - ISBN-13: 978-1118386088.

The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression.

While maintaining the accessible appeal of each previous edition, Applied Linear Regression, Fourth Edition features: Graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis + In-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics + Newly added material on topics including testing, ANOVA, and variance assumptions + Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods. Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.

Applied Statistics and Multivariate Data Analysis for Business and Economics

Thomas Cleff ... 497 pages - Publisher: Springer; (July, 2019) ... Language: English - AmazonSIN: B07V82V6L6.

This textbook will familiarize students in economics and business, as well as practitioners, with the basic principles, techniques, and applications of applied statistics, statistical testing, and multivariate data analysis. Drawing on practical examples from the business world, it demonstrates the methods of univariate, bivariate, and multivariate statistical analysis. The textbook covers a range of topics, from data collection and scaling to the presentation and simple univariate analysis of quantitative data, while also providing advanced analytical procedures for assessing multivariate relationships. Accordingly, it addresses all topics typically covered in university courses on statistics and advanced applied data analysis. In addition, it does not limit itself to presenting applied methods, but also discusses the related use of Excel, SPSS, and Stata.

Data Analysis Using Stata 3rd Edition

Ulrich Kohler, Frauke Kreuter ... 497 pages - Publisher: Stata Press; 3rd edition (August, 2012) ... Language: English - ISBN-10: 1597181102 - ISBN-13: 978-1597181105.

Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks. The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand. Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.

Statistics: An Introduction-The Easy Way to Learn Stats

Alan Graham ... 320 pages - Publisher: Teach Yourself; (April, 2017) ... Language: English - AmazonSIN: B01LZ6WZXS.

Do you need to gain confidence with handling numbers and formulae? Do you want a clear, step-by-step guide to the key concepts and principles of statistics? Nearly all aspects of our lives can be subject to statistical analysis. Statistics: An Introduction shows you how to interpret, analyze and present figures. Assuming minimal knowledge of maths and using examples from a wide variety of everyday contexts, this book makes often complex concepts and techniques easy to get to grips with. This new edition has been fully updated. Whether you want to understand the statistics that you are bombarded with every day or are a student or professional coming to statistics from a wide range of disciplines, Statistics: An Introduction covers it all.

Statistical Techniques for Transportation Engineering

Kumar Molugaram, G. Shanker Rao ... 538 pages - Publisher: Butterworth-Heinemann; (March, 2017) ... Language: English - AmazonSIN: B06XFRF985.

Statistical Techniques for Transportation Engineering is written with a systematic approach in mind and covers a full range of data analysis topics, from the introductory level (basic probability, measures of dispersion, random variable, discrete and continuous distributions) through more generally used techniques (common statistical distributions, hypothesis testing), to advanced analysis and statistical modeling techniques (regression, AnoVa, and time series). The book also provides worked out examples and solved problems for a wide variety of transportation engineering challenges.

Demonstrates how to effectively interpret, summarize, and report transportation data using appropriate statistical descriptors + Teaches how to identify and apply appropriate analysis methods for transportation data + Explains how to evaluate transportation proposals and schemes with statistical rigor.

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