Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Least Squares Model Averaging By Prediction Criterion
Download Least Squares Model Averaging By Prediction Criterion full books in PDF, epub, and Kindle. Read online Least Squares Model Averaging By Prediction Criterion ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics by : Jeffrey Racine
Download or read book The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics written by Jeffrey Racine and published by Oxford University Press. This book was released on 2014-04 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.
Download or read book Model Averaging written by David Fletcher and published by Springer. This book was released on 2019-01-17 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
Book Synopsis Specification Searches by : E. E. Leamer
Download or read book Specification Searches written by E. E. Leamer and published by . This book was released on 1978-04-24 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers a radically new approach to inference with nonexperimental data when the statistical model is ambiguously defined. Examines the process of model searching and its implications for inference. Identifies six different varieties of specification searches, discussing the inferential consequences of each in detail.
Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Book Synopsis Regression and Time Series Model Selection by : Allan D. R. McQuarrie
Download or read book Regression and Time Series Model Selection written by Allan D. R. McQuarrie and published by World Scientific. This book was released on 1998 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.
Book Synopsis Selecting Models from Data by : P. Cheeseman
Download or read book Selecting Models from Data written by P. Cheeseman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.
Book Synopsis Model Selection and Multimodel Inference by : Kenneth P. Burnham
Download or read book Model Selection and Multimodel Inference written by Kenneth P. Burnham and published by Springer Science & Business Media. This book was released on 2007-05-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.
Book Synopsis Model Selection and Model Averaging by : Gerda Claeskens
Download or read book Model Selection and Model Averaging written by Gerda Claeskens and published by . This book was released on 2008-07-28 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: First book to synthesize the research and practice from the active field of model selection.
Book Synopsis Statistical Rethinking by : Richard McElreath
Download or read book Statistical Rethinking written by Richard McElreath and published by CRC Press. This book was released on 2018-01-03 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
Book Synopsis Elements of Large-Sample Theory by : E.L. Lehmann
Download or read book Elements of Large-Sample Theory written by E.L. Lehmann and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by one of the main figures in twentieth century statistics, this book provides a unified treatment of first-order large-sample theory. It discusses a broad range of applications including introductions to density estimation, the bootstrap, and the asymptotics of survey methodology. The book is written at an elementary level making it accessible to most readers.
Book Synopsis Linear Regression Analysis by : Xin Yan
Download or read book Linear Regression Analysis written by Xin Yan and published by World Scientific. This book was released on 2009 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the techniques described in the book. This book is suitable for graduate students who are either majoring in statistics/biostatistics or using linear regression analysis substantially in their subject area." --Book Jacket.
Book Synopsis Bayesian Theory and Applications by : Paul Damien
Download or read book Bayesian Theory and Applications written by Paul Damien and published by Oxford University Press. This book was released on 2013-01-24 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field.
Book Synopsis Statistical Regression and Classification by : Norman Matloff
Download or read book Statistical Regression and Classification written by Norman Matloff and published by CRC Press. This book was released on 2017-09-19 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.
Download or read book Econometrics written by Bruce Hansen and published by Princeton University Press. This book was released on 2022-06-28 with total page 1081 pages. Available in PDF, EPUB and Kindle. Book excerpt: The most authoritative and up-to-date core econometrics textbook available Econometrics is the quantitative language of economic theory, analysis, and empirical work, and it has become a cornerstone of graduate economics programs. Econometrics provides graduate and PhD students with an essential introduction to this foundational subject in economics and serves as an invaluable reference for researchers and practitioners. This comprehensive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of econometrics. Covers the full breadth of econometric theory and methods with mathematical rigor while emphasizing intuitive explanations that are accessible to students of all backgrounds Draws on integrated, research-level datasets, provided on an accompanying website Discusses linear econometrics, time series, panel data, nonparametric methods, nonlinear econometric models, and modern machine learning Features hundreds of exercises that enable students to learn by doing Includes in-depth appendices on matrix algebra and useful inequalities and a wealth of real-world examples Can serve as a core textbook for a first-year PhD course in econometrics and as a follow-up to Bruce E. Hansen’s Probability and Statistics for Economists
Book Synopsis Information Criteria and Statistical Modeling by : Sadanori Konishi
Download or read book Information Criteria and Statistical Modeling written by Sadanori Konishi and published by Springer Science & Business Media. This book was released on 2008 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.
Book Synopsis The Statistical Theory of Linear Systems by : E. J. Hannan
Download or read book The Statistical Theory of Linear Systems written by E. J. Hannan and published by SIAM. This book was released on 2012-05-31 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originally published: New York: Wiley, c1988.
Book Synopsis Forecasting Financial Time Series Using Model Averaging by : Francesco Ravazzolo
Download or read book Forecasting Financial Time Series Using Model Averaging written by Francesco Ravazzolo and published by Rozenberg Publishers. This book was released on 2007 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Believing in a single model may be dangerous, and addressing model uncertainty by averaging different models in making forecasts may be very beneficial. In this thesis we focus on forecasting financial time series using model averaging schemes as a way to produce optimal forecasts. We derive and discuss in simulation exercises and empirical applications model averaging techniques that can reproduce stylized facts of financial time series, such as low predictability and time-varying patterns. We emphasize that model averaging is not a "magic" methodology which solves a priori problems of poorly forecasting. Averaging techniques have an essential requirement: individual models have to fit data. In the first section we provide a general outline of the thesis and its contributions to previ ous research. In Chapter 2 we focus on the use of time varying model weight combinations. In Chapter 3, we extend the analysis in the previous chapter to a new Bayesian averaging scheme that models structural instability carefully. In Chapter 4 we focus on forecasting the term structure of U.S. interest rates. In Chapter 5 we attempt to shed more light on forecasting performance of stochastic day-ahead price models. We examine six stochastic price models to forecast day-ahead prices of the two most active power exchanges in the world: the Nordic Power Exchange and the Amsterdam Power Exchange. Three of these forecasting models include weather forecasts. To sum up, the research finds an increase of forecasting power of financial time series when parameter uncertainty, model uncertainty and optimal decision making are included.
