A Distribution-Free Theory of Nonparametric Regression PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download A Distribution-Free Theory of Nonparametric Regression PDF full book. Access full book title A Distribution-Free Theory of Nonparametric Regression by László Györfi. Download full books in PDF and EPUB format.
Author: László Györfi
Publisher: Springer Science & Business Media
ISBN: 0387954414
Category : Mathematics
Languages : en
Pages : 662
Get Book Here
Book Description
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.
Author: László Györfi
Publisher: Springer Science & Business Media
ISBN: 0387954414
Category : Mathematics
Languages : en
Pages : 662
Get Book Here
Book Description
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.
Author: László Györfi
Publisher: Springer Science & Business Media
ISBN: 0387224424
Category : Mathematics
Languages : en
Pages : 662
Get Book Here
Book Description
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.
Author: Thorsten Dickhaus
Publisher: Springer
ISBN: 3319763156
Category : Mathematics
Languages : en
Pages : 134
Get Book Here
Book Description
This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.
Author: Sam Efromovich
Publisher: Springer Science & Business Media
ISBN: 0387226389
Category : Mathematics
Languages : en
Pages : 423
Get Book Here
Book Description
This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis. The companion software package, available over the Internet, brings all of the discussed topics into the realm of interactive research. Virtually every claim and development mentioned in the book is illustrated with graphs which are available for the reader to reproduce and modify, making the material fully transparent and allowing for complete interactivity.
Author: Wolfgang Karl Härdle
Publisher: Springer Science & Business Media
ISBN: 364217146X
Category : Mathematics
Languages : en
Pages : 317
Get Book Here
Book Description
The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.
Author: Albert W. Marshall
Publisher: Springer Science & Business Media
ISBN: 0387684778
Category : Technology & Engineering
Languages : en
Pages : 785
Get Book Here
Book Description
This book is devoted to the study of univariate distributions appropriate for the analyses of data known to be nonnegative. The book includes much material from reliability theory in engineering and survival analysis in medicine.
Author: Larry Wasserman
Publisher: Springer Science & Business Media
ISBN: 0387306234
Category : Mathematics
Languages : en
Pages : 272
Get Book Here
Book Description
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.
Author: K. Takezawa
Publisher: John Wiley & Sons
ISBN: 0471771449
Category : Mathematics
Languages : en
Pages : 566
Get Book Here
Book Description
An easy-to-grasp introduction to nonparametric regression This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features: * Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods * Statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions * Mathematical equations that are accompanied by a clear explanation of how the equation was derived The first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts. With a learning-by-doing approach, each topical chapter includes thorough S-Plus? examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects. In addition, each chapter ends with a set of problems that test readers' grasp of key concepts and techniques and also prepares them for more advanced topics. This book is recommended as a textbook for undergraduate and graduate courses in nonparametric regression. Only a basic knowledge of linear algebra and statistics is required. In addition, this is an excellent resource for researchers and engineers in such fields as pattern recognition, speech understanding, and data mining. Practitioners who rely on nonparametric regression for analyzing data in the physical, biological, and social sciences, as well as in finance and economics, will find this an unparalleled resource.
Author: Daniel J. Henderson
Publisher: Cambridge University Press
ISBN: 110701025X
Category : Business & Economics
Languages : en
Pages : 381
Get Book Here
Book Description
The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.
Author: Eric D. Feigelson
Publisher: Cambridge University Press
ISBN: 052176727X
Category : Science
Languages : en
Pages : 495
Get Book Here
Book Description
Modern Statistical Methods for Astronomy: With R Applications.
