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Author : Wolfgang Härdle
Publisher : Cambridge University Press
Page : 356 pages
File Size : 49,8 Mb
Release : 1990
Category : Business & Economics
ISBN : 0521429501
This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.
Author : Wolfgang Hrdle
Publisher : Unknown
Page : 333 pages
File Size : 41,9 Mb
Release : 1990
Category : Electronic
ISBN : OCLC:880995820
Author : Wolfgang Hrdle
Publisher : Unknown
Page : 0 pages
File Size : 45,6 Mb
Release : 1990
Category : Electronic
ISBN : OCLC:880995820
Author : M. Luz Gámiz,K. B. Kulasekera,Nikolaos Limnios,Bo Henry Lindqvist
Publisher : Springer Science & Business Media
Page : 230 pages
File Size : 50,6 Mb
Release : 2011-02-14
Category : Technology & Engineering
ISBN : 9780857291189
Nonparametric statistics has probably become the leading methodology for researchers performing data analysis. It is nevertheless true that, whereas these methods have already proved highly effective in other applied areas of knowledge such as biostatistics or social sciences, nonparametric analyses in reliability currently form an interesting area of study that has not yet been fully explored. Applied Nonparametric Statistics in Reliability is focused on the use of modern statistical methods for the estimation of dependability measures of reliability systems that operate under different conditions. The scope of the book includes: smooth estimation of the reliability function and hazard rate of non-repairable systems; study of stochastic processes for modelling the time evolution of systems when imperfect repairs are performed; nonparametric analysis of discrete and continuous time semi-Markov processes; isotonic regression analysis of the structure function of a reliability system, and lifetime regression analysis. Besides the explanation of the mathematical background, several numerical computations or simulations are presented as illustrative examples. The corresponding computer-based methods have been implemented using R and MATLAB®. A concrete modelling scheme is chosen for each practical situation and, in consequence, a nonparametric inference procedure is conducted. Applied Nonparametric Statistics in Reliability will serve the practical needs of scientists (statisticians and engineers) working on applied reliability subjects.
Author : Peter Sprent,Nigel C. Smeeton
Publisher : CRC Press
Page : 536 pages
File Size : 45,9 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781439894019
While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. Reorganized and with additional material, this edition begins with a brief summary of some
Author : Jeffrey Racine,Liangjun Su,Aman Ullah
Publisher : Oxford University Press
Page : 562 pages
File Size : 50,9 Mb
Release : 2014-04
Category : Business & Economics
ISBN : 9780199857944
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.
Author : Adrian W. Bowman,Adelchi Azzalini
Publisher : OUP Oxford
Page : 205 pages
File Size : 52,5 Mb
Release : 1997-08-14
Category : Mathematics
ISBN : 9780191545696
The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can be applied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawn from a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level, as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scripts are provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.
Author : Daniel J. Henderson,Christopher F. Parmeter
Publisher : Cambridge University Press
Page : 381 pages
File Size : 40,5 Mb
Release : 2015-01-19
Category : Business & Economics
ISBN : 9781107010253
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 : John Fox
Publisher : SAGE
Page : 100 pages
File Size : 43,5 Mb
Release : 2000-05
Category : Mathematics
ISBN : 0761921893
This volume introduces this useful technique which makes minimal assumptions about the form of relationship between the average response and the predictors.
Author : Peter Sprent
Publisher : Springer Science & Business Media
Page : 268 pages
File Size : 52,8 Mb
Release : 2012-12-06
Category : Science
ISBN : 9789400912236
This book is a practical introduction to statistical techniques called nonpara metric methods. Using examples, we explain assumptions and demonstrate procedures; theory is kept to a minimum. We show how basic problems are tackled and try to clear up common misapprehensions so as to help both students of statistics meeting the methods for the first time and workers in other fields faced with data needing simple but informative analysis. An analogy between experimenters and car drivers describes our aim. Statistical analyses may be done by following a set of rules without understanding their logical basis, but this has dangers. It is like driving a car with no inkling ofhow the internal combustion engine, the gears, the ignition system, the brakes actually work. Understanding the rudiments helps one get better performance and makesdrivingsafer;appropriate gearchanges become a way to reduce engine stress, prolong engine life, improve fuel economy, minimize wear on brake linings. Knowing how to change the engine oil or replace worn sparking plugs is notessential for adriver, but it will reduce costs. Learning such basics will not make one a fully fledged mechanic, even less an automotive engineer; but it all contributes to more economical and safer driving, alertingone to the dangers ofbald tyres, aleakingexhaust, worn brake linings.
Author : Hans-Georg Müller
Publisher : Springer Science & Business Media
Page : 208 pages
File Size : 49,7 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461239260
This monograph reviews some of the work that has been done for longitudi nal data in the rapidly expanding field of nonparametric regression. The aim is to give the reader an impression of the basic mathematical tools that have been applied, and also to provide intuition about the methods and applications. Applications to the analysis of longitudinal studies are emphasized to encourage the non-specialist and applied statistician to try these methods out. To facilitate this, FORTRAN programs are provided which carry out some of the procedures described in the text. The emphasis of most research work so far has been on the theoretical aspects of nonparametric regression. It is my hope that these techniques will gain a firm place in the repertoire of applied statisticians who realize the large potential for convincing applications and the need to use these techniques concurrently with parametric regression. This text evolved during a set of lectures given by the author at the Division of Statistics at the University of California, Davis in Fall 1986 and is based on the author's Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpublished work. Completeness is not attempted, neither in the text nor in the references. The following persons have been particularly generous in sharing research or giving advice: Th. Gasser, P. Ihm, Y. P. Mack, V. Mammi tzsch, G . G. Roussas, U. Stadtmuller, W. Stute and R.
Author : John Fox
Publisher : SAGE
Page : 100 pages
File Size : 45,9 Mb
Release : 2000-01-20
Category : Social Science
ISBN : 0761915850
Nonparametric simple regression forms the basis for nonparametric multiple regression and directly supplies the building blocks for the type of nonparametric multiple regression called additive regression.
Author : P.J. Green,Bernard. W. Silverman
Publisher : CRC Press
Page : 198 pages
File Size : 42,8 Mb
Release : 1993-05-01
Category : Mathematics
ISBN : 0412300400
In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method allows parametric assumptions to be realized in regression problems, in those approached by generalized linear modelling, and in many other contexts. The emphasis throughout is methodological rather than theoretical, and it concentrates on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. Some publicly available software is also discussed. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students and other encountering the material for the first time.
Author : K. Takezawa
Publisher : John Wiley & Sons
Page : 566 pages
File Size : 50,7 Mb
Release : 2005-12-02
Category : Mathematics
ISBN : 9780471771449
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 : Wayne W. Daniel
Publisher : Unknown
Page : 536 pages
File Size : 53,9 Mb
Release : 1978
Category : Nonparametric statistics
ISBN : UOM:39076006115476
Introduction and review; Procedures that utilize data from a single sample; Procedures that utilize data from two independent samples; Procedures that utilize data from two related samples; Chi-square tests of independence and homogeneity; Procedures that utilize data from three or more independent samples; Procedures that utilize data from three or more related; Coodness-of-fit tests; Rank correlation and other measures of association; Simple linear regression analysis.