Robust Estimation in Nonlinear Regression and Limited Dependent Variable Models PDF Download
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Author: Pavel Čížek
Publisher:
ISBN:
Category : Robust statistics
Languages : en
Pages : 86
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Book Description
Author: Pavel Čížek
Publisher:
ISBN:
Category : Robust statistics
Languages : en
Pages : 86
Get Book Here
Book Description
Author: Pavel Čížek
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Pavel Čížek
Publisher:
ISBN:
Category :
Languages : en
Pages : 41
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Book Description
Author: Helga Bunke
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 458
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Book Description
This book, the second volume in a three part work, provides a comprehensive and unified account of nonlinear regression analysis, functional and structural relations, and of nonparametric and robust estimators. Research in these areas has been stimulated by the increase in computational capabilities and this volume will therefore be of great interest to researchers in statistics as well as applied statisticians working in industry. The material provided includes recent work from German and Russian sources, as well as from English-speaking sources, and the treatment throughout is mathematically rigorous but accessible. The text will benefit rsearchers in statistics and applied statisticians working in industry.
Author: Mia Hubert
Publisher: Birkhäuser
ISBN: 303487958X
Category : Mathematics
Languages : en
Pages : 399
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Book Description
Intended for both researchers and practitioners, this book will be a valuable resource for studying and applying recent robust statistical methods. It contains up-to-date research results in the theory of robust statistics Treats computational aspects and algorithms and shows interesting and new applications.
Author: K. Mukherjee
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Book Description
Author: Pavel Cizek
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The least squares estimator is probably the most frequently used estimation method in regression analysis. Unfortunately, it is also quite sensitive to data contamination and model misspecification. Although there are several robust estimators designed for parametric regression models that can be used in place of least squares, these robust estimators cannot be easily applied to models containing binary and categorical explanatory variables. Therefore, I design a robust estimator that can be used for any linear regression model no matter what kind of explanatory variables the model contains. Additionally, I propose an adaptive procedure that maximizes the efficiency of the proposed estimator for a given data set while preserving its robustness.
Author: David Birkes
Publisher: John Wiley & Sons
ISBN: 1118150244
Category : Mathematics
Languages : en
Pages : 248
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Book Description
Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data sets real. Topics include: multi-response parameter estimation; models defined by systems of differential equations; and improved methods for presenting inferential results of nonlinear analysis. 1988 (0-471-81643-4) 365 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild ".[a] comprehensive and scholarly work.impressively thorough with attention given to every aspect of the modeling process." --Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range of estimation techniques including least squares, quasi-likelihood, and Bayesian methods, and discuss some of the problems associated with estimation. The book presents new and important material relating to the concept of curvature and its growing role in statistical inference. It also covers three useful classes of models --growth, compartmental, and multiphase --and emphasizes the limitations involved in fitting these models. Packed with examples and graphs, it offers statisticians, statistical consultants, and statistically oriented research scientists up-to-date access to their fields. 1989 (0-471-61760-1) 768 pp. Mathematical Programming in Statistics T. S. Arthanari and Yadolah Dodge "The authors have achieved their stated intention.in an outstanding and useful manner for both students and researchers.Contains a superb synthesis of references linked to the special topics and formulations by a succinct set of bibliographical notes.Should be in the hands of all system analysts and computer system architects." --Computing Reviews This unique book brings together most of the available results on applications of mathematical programming in statistics, and also develops the necessary statistical and programming theory and methods. 1981 (0-471-08073-X) 413 pp.
Author: Christian Ritz
Publisher: Springer Science & Business Media
ISBN: 0387096167
Category : Mathematics
Languages : en
Pages : 151
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Book Description
- Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.
Author: Hengqing Tong
Publisher: John Wiley & Sons
ISBN: 0470681772
Category : Business & Economics
Languages : en
Pages : 489
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Book Description
Statistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mining Highlights recent advances in statistical theory and methods that benefit econometric practice. Features examples with real life data. Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics; business analysts involved in economics and management; and Graduate students and researchers in economics and statistics.
