Regression Estimators
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Regression Estimators
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Author : Marvin H. J. Gruber
language : en
Publisher: Academic Press
Release Date : 2014-05-10
Regression Estimators written by Marvin H. J. Gruber and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-10 with Mathematics categories.
Regression Estimators: A Comparative Study presents, compares, and contrasts the development and the properties of the ridge type estimators that result from both Bayesian and non-Bayesian (frequentist) methods. The book is divided into four parts. The first part (Chapters I and II) discusses the need for alternatives to least square estimators, gives a historical survey of the literature and summarizes basic ideas in Matrix Theory and Statistical Decision Theory used throughout the book. The second part (Chapters III and IV) covers the estimators from both the Bayesian and from the frequentist points of view and explores the mathematical relationships between them. The third part (Chapters V-VIII) considers the efficiency of the estimators with and without averaging over a prior distribution. Part IV, the final two chapters IX and X, suggests applications of the methods and results of Chapters III-VII to Kaiman Filters and Analysis of Variance, two very important areas of application. Statisticians and workers in fields that use statistical methods who would like to know more about the analytical properties of ridge type estimators will find the book invaluable.
Theory Of Ridge Regression Estimation With Applications
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Author : A. K. Md. Ehsanes Saleh
language : en
Publisher: John Wiley & Sons
Release Date : 2019-01-08
Theory Of Ridge Regression Estimation With Applications written by A. K. Md. Ehsanes Saleh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-08 with Mathematics categories.
A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.
Improving Efficiency By Shrinkage
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Author : Marvin Gruber
language : en
Publisher: Routledge
Release Date : 2017-11-01
Improving Efficiency By Shrinkage written by Marvin Gruber and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-01 with Mathematics categories.
Offers a treatment of different kinds of James-Stein and ridge regression estimators from a frequentist and Bayesian point of view. The book explains and compares estimators analytically as well as numerically and includes Mathematica and Maple programs used in numerical comparison.;College or university bookshops may order five or more copies at a special student rate, available on request.
Robust Regression
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Author : Kenneth D. Lawrence
language : en
Publisher: Routledge
Release Date : 2019-05-20
Robust Regression written by Kenneth D. Lawrence and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-20 with Mathematics categories.
Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weight each observation discusses generalized properties of Lp-estimators. Includes an algorithm for identifying outliers using least absolute value criterion in regression modeling reviews redescending M-estimators studies Li linear regression proposes the best linear unbiased estimators for fixed parameters and random errors in the mixed linear model summarizes known properties of Li estimators for time series analysis examines ordinary least squares, latent root regression, and a robust regression weighting scheme and evaluates results from five different robust ridge regression estimators.
Robust Regression And Outlier Detection
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Author : Peter J. Rousseeuw
language : en
Publisher: John Wiley & Sons
Release Date : 2005-02-25
Robust Regression And Outlier Detection written by Peter J. Rousseeuw and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-25 with Mathematics categories.
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selectedbooks that have been made more accessible to consumers in an effortto increase global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. "The writing style is clear and informal, and much of thediscussion is oriented to application. In short, the book is akeeper." –Mathematical Geology "I would highly recommend the addition of this book to thelibraries of both students and professionals. It is a usefultextbook for the graduate student, because it emphasizes both thephilosophy and practice of robustness in regression settings, andit provides excellent examples of precise, logical proofs oftheorems. . . .Even for those who are familiar with robustness, thebook will be a good reference because it consolidates the researchin high-breakdown affine equivariant estimators and includes anextensive bibliography in robust regression, outlier diagnostics,and related methods. The aim of this book, the authors tell us, is‘to make robust regression available for everyday statisticalpractice.’ Rousseeuw and Leroy have included all of thenecessary ingredients to make this happen." –Journal of the American Statistical Association
Linear Regression
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Author : Jürgen Groß
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Linear Regression written by Jürgen Groß and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
The book covers the basic theory of linear regression models and presents a comprehensive survey of different estimation techniques as alternatives and complements to least squares estimation. Proofs are given for the most relevant results, and the presented methods are illustrated with the help of numerical examples and graphics. Special emphasis is placed on practicability and possible applications. The book is rounded off by an introduction to the basics of decision theory and an appendix on matrix algebra.
Quantile Regression
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Author : Marilena Furno
language : en
Publisher: John Wiley & Sons
Release Date : 2018-07-18
Quantile Regression written by Marilena Furno and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-18 with Mathematics categories.
Contains an overview of several technical topics of Quantile Regression Volume two of Quantile Regression offers an important guide for applied researchers that draws on the same example-based approach adopted for the first volume. The text explores topics including robustness, expectiles, m-quantile, decomposition, time series, elemental sets and linear programming. Graphical representations are widely used to visually introduce several issues, and to illustrate each method. All the topics are treated theoretically and using real data examples. Designed as a practical resource, the book is thorough without getting too technical about the statistical background. The authors cover a wide range of QR models useful in several fields. The software commands in R and Stata are available in the appendixes and featured on the accompanying website. The text: Provides an overview of several technical topics such as robustness of quantile regressions, bootstrap and elemental sets, treatment effect estimators Compares quantile regression with alternative estimators like expectiles, M-estimators and M-quantiles Offers a general introduction to linear programming focusing on the simplex method as solving method for the quantile regression problem Considers time-series issues like non-stationarity, spurious regressions, cointegration, conditional heteroskedasticity via quantile regression Offers an analysis that is both theoretically and practical Presents real data examples and graphical representations to explain the technical issues Written for researchers and students in the fields of statistics, economics, econometrics, social and environmental science, this text offers guide to the theory and application of quantile regression models.
Adaptive Regression
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Author : Yadolah Dodge
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-01
Adaptive Regression written by Yadolah Dodge and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-01 with Mathematics categories.
While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
On The Biased Estimation In Regression
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Author : Esko Leskinen
language : en
Publisher:
Release Date : 1980
On The Biased Estimation In Regression written by Esko Leskinen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1980 with Estimation theory categories.
Functional Estimation For Density Regression Models And Processes
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Author : Odile Pons
language : en
Publisher: World Scientific
Release Date : 2011-03-21
Functional Estimation For Density Regression Models And Processes written by Odile Pons and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-21 with Mathematics categories.
This book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators.It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators.