Some New Results On Ridge Regression Estimation

Download Some New Results On Ridge Regression Estimation full books in PDF, epub, and Kindle. Read online Some New Results On Ridge Regression Estimation ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Some New Results on Ridge Regression Estimation

Author : Karl Lin
Publisher :
ISBN 13 :
Total Pages : 45 pages
Book Rating : 4.:/5 (118 download)

DOWNLOAD NOW!

Book Synopsis Some New Results on Ridge Regression Estimation by : Karl Lin

Download or read book Some New Results on Ridge Regression Estimation written by Karl Lin and published by . This book was released on 1979 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we consider various interpretations of the ordinary ridge regression estimator with a given shrinkage factor k, and report the results of an extensive Monte Carlo of several ridge regression estimators involving sample-based rules for selecting k. A major distinguishing feature of the study is the use of a general loss structure, the p-norm, in the evaluation process. Other factors taken into consideration include different degree of ill-conditioning of data, different number of explanatory variables, and different shape and non-centrality of the regression coefficients. The main results are: (i) With minor exceptions, all the ridge regression estimators considered yield a smaller average loss regardless of the loss function used. (ii) The reduction in the average loss of the ridge regression estimators increases when the degree ill-conditioning of data increases. The reduction reaches a substantial level when the degree of ill-conditioning is only moderate. (iii) On the basis of our experiment it is possible to make a recomendation concerning the rule of k.

Theory of Ridge Regression Estimation with Applications

Author : A. K. Md. Ehsanes Saleh
Publisher : John Wiley & Sons
ISBN 13 : 1118644611
Total Pages : 384 pages
Book Rating : 4.1/5 (186 download)

DOWNLOAD NOW!

Book Synopsis Theory of Ridge Regression Estimation with Applications by : A. K. Md. Ehsanes Saleh

Download or read book Theory of Ridge Regression Estimation with Applications written by A. K. Md. Ehsanes Saleh and published by John Wiley & Sons. This book was released on 2019-02-12 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Ridge Fuzzy Regression Modelling for Solving Multicollinearity

Author : Hyoshin Kim
Publisher : Infinite Study
ISBN 13 :
Total Pages : 15 pages
Book Rating : 4./5 ( download)

DOWNLOAD NOW!

Book Synopsis Ridge Fuzzy Regression Modelling for Solving Multicollinearity by : Hyoshin Kim

Download or read book Ridge Fuzzy Regression Modelling for Solving Multicollinearity written by Hyoshin Kim and published by Infinite Study. This book was released on with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes an a-level estimation algorithm for ridge fuzzy regression modeling, addressing the multicollinearity phenomenon in the fuzzy linear regression setting.

Applied Linear Statistical Models

Author : Michael H. Kutner
Publisher : McGraw-Hill/Irwin
ISBN 13 : 9780072386882
Total Pages : 1396 pages
Book Rating : 4.3/5 (868 download)

DOWNLOAD NOW!

Book Synopsis Applied Linear Statistical Models by : Michael H. Kutner

Download or read book Applied Linear Statistical Models written by Michael H. Kutner and published by McGraw-Hill/Irwin. This book was released on 2005 with total page 1396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear regression with one predictor variable; Inferences in regression and correlation analysis; Diagnosticis and remedial measures; Simultaneous inferences and other topics in regression analysis; Matrix approach to simple linear regression analysis; Multiple linear regression; Nonlinear regression; Design and analysis of single-factor studies; Multi-factor studies; Specialized study designs.

Regression Estimators

Author : Marvin H. J. Gruber
Publisher : Academic Press
ISBN 13 : 1483260976
Total Pages : 361 pages
Book Rating : 4.4/5 (832 download)

DOWNLOAD NOW!

Book Synopsis Regression Estimators by : Marvin H. J. Gruber

Download or read book Regression Estimators written by Marvin H. J. Gruber and published by Academic Press. This book was released on 2014-05-10 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Multiple Regression and Beyond

Author : Timothy Z. Keith
Publisher : Routledge
ISBN 13 : 1351667939
Total Pages : 640 pages
Book Rating : 4.3/5 (516 download)

DOWNLOAD NOW!

Book Synopsis Multiple Regression and Beyond by : Timothy Z. Keith

Download or read book Multiple Regression and Beyond written by Timothy Z. Keith and published by Routledge. This book was released on 2019-01-14 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companion Website materials: https://tzkeith.com/ Multiple Regression and Beyond offers a conceptually-oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. This book: • Covers both MR and SEM, while explaining their relevance to one another • Includes path analysis, confirmatory factor analysis, and latent growth modeling • Makes extensive use of real-world research examples in the chapters and in the end-of-chapter exercises • Extensive use of figures and tables providing examples and illustrating key concepts and techniques New to this edition: • New chapter on mediation, moderation, and common cause • New chapter on the analysis of interactions with latent variables and multilevel SEM • Expanded coverage of advanced SEM techniques in chapters 18 through 22 • International case studies and examples • Updated instructor and student online resources

Ridge Regression

Author : Matthew K. MacGue
Publisher :
ISBN 13 :
Total Pages : 486 pages
Book Rating : 4.:/5 (319 download)

DOWNLOAD NOW!

Book Synopsis Ridge Regression by : Matthew K. MacGue

Download or read book Ridge Regression written by Matthew K. MacGue and published by . This book was released on 1981 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Learning with Sparsity

Author : Trevor Hastie
Publisher : CRC Press
ISBN 13 : 1498712177
Total Pages : 354 pages
Book Rating : 4.4/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Statistical Learning with Sparsity by : Trevor Hastie

Download or read book Statistical Learning with Sparsity written by Trevor Hastie and published by CRC Press. This book was released on 2015-05-07 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl

Alternative Methods of Regression

Author : David Birkes
Publisher : John Wiley & Sons
ISBN 13 : 1118150244
Total Pages : 248 pages
Book Rating : 4.1/5 (181 download)

DOWNLOAD NOW!

Book Synopsis Alternative Methods of Regression by : David Birkes

Download or read book Alternative Methods of Regression written by David Birkes and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Linear Regression Analysis

Author : Xin Yan
Publisher : World Scientific
ISBN 13 : 9812834109
Total Pages : 349 pages
Book Rating : 4.8/5 (128 download)

DOWNLOAD NOW!

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.

Model Estimation Using Ridge Regression with the Variance Normalization Criterion

Author : Lee, Wan-Fung
Publisher : 1979.
ISBN 13 :
Total Pages : 200 pages
Book Rating : 4.:/5 (455 download)

DOWNLOAD NOW!

Book Synopsis Model Estimation Using Ridge Regression with the Variance Normalization Criterion by : Lee, Wan-Fung

Download or read book Model Estimation Using Ridge Regression with the Variance Normalization Criterion written by Lee, Wan-Fung and published by 1979.. This book was released on 1979 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Applied Regression Analysis and Other Multivariable Methods

Author : David G. Kleinbaum
Publisher : Duxbury
ISBN 13 : 9780495384984
Total Pages : 906 pages
Book Rating : 4.3/5 (849 download)

DOWNLOAD NOW!

Book Synopsis Applied Regression Analysis and Other Multivariable Methods by : David G. Kleinbaum

Download or read book Applied Regression Analysis and Other Multivariable Methods written by David G. Kleinbaum and published by Duxbury. This book was released on 2008 with total page 906 pages. Available in PDF, EPUB and Kindle. Book excerpt: This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques.

Lectures on Probability Theory and Mathematical Statistics - 3rd Edition

Author : Marco Taboga
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781981369195
Total Pages : 670 pages
Book Rating : 4.3/5 (691 download)

DOWNLOAD NOW!

Book Synopsis Lectures on Probability Theory and Mathematical Statistics - 3rd Edition by : Marco Taboga

Download or read book Lectures on Probability Theory and Mathematical Statistics - 3rd Edition written by Marco Taboga and published by Createspace Independent Publishing Platform. This book was released on 2017-12-08 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a collection of 80 short and self-contained lectures covering most of the topics that are usually taught in intermediate courses in probability theory and mathematical statistics. There are hundreds of examples, solved exercises and detailed derivations of important results. The step-by-step approach makes the book easy to understand and ideal for self-study. One of the main aims of the book is to be a time saver: it contains several results and proofs, especially on probability distributions, that are hard to find in standard references and are scattered here and there in more specialistic books. The topics covered by the book are as follows. PART 1 - MATHEMATICAL TOOLS: set theory, permutations, combinations, partitions, sequences and limits, review of differentiation and integration rules, the Gamma and Beta functions. PART 2 - FUNDAMENTALS OF PROBABILITY: events, probability, independence, conditional probability, Bayes' rule, random variables and random vectors, expected value, variance, covariance, correlation, covariance matrix, conditional distributions and conditional expectation, independent variables, indicator functions. PART 3 - ADDITIONAL TOPICS IN PROBABILITY THEORY: probabilistic inequalities, construction of probability distributions, transformations of probability distributions, moments and cross-moments, moment generating functions, characteristic functions. PART 4 - PROBABILITY DISTRIBUTIONS: Bernoulli, binomial, Poisson, uniform, exponential, normal, Chi-square, Gamma, Student's t, F, multinomial, multivariate normal, multivariate Student's t, Wishart. PART 5 - MORE DETAILS ABOUT THE NORMAL DISTRIBUTION: linear combinations, quadratic forms, partitions. PART 6 - ASYMPTOTIC THEORY: sequences of random vectors and random variables, pointwise convergence, almost sure convergence, convergence in probability, mean-square convergence, convergence in distribution, relations between modes of convergence, Laws of Large Numbers, Central Limit Theorems, Continuous Mapping Theorem, Slutsky's Theorem. PART 7 - FUNDAMENTALS OF STATISTICS: statistical inference, point estimation, set estimation, hypothesis testing, statistical inferences about the mean, statistical inferences about the variance.

Improving Efficiency by Shrinkage

Author : Marvin Gruber
Publisher : Routledge
ISBN 13 : 1351439162
Total Pages : 648 pages
Book Rating : 4.3/5 (514 download)

DOWNLOAD NOW!

Book Synopsis Improving Efficiency by Shrinkage by : Marvin Gruber

Download or read book Improving Efficiency by Shrinkage written by Marvin Gruber and published by Routledge. This book was released on 2017-11-01 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Practical Statistics for Data Scientists

Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 322 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Parameter Estimation in Engineering and Science

Author : James Vere Beck
Publisher : James Beck
ISBN 13 : 9780471061182
Total Pages : 540 pages
Book Rating : 4.0/5 (611 download)

DOWNLOAD NOW!

Book Synopsis Parameter Estimation in Engineering and Science by : James Vere Beck

Download or read book Parameter Estimation in Engineering and Science written by James Vere Beck and published by James Beck. This book was released on 1977 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to and survey of parameter estimation; Probability; Introduction to statistics; Parameter estimation methods; Introduction to linear estimation; Matrix analysis for linear parameter estimation; Minimization of sum of squares functions for models nonlinear in parameters; Design of optimal experiments.

Linear Models in Statistics

Author : Alvin C. Rencher
Publisher : John Wiley & Sons
ISBN 13 : 0470192607
Total Pages : 690 pages
Book Rating : 4.4/5 (71 download)

DOWNLOAD NOW!

Book Synopsis Linear Models in Statistics by : Alvin C. Rencher

Download or read book Linear Models in Statistics written by Alvin C. Rencher and published by John Wiley & Sons. This book was released on 2008-01-07 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.