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Author : Carolyn Helen Lichtenstein
Publisher :
ISBN 13 :
Total Pages : 214 pages
Book Rating : 4.E/5 ( download)
Download or read book Ridge Regression and Its Effect on High Leverage Points in the Data written by Carolyn Helen Lichtenstein and published by . This book was released on 1981 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : David A. Belsley
Publisher : Wiley-Interscience
ISBN 13 : 9780471528890
Total Pages : 0 pages
Book Rating : 4.5/5 (288 download)
Download or read book Conditioning Diagnostics written by David A. Belsley and published by Wiley-Interscience. This book was released on 1991-02-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrating the research from the author's previous work, Regression Diagnostics, and significant revision and updating, this monograph presents a self-contained treatment of the problems of ill-conditioning and data weaknesses as they affect the least-squares estimation of the linear model, along with extensions to nonlinear models and simultaneous-equations estimators. Also features a substantial amount of new information, including background material and data sets and numerous related elements previously scattered throughout the literature.
Author : Dalip Kumar
Publisher :
ISBN 13 :
Total Pages : 70 pages
Book Rating : 4.:/5 (111 download)
Download or read book Ridge Regression and Lasso Estimators for Data Analysis written by Dalip Kumar and published by . This book was released on 2019 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: An important problem in data science and statistical learning is to predict an outcome based on data collected on several predictor variables. This is generally known as a regression problem. In the field of big data studies, the regression model often depends on a large number of predictor variables. The data scientist is often dealing with the difficult task of determining the most appropriate set of predictor variables to be employed in the regression model. In this thesis we adopt a technique that constraints the coefficient estimates which in effect shrinks the coefficient estimates towards zero. Ridge regression and lasso are two well-known methods for shrinking the coefficients towards zero. These two methods are investigated in this thesis. Ridge regression and lasso techniques are compared by analyzing a real data set for a regression model with a large collection of predictor variables.
Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 322 pages
Book Rating : 4.4/5 (919 download)
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
Author : A. K. Md. Ehsanes Saleh
Publisher : John Wiley & Sons
ISBN 13 : 1118644522
Total Pages : 380 pages
Book Rating : 4.1/5 (186 download)
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 2019-01-08 with total page 380 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.
Author : Ali S. Hadi
Publisher : John Wiley & Sons
ISBN 13 : 1119830893
Total Pages : 485 pages
Book Rating : 4.1/5 (198 download)
Download or read book Regression Analysis By Example Using R written by Ali S. Hadi and published by John Wiley & Sons. This book was released on 2023-10-11 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression Analysis By Example Using R A STRAIGHTFORWARD AND CONCISE DISCUSSION OF THE ESSENTIALS OF REGRESSION ANALYSIS In the newly revised sixth edition of Regression Analysis By Example Using R, distinguished statistician Dr Ali S. Hadi delivers an expanded and thoroughly updated discussion of exploratory data analysis using regression analysis in R. The book provides in-depth treatments of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The author clearly demonstrates effective methods of regression analysis with examples that contain the types of data irregularities commonly encountered in the real world. This newest edition also offers a brand-new, easy to read chapter on the freely available statistical software package R. Readers will also find: Reorganized, expanded, and upgraded exercises at the end of each chapter with an emphasis on data analysis Updated data sets and examples throughout the book Complimentary access to a companion website that provides data sets in xlsx, csv, and txt format Perfect for upper-level undergraduate or beginning graduate students in statistics, mathematics, biostatistics, and computer science programs, Regression Analysis By Example Using R will also benefit readers who need a reference for quick updates on regression methods and applications.
Author : Måns Thulin
Publisher : CRC Press
ISBN 13 : 9781032512440
Total Pages : 0 pages
Book Rating : 4.5/5 (124 download)
Download or read book Modern Statistics with R written by Måns Thulin and published by CRC Press. This book was released on 2024-08-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling - importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis - using visualisations and multivariate techniques to explore datasets. Statistical inference - modern methods for testing hypotheses and computing confidence intervals. Predictive modelling - regression models and machine learning methods for prediction, classification, and forecasting. Simulation - using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics - ethical issues and good statistical practice. R programming - writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book. In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.
Author : Dr Jitendra Singh
Publisher : Dr. Jitendra Singh
ISBN 13 :
Total Pages : 185 pages
Book Rating : 4./5 ( download)
Download or read book Advanced Statistical Techniques written by Dr Jitendra Singh and published by Dr. Jitendra Singh. This book was released on 2024-10-02 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to "Advanced Statistical Techniques," a comprehensive guide designed for students preparing for the CSIR NET (JRF) in Mathematical Sciences and other competitive mathematics examinations. This book aims to bridge theoretical concepts with practical applications, providing a solid foundation in advanced statistical methodologies. The structure of this book is meticulously crafted to guide readers through essential topics, starting with nonparametric tests. We delve into simple tests for one and two sample problems, explore rank correlation, and examine tests for independence, ensuring a robust understanding of situations where traditional parametric methods may fall short. Next, we introduce Bayesian inference, emphasizing fundamental concepts, posterior distributions, and the pivotal Bayes theorem in statistical inference. This section aims to equip readers with a modern statistical perspective, highlighting the flexibility and power of Bayesian methods. The discussion progresses to linear models, covering Gauss-Markov models and the estimability of parameters, culminating in an exploration of Best Linear Unbiased Estimators (BLUE). We then tackle Analysis of Variance and Covariance (ANOVA and ANCOVA), detailing one-way and two-way ANOVA, and discussing fixed, random, and mixed effects models alongside analysis of covariance. Finally, our journey concludes with regression analysis, encompassing simple and multiple linear regression, essential regression diagnostics, and an introduction to logistic regression models.We hope you find it enlightening and empowering on your statistical journey.
Author : Xin Yan
Publisher : World Scientific
ISBN 13 : 9812834109
Total Pages : 349 pages
Book Rating : 4.8/5 (128 download)
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.
Author : C. Radhakrishna Rao
Publisher : Springer Science & Business Media
ISBN 13 : 3540742271
Total Pages : 583 pages
Book Rating : 4.5/5 (47 download)
Download or read book Linear Models and Generalizations written by C. Radhakrishna Rao and published by Springer Science & Business Media. This book was released on 2007-10-15 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights of coverage include sensitivity analysis and model selection, an analysis of incomplete data, an analysis of categorical data based on a unified presentation of generalized linear models, and an extensive appendix on matrix theory.
Author :
Publisher :
ISBN 13 :
Total Pages : 552 pages
Book Rating : 4.3/5 (91 download)
Download or read book Technometrics written by and published by . This book was released on 2001 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Chidchanok Churngchow
Publisher :
ISBN 13 :
Total Pages : 262 pages
Book Rating : 4.:/5 (178 download)
Download or read book Ridge Regression written by Chidchanok Churngchow and published by . This book was released on 1988 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : John Fox
Publisher : SAGE
ISBN 13 : 9780803939714
Total Pages : 100 pages
Book Rating : 4.9/5 (397 download)
Download or read book Regression Diagnostics written by John Fox and published by SAGE. This book was released on 1991-08-14 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explaining the techniques needed for exploring problems that comprise a regression analysis, and for determining whether certain assumptions appear reasonable, this book covers such topics as the problem of collinearity in multiple regression, non-normality of errors, and discrete data.
Author : A. Arjun K. Gupta
Publisher : VSP
ISBN 13 : 9789067642088
Total Pages : 402 pages
Book Rating : 4.6/5 (42 download)
Download or read book Multidimensional Statistical Analysis and Theory of Random Matrices written by A. Arjun K. Gupta and published by VSP. This book was released on 1996 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the papers from the Sixth Eugene Lukacs Symposium on ''Multidimensional Statistical Analysis and Random Matrices'', which was held at the Bowling Green State University, Ohio, USA, 29--30 March 1996. Multidimensional statistical analysis and random matrices have been the topics of great research. The papers presented in this volume discuss many varied aspects of this all-encompassing topic. In particular, topics covered include generalized statistical analysis, elliptically contoured distribution, covariance structure analysis, metric scaling, detection of outliers, density approximation, and circulant and band random matrices.
Author : Jürgen Groß
Publisher : Springer Science & Business Media
ISBN 13 : 9783540401780
Total Pages : 420 pages
Book Rating : 4.4/5 (17 download)
Download or read book Linear Regression written by Jürgen Groß and published by Springer Science & Business Media. This book was released on 2003-07-25 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Alvin C. Rencher
Publisher : John Wiley & Sons
ISBN 13 : 0470192607
Total Pages : 690 pages
Book Rating : 4.4/5 (71 download)
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.
Author : Joseph F. Hair
Publisher : Macmillan College
ISBN 13 :
Total Pages : 568 pages
Book Rating : 4.3/5 (91 download)
Download or read book Multivariate Data Analysis with Readings written by Joseph F. Hair and published by Macmillan College. This book was released on 1992 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Key terms what is muitivariate analysis, impact of computer revolution and multivariate analysis defined. Multiple regression analysis, multiple discriminant analysis and multivariate analysis of variance. Canonical correlation analysis, factor analysis, cluster analysis, muldidimensional scaling, canjoint analysis, structural equation modeling, a mathematical representation in LISREL, notation. Path analysis: a method of computing structural coefficients. Overall goodness-of Fit measures for structural equation modeling. Application of multivariate data analysis.
