Subset Selection In Regression

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Subset Selection In Regression

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Author : Alan Miller
language : en
Publisher: CRC Press
Release Date : 2002-04-15

Subset Selection In Regression written by Alan Miller and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-04-15 with Mathematics categories.

Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author ha

Subset Selection In Regression

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Author : Alan Miller
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2002-04-15

Subset Selection In Regression written by Alan Miller and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-04-15 with Mathematics categories.

Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition: A separate chapter on Bayesian methods Complete revision of the chapter on estimation A major example from the field of near infrared spectroscopy More emphasis on cross-validation Greater focus on bootstrapping Stochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presented More examples Subset Selection in Regression, Second Edition remains dedicated to the techniques for fitting and choosing models that are linear in their parameters and to understanding and correcting the bias introduced by selecting a model that fits only slightly better than others. The presentation is clear, concise, and belongs on the shelf of anyone researching, using, or teaching subset selecting techniques.

Feature Engineering And Selection

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Author : Max Kuhn
language : en
Publisher: CRC Press
Release Date : 2019-07-25

Feature Engineering And Selection written by Max Kuhn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Business & Economics categories.

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

Machine Learning Under A Modern Optimization Lens

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Author : Dimitris Bertsimas
language : en
Publisher:
Release Date : 2019

Machine Learning Under A Modern Optimization Lens written by Dimitris Bertsimas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Machine learning categories.

Optimal Subset Selection

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Author : David Boyce
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-08

Optimal Subset Selection written by David Boyce 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 2013-03-08 with Mathematics categories.

In the course of one's research, the expediency of meeting contractual and other externally imposed deadlines too often seems to take priority over what may be more significant research findings in the longer run. Such is the case with this volume which, despite our best intentions, has been put aside time and again since 1971 in favor of what seemed to be more urgent matters. Despite this delay, to our knowledge the principal research results and documentation presented here have not been superseded by other publications. The background of this endeavor may be of some historical interest, especially to those who agree that research is not a straightforward, mechanistic process whose outcome or even direction is known in ad vance. In the process of this brief recounting, we would like to express our gratitude to those individuals and organizations who facilitated and supported our efforts. We were introduced to the Beale, Kendall and Mann algorithm, the source of all our efforts, quite by chance. Professor Britton Harris suggested to me in April 1967 that I might like to attend a CEIR half-day seminar on optimal regression being given by Professor M. G. Kendall in Washington. D. C. I agreed that the topic seemed interesting and went along. Had it not been for Harris' suggestion and financial support, this work almost certainly would have never begun.

Developing A Protocol For Observational Comparative Effectiveness Research A User S Guide

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Author : Agency for Health Care Research and Quality (U.S.)
language : en
Publisher: Government Printing Office
Release Date : 2013-02-21

Developing A Protocol For Observational Comparative Effectiveness Research A User S Guide written by Agency for Health Care Research and Quality (U.S.) and has been published by Government Printing Office this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-21 with Medical categories.

This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Handbook Of Research On Machine And Deep Learning Applications For Cyber Security

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Author : Padmavathi Ganapathi
language : en
Publisher: IGI Global, Information Science Reference
Release Date : 2019-07-26

Handbook Of Research On Machine And Deep Learning Applications For Cyber Security written by Padmavathi Ganapathi and has been published by IGI Global, Information Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-26 with Computers categories.

"This book explores the use of machine learning and deep learning applications in the areas of cyber security and cyber-attack handling mechanisms"--

Multiple Regression And Beyond

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Author : Timothy Z. Keith
language : en
Publisher: Routledge
Release Date : 2019-01-14

Multiple Regression And Beyond written by Timothy Z. Keith and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-14 with Education categories.

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

Regression Models

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Author : Richard Breen
language : en
Publisher: SAGE
Release Date : 1996-01-09

Regression Models written by Richard Breen and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-01-09 with Mathematics categories.

What techniques can social scientists use when an outcome variable for a sample (for example, y) is not representative of the population for which generalized results are preferred? Author Richard Breen provides an introduction to regression models for such data, including censored, sample-selected, and truncated data. Regression Models begins with a discussion of the Tobit model and examines issues such as maximum likelihood estimation and the interpretation of parameters. The author next discusses the basic sample selection model and the truncated regression model. Elaborating on the modeling of censored and sample-selected data via maximum likelihood, he shows the close links between the models introduced and other regression models for non-continuous dependent variables, such as the ordered probit. Concluding with an exploration of some of the criticisms of these approaches and difficulties associated with them, this volume gives readers a guide to the practical utility of these models.

Forecasting Principles And Practice

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Author : Rob J Hyndman
language : en
Publisher: OTexts
Release Date : 2018-05-08

Forecasting Principles And Practice written by Rob J Hyndman and has been published by OTexts this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-08 with Business & Economics categories.

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.