Statistical Tools for Nonlinear Regression

Author: Sylvie Huet
Edition: 2nd
Publisher: Springer
Binding: Hardcover
ISBN: 0387400818

Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples (Springer Series in Statistics)

Statistical Tools for Nonlinear Regression presents methods for analyzing data. Medical books Statistical Tools for Nonlinear Regression. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap Medical books Statistical Tools For Nonlinear Regression By Sylvie Huet Paperback Book (en. Store Search search Title, ISBN and Author Statistical Tools for Nonlinear Regression by Sylvie Huet, Anne Bouvier, Marie-anne Poursat, Emmanuel Jolivet Estimated delivery 3-12 business days Format Paperback Condition Brand New Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the

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Store Search search Title, ISBN and Author Statistical Tools for Nonlinear Regression by Sylvie Huet, Anne Bouvier, Marie-anne Poursat, Emmanuel Jolivet Estimated delivery 3-12 business days Format Paperback Condition Brand New Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the

Categories: Parameter estimation, Regression analysis. Contributors: Sylvie Huet - Author. Format: Hardcover

"Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.File Size: 3078 KBPrint Length: 250 pagesPage Numbers Source ISBN: 0387400818Pub

"Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.File Size: 3078 KBPrint Length: 250 pagesPage Numbers Source ISBN: 0387400818Pub

Medical Book Statistical Tools for Nonlinear Regression

It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.

Regression Modeling Strategies Epub

Author: Frank E. Harrell
Edition: Corrected
Publisher: Springer
Binding: Hardcover
ISBN: 0387952322

Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (Springer Series in Statistics)

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Medical books Regression Modeling Strategies. Medical books Regression Modeling Strategies. Regression Modeling Strategies

Download link for Regression Modeling Strategies : With Applications t..., 9781441929181

Regression Modeling Strategies

Store Search search Title, ISBN and Author Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis by Frank E. Harrell, FE Harrell Estimated delivery 3-12 business days Format Hardcover Condition Brand New There are many books that are excellent sources of knowledge about individual stastical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatf

author frank e harrell jr format hardback language english publication year 31 12 2001 series springer series in statistics subject mathematics sciences subject 2 mathematics title regression modeling strategies with applications to linear models logistic regression and survival analysis author frank e jr harrell publisher springer verlag publication date aug 01 2001 pages 568 binding hardcover edition 1 st ed 2001 corr 2 nd printing dimensions 7 00 wx 9 75 hx 1 25 d isbn 0387952322 subject ma

There are many books that are excellent sources of knowledge about individual stastical tools survival models general linear models etc but the art of data analysis is about choosing and using multiple tools In the words of Chatfield students typically know the technical details of regressin for example but not necessarily when and how to apply it This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies Whether analyzing risk factors adjusting for biases in observational studies or developing predictive models the

Medical Book Regression Modeling Strategies

Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

Modern Regression Methods Epub

Author: Thomas P. Ryan
Edition: 2
Publisher: Wiley-Interscience
Binding: Hardcover
ISBN: 0470081864

Modern Regression Methods (Wiley Series in Probability and Statistics)

"Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one. Medical books Modern Regression Methods . (The American Statistician)

"The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems." (Computing Reviews)

Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003 Medical books Modern Regression Methods By Thomas P. Ryan Hardcover Book. Store Search search Title, ISBN and Author Modern Regression Methods by Thomas P. Ryan Estimated delivery 3-12 business days Format Hardcover Condition Brand New Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one. ( The American Statistician ) The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite.

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Store Search search Title, ISBN and Author Modern Regression Methods by Thomas P. Ryan Estimated delivery 3-12 business days Format Hardcover Condition Brand New Over the years, I have had the opportunity to teach several regression courses, and I cannot think of a better undergraduate text than this one. ( The American Statistician ) The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite.

author thomas p ryan format hardback language english publication year 25 11 2008 series wiley series in probability and statistics subject mathematics sciences subject 2 mathematics title modern regression methods author ryan thomas p publisher wiley interscience publication date nov 24 2008 pages 642 binding hardcover edition 2 nd dimensions 6 25 wx 9 50 hx 1 50 d isbn 0470081864 subject mathematics probability statistics regression analysis description in this second edition ryan author edi

Powered by Frooition Pro Click here to view full size. Full Size Image Click to close full size. Modern Regression Methods [9780470081860] - Book NEW Author(s): Thomas P. Ryan Format: Hardcover # Pages: 642 ISBN-13: 9780470081860 Published: 11/10/2008 Language: English Weight: 2.32 pounds Praise for the First Edition ". . . provides a very thorough treatment of regression approaches, including techniques not covered in many books . . ." -Technometrics ". . . an excellent book . . . worthwhile f

Regression is a popular research area, and regression analysis is an ever-changing collection of techniques. This book offers a synthesis of state-of-the-art regression methodology. Featuring a strong data analysis orientation, it presents regression diagnostics in a uniquely comprehensive manner. Each chapter contains a brief section for SAS, Minitab, SPSS, BMDP, and Systat users, though particular software packages are not emphasized overall. Enhancements to the second edition include a unification and expansion of subject areas addressed in the first edition, as well as an increased emphasi

Medical Book Modern Regression Methods

(The American Statistician)

"The book is well written and has many exercises. It can serve as a very good textbook for scientists and engineers, with only basic statistics as a prerequisite. I also highly recommend it to practitioners who want to solve real-life prediction problems." (Computing Reviews)

Modern Regression Methods, Second Edition maintains the accessible organization, breadth of coverage, and cutting-edge appeal that earned its predecessor the title of being one of the top five books for statisticians by an Amstat News book editor in 2003. This new edition has been updated and enhanced to include all-new information on the latest advances and research in the evolving field of regression analysis.

The book provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and ridge regression. Unifying key concepts and procedures, this new edition emphasizes applications to provide a more hands-on and comprehensive understanding of regression diagnostics. New features of the Second Edition include:

• A revised chapter on logistic regression, including improved methods of parameter estimation

• A new chapter focusing on additional topics of study in regression, including quantile regression, semiparametric regression, and Poisson regression

• A wealth of new and updated exercises with worked solutions

• An extensive FTP site complete with Minitab macros, which allow the reader to compute analyses, and specialized procedures

• Updated references at the end of each chapter that direct the reader to the appropriate resources for further study

An accessible guide to state-of-the-art regression techniques, Modern Regression Methods, Second Edition is an excellent book for courses in regression analysis at the upper-undergraduate and graduate levels. It is also a valuable reference for practicing statisticians, engineers, and physical scientists.

Regression with Linear Predictors

Author: Per Kragh Andersen
Edition: 2010
Publisher: Springer
Binding: Hardcover
ISBN: 1441971696

Regression with Linear Predictors (Statistics for Biology and Health)

This text provides, in a non-technical language, a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. Medical books Regression with Linear Predictors . This is done by focusing on the many common aspects of these models, in particular the linear predictor, which combines the effects of all explanatory variables into a function which is linear in the unknown parameters. Specification and interpretation of various choices of parametrization of the effects of the covariates (categorical as well as quantitative) and interaction among these are elaborated upon. The merits and drawbacks of different link functions relating the linear predictor to the outcome are discussed with an emphasis on interpretational issues, and the fact that different research questions arise from adding or deleting covariates from the model is emphasized in both theory and practice. Regression models with a linear predictor are commonly used in fields such as clinical medicine, epidemiology, and public health, and the book, including its many worked examples, builds on the authors' more than thirty years of experience as teachers, researchers and consultants at a biostatistical department Medical books Regression with Linear Predictors. This text provides, in a non-technical language, a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. This is done by focusing on the many common aspects of these models, in particular the linear predictor, which combines the effects of all explanatory variables into a function which is linear in the unknown parameters. Specification and interpretation of various choices of parametrization of the effects of the covariates (categorical as well as quantitative) and interaction among these are elaborated upon. The merits and drawbacks of different link functions relating the linear predictor to the outcome are discussed with an emphasis on interpretational issues, and the fact that different research questions arise from adding or deleting covariates from the model is emphasized in both theory and practice.
Regression models with a linear predictor are commonly used in fields such as clinical medicine, epidemiology, and public health, and the book, including its many worked examples, builds on the authors' more than thirty years of experience as teachers, researchers and consultants at a biostatistical department. The book is well-suited for readers without a solid mathematical background and is accompanied by Web pages documenting in R, SAS, and STATA, the analyses presented throughout the text.

Download link for Regression with Linear Predictors

This text provides, in a non-technical language, a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. This is done by focusing on the many common aspects of these models, in particular the linear predictor, which combines the effects of all explanatory variables into a function which is linear in the unknown parameters. Specification and interpretation of various choices of parametrization of the effects of the covariates (categorical as well as quantitative) and interaction among these are elaborated upon. Th

Store Search search Title, ISBN and Author Regression with Linear Predictors by Per Kragh Andersen Estimated delivery 3-12 business days Format Hardcover Condition Brand New This text provides a unified treatment of regression models for different outcome types, such as linear regression, logistic regression, and Cox regression. It includes many worked examples. Publisher Description This text provides, in a non-technical language, a unified treatment of regression models for different outcome

author lene theil skovgaard author per kragh andersen format hardback language english publication year 01 07 2010 series statistics for biology and health subject mathematics sciences subject 2 science mathematics textbooks study guides title regression with linear predictors author per kragh andersen publisher springer verlag gmbh publication date jul 23 2010 pages 494 binding hardcover dimensions 6 50 wx 9 75 hx 1 50 d isbn 1441971696 subject mathematics probability statistics general descr

Electronics Cameras Computers Software Housewares Sports DVDs Music Books Games Toys in titles descriptions Company Info |Checkout Info |Shipping Info |Return Policy |FAQ's Add us as a favorite seller By continuing with your purchase using the eBay Buy It Now button, you agree to the Buy Terms of Use at http://stores.ebay.com/Buys-Internet-Superstore/Terms.html . Regression with Linear Predictors - Andersen, Per Kragh/ Skovgaard, Lene Theil THIS IS A BRAND NEW UNOPENED ITEM. Description This te

Medical Book Regression with Linear Predictors

This is done by focusing on the many common aspects of these models, in particular the linear predictor, which combines the effects of all explanatory variables into a function which is linear in the unknown parameters. Specification and interpretation of various choices of parametrization of the effects of the covariates (categorical as well as quantitative) and interaction among these are elaborated upon. The merits and drawbacks of different link functions relating the linear predictor to the outcome are discussed with an emphasis on interpretational issues, and the fact that different research questions arise from adding or deleting covariates from the model is emphasized in both theory and practice. Regression models with a linear predictor are commonly used in fields such as clinical medicine, epidemiology, and public health, and the book, including its many worked examples, builds on the authors' more than thirty years of experience as teachers, researchers and consultants at a biostatistical department. The book is well-suited for readers without a solid mathematical background and is accompanied by Web pages documenting in R, SAS, and STATA, the analyses presented throughout the text.

Regression Models as a Tool in Medical Research

Author: Werner Vach
Edition: 1
Publisher: Chapman and Hall/CRC
Binding: Hardcover
ISBN: 1466517484

Regression Models as a Tool in Medical Research

While regression models have become standard tools in medical research, understanding how to properly apply the models and interpret the results is often challenging for beginners. Medical books Regression Models as a Tool in Medical Research. Regression Models as a Tool in Medical Research presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The text emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.

After reviewing popular models and basic methods, the book focuses on advanced topics and techniques. It considers the comparison of regression coefficients, the selection of covariates, the modeling of nonlinear and nonadditive effects, and the analysis of clustered and longitudinal data, highlighting the impact of selection mechanisms, measurement error, and incomplete covariate data Medical books Regression Models As A Tool In Medical Research By Werner Vach Hardcover Boo. Store Search search Title, ISBN and Author Regression Models as a Tool in Medical Research by Werner Vach Estimated delivery 4-14 business days Format Hardcover Condition Brand New Details ISBN 1466517484 ISBN-13 9781466517486 Title Regression Models as a Tool in Medical Research Author Werner Vach Format Hardcover Year 2012 Pages 473 Publisher CRC Press Dimensions 6.2 in. x 1.3 in. x 9.2 in. About Us Grand Eagle Retail is the ideal place for all your reading and entertainment needs! With fast

Download link for Regression Models As A Tool In Medical Research Vach, Werner (author)

Store Search search Title, ISBN and Author Regression Models as a Tool in Medical Research by Werner Vach Estimated delivery 4-14 business days Format Hardcover Condition Brand New Details ISBN 1466517484 ISBN-13 9781466517486 Title Regression Models as a Tool in Medical Research Author Werner Vach Format Hardcover Year 2012 Pages 473 Publisher CRC Press Dimensions 6.2 in. x 1.3 in. x 9.2 in. About Us Grand Eagle Retail is the ideal place for all your reading and entertainment needs! With fast

Regression Models As A Tool In Medical Research

While Regression models have become standard tools in Medical research, understanding how to propTHE BASICSWhy Use Regression Models?Why using simple regression models?Why using multiple regression models?Some basic notationAn Introductory ExampleA single line modelFi

"Preface Regression models are today a standard tool in medical research. However, their application and the interpretation of their results is often challenging. It is the purpose of this book to introduce medical researchers to basic concepts and other important aspects of regression models, which have to be taken into account in their application to ensure an optimal use and in the interpretation of their results to support the adequate addressing of subject matter questions. Three regression models are in particular popular in medical research: The classical regression model for a continuo

Medical Book Regression Models as a Tool in Medical Research

Regression Models as a Tool in Medical Research presents the fundamental concepts and important aspects of regression models most commonly used in medical research, including the classical regression model for continuous outcomes, the logistic regression model for binary outcomes, and the Cox proportional hazards model for survival data. The text emphasizes adequate use, correct interpretation of results, appropriate presentation of results, and avoidance of potential pitfalls.

After reviewing popular models and basic methods, the book focuses on advanced topics and techniques. It considers the comparison of regression coefficients, the selection of covariates, the modeling of nonlinear and nonadditive effects, and the analysis of clustered and longitudinal data, highlighting the impact of selection mechanisms, measurement error, and incomplete covariate data. The text then covers the use of regression models to construct risk scores and predictors. It also gives an overview of more specific regression models and their applications as well as alternatives to regression modeling. The mathematical details underlying the estimation and inference techniques are provided in the appendices.

Analysing cycles in biology & medicine-a practical introduction to circular variables & periodic regression

Author: Kim N.I. Bell
Edition:
Publisher: Razorbill Press
Binding: Paperback
ISBN: 0973620927

Analysing cycles in biology & medicine-a practical introduction to circular variables & periodic regression

This book makes periodic regression readily accessible. Medical books Analysing cycles in biology & medicine-a practical introduction to circular variables & periodic regression.  It provides worked-out examples, and text that describes why each step is taken (and what steps to not take).  Periodic regression is suited to situations where a Y variable is measured over (around) a cycle, e.g. "air temperature over the course of a day, or year";  this is sometimes called the "cylindrical" situation---think of a 24h clock, lying flat, and temperatures marked on bars at times around its circumference Medical books Analysing Cycles in Biology & Medicine-A Practical Introduction to Circular Variables & Periodic Regression. Softcover book, 184 pages. Published by Razorbill Press (2008)

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Softcover book, 184 pages. Published by Razorbill Press (2008)

Contributors: Kim NI Bell - Author. Format: Paperback

Contributors: Kim NI Bell - Author. Format: Paperback

Razorbill Press 9780973620924 Analysing Cycles in Biology Medicine-A Practical Introduction to Circular Variables Periodic Regression Description TITLE: ANALYSING CYCLES-in biology medicine. A practical introduction to circular variables and periodic regression. With stats refresher. MOTIVATION: Cycles surround us. Indeed, they are the essence of life, and critically important in biology and medicine. We need to know how to analyse and understand them. But, too often, researchers are given poor

Medical Book Analysing cycles in biology & medicine-a practical introduction to circular variables & periodic regression

 It provides worked-out examples, and text that describes why each step is taken (and what steps to not take).  Periodic regression is suited to situations where a Y variable is measured over (around) a cycle, e.g. "air temperature over the course of a day, or year";  this is sometimes called the "cylindrical" situation---think of a 24h clock, lying flat, and temperatures marked on bars at times around its circumference. 

   The uses or periodic regression are not restricted to only those situations where one had a direct interest in the cycles.  Even if the interest is the effect of A and B on Y,  if a third variable C also affects Y then any analysis obtains improved accuracy for A and B when C is included.  Likewise, cycles can affect many measures, so their inclusion improves the analysis for the effects of the variables of main interest.  This would apply to many examples of ecological, biological, and medical research.

   Written in a welcoming style, written to be accessible --- for instance Sec. 11.1.2 "The very basics (stats in the wild west)" --- the book anticipates readers ranging from apprehensive to advanced. Most tricky concepts are explained more than one way.  It is copiously illustrated with conceptual diagrams and worked-out examples.  

   If you are an advanced reader you may wish to go straight to Ch. 6, the chapter on periodic regression itself, referring to the cross-referenced sections as needed.

   Resource chapters provide the basic geometry, key tools for circular stats, the handling of dates and natural cycles, and a simple notation system to make formulas and calculations more readable.  There is an index, a glossary, and a Day-of-Year table.  

   Some special tools and topics are addressed.  Sec. 6.4.5 shows how to calculate the joint significance of one or more variables in a regression; this is needed because each cycle will always be expressed by two variables.  Sec. 6.4.6 addresses some cautions and uses simulation to illuminate the issue of non-orthogonality in data with uneven spacing or missing or replicated values, as is liable to happen in field work or any system where logistics interfere with the plan; the simulations show that the effect on inference is, at least, surprisingly small.  Sec. 10.2.1. provides the text for some core macros that can simplify and add reliability to data conversion and calculation (e.g.) of the phase angle implied by any pair of sine and cosine.  And if you think sines and cosines are material for headaches, Figure 3-1 is better than an aspirin.  

   Even where the topics are somewhat advanced, the book attempts to maintain accessibility.  It is written to help people enjoy statistics, and use and judge them well.

Introduction to Nonparametric Regression

Author: K. Takezawa
Edition: 1
Publisher: Wiley-Interscience
Binding: Hardcover
ISBN: 0471745839

Introduction to Nonparametric Regression (Wiley Series in Probability and Statistics)

An easy-to-grasp introduction to nonparametric regression

This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Medical books Introduction to Nonparametric Regression . Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features:
* Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods
* Statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions
* Mathematical equations that are accompanied by a clear explanation of how the equation was derived


The first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts.

With a learning-by-doing approach, each topical chapter includes thorough S-Plus? examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects Medical books Introduction to Nonparametric Regression by Takezawa, Kunio. Introduction to Nonparametric Regression by Takezawa, Kunio [Hardcover]

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Introduction to Nonparametric Regression by Takezawa, Kunio [Hardcover]

An easy-to-grasp introduction to nonparametric regression This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features: Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods Statistical techniques accompanied by clear numerical examples that further assist reade

An easy-to-grasp introduction to nonparametric regression
This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features:
Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods
Statistical techniques accompanied by clear numerical examples that further assist reade

Medical Book Introduction to Nonparametric Regression

Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features:
* Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods
* Statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions
* Mathematical equations that are accompanied by a clear explanation of how the equation was derived


The first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts.

With a learning-by-doing approach, each topical chapter includes thorough S-Plus? examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects. In addition, each chapter ends with a set of problems that test readers' grasp of key concepts and techniques and also prepares them for more advanced topics.

This book is recommended as a textbook for undergraduate and graduate courses in nonparametric regression. Only a basic knowledge of linear algebra and statistics is required. In addition, this is an excellent resource for researchers and engineers in such fields as pattern recognition, speech understanding, and data mining. Practitioners who rely on nonparametric regression for analyzing data in the physical, biological, and social sciences, as well as in finance and economics, will find this an unparalleled resource.

Adaptive Regression

Author: Yadolah Dodge
Edition: 2000
Publisher: Springer
Binding: Hardcover
ISBN: 038798965X

Adaptive Regression

While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. Medical books Adaptive Regression. Medical books Adaptive Regression .... Adaptive Regression SPRIV 9781461264644 09781461264644

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Adaptive Regression SPRIV 9781461264644 09781461264644

Electronics Cameras Computers Software Housewares Sports DVDs Music Books Games Toys in titles descriptions Company Info |Checkout Info |Shipping Info |Return Policy |FAQ's Add us as a favorite seller By continuing with your purchase using the eBay Buy It Now button, you agree to the Buy Terms of Use at http://stores.ebay.com/Buys-Internet-Superstore/Terms.html . Adaptive Regression - Dodge, Yadolah/ Jureckova, Jana THIS IS A BRAND NEW UNOPENED ITEM. Buy SKU: 248300843 If you want additional in

Adaptive Regression (2000 Edition) by Dodge, Yadolah/ Dodge, Y./ Jureckova, J. [Hardcover]

Categories: Regression analysis. Contributors: Yadolah Dodge - Author. Format: Hardcover

Medical Book Adaptive Regression

• ISBN13: 9780387989655
• Condition: New
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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.