Semiparametric Regression With R

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Semiparametric Regression with R

Jaroslaw Harezlak,David Ruppert,Matt P. Wand
Semiparametric Regression with R Book PDF

Author : Jaroslaw Harezlak,David Ruppert,Matt P. Wand
Publisher : Springer
Page : 331 pages
File Size : 53,8 Mb
Release : 2018-12-12
Category : Mathematics
ISBN : 9781493988532

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Semiparametric Regression with R by Jaroslaw Harezlak,David Ruppert,Matt P. Wand Pdf

This easy-to-follow applied book on semiparametric regression methods using R is intended to close the gap between the available methodology and its use in practice. Semiparametric regression has a large literature but much of it is geared towards data analysts who have advanced knowledge of statistical methods. While R now has a great deal of semiparametric regression functionality, many of these developments have not trickled down to rank-and-file statistical analysts. The authors assemble a broad range of semiparametric regression R analyses and put them in a form that is useful for applied researchers. There are chapters devoted to penalized spines, generalized additive models, grouped data, bivariate extensions of penalized spines, and spatial semi-parametric regression models. Where feasible, the R code is provided in the text, however the book is also accompanied by an external website complete with datasets and R code. Because of its flexibility, semiparametric regression has proven to be of great value with many applications in fields as diverse as astronomy, biology, medicine, economics, and finance. This book is intended for applied statistical analysts who have some familiarity with R.

Semiparametric Regression

David Ruppert,M. P. Wand,R. J. Carroll
Semiparametric Regression Book PDF

Author : David Ruppert,M. P. Wand,R. J. Carroll
Publisher : Cambridge University Press
Page : 408 pages
File Size : 43,9 Mb
Release : 2003-07-14
Category : Mathematics
ISBN : 0521785162

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Semiparametric Regression by David Ruppert,M. P. Wand,R. J. Carroll Pdf

Even experts on semiparametric regression should find something new here.

Semiparametric Regression for the Social Sciences

Luke John Keele
Semiparametric Regression for the Social Sciences Book PDF

Author : Luke John Keele
Publisher : John Wiley & Sons
Page : 230 pages
File Size : 49,9 Mb
Release : 2008-04-15
Category : Mathematics
ISBN : 0470998121

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Semiparametric Regression for the Social Sciences by Luke John Keele Pdf

An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets. It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences. Each chapter includes exercises and examples and there is a supplementary website containing all the datasets used, as well as computer code, allowing readers to replicate every analysis reported in the book. Includes software for implementing the methods in S-Plus and R.

Multivariate Nonparametric Regression and Visualization

Jussi Sakari Klemelä
Multivariate Nonparametric Regression and Visualization Book PDF

Author : Jussi Sakari Klemelä
Publisher : John Wiley & Sons
Page : 317 pages
File Size : 46,6 Mb
Release : 2014-05-05
Category : Mathematics
ISBN : 9781118593509

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Multivariate Nonparametric Regression and Visualization by Jussi Sakari Klemelä Pdf

A modern approach to statistical learning and its applications through visualization methods With a unique and innovative presentation, Multivariate Nonparametric Regression and Visualization provides readers with the core statistical concepts to obtain complete and accurate predictions when given a set of data. Focusing on nonparametric methods to adapt to the multiple types of data generating mechanisms, the book begins with an overview of classification and regression. The book then introduces and examines various tested and proven visualization techniques for learning samples and functions. Multivariate Nonparametric Regression and Visualization identifies risk management, portfolio selection, and option pricing as the main areas in which statistical methods may be implemented in quantitative finance. The book provides coverage of key statistical areas including linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods. Exploring the additional applications of nonparametric and semiparametric methods, Multivariate Nonparametric Regression and Visualization features: An extensive appendix with R-package training material to encourage duplication and modification of the presented computations and research Multiple examples to demonstrate the applications in the field of finance Sections with formal definitions of the various applied methods for readers to utilize throughout the book Multivariate Nonparametric Regression and Visualization is an ideal textbook for upper-undergraduate and graduate-level courses on nonparametric function estimation, advanced topics in statistics, and quantitative finance. The book is also an excellent reference for practitioners who apply statistical methods in quantitative finance.

Semiparametric Regression for the Applied Econometrician

Adonis Yatchew
Semiparametric Regression for the Applied Econometrician Book PDF

Author : Adonis Yatchew
Publisher : Cambridge University Press
Page : 238 pages
File Size : 54,5 Mb
Release : 2003-06-02
Category : Business & Economics
ISBN : 0521012260

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Semiparametric Regression for the Applied Econometrician by Adonis Yatchew Pdf

This book provides an accessible collection of techniques for analyzing nonparametric and semiparametric regression models. Worked examples include estimation of Engel curves and equivalence scales, scale economies, semiparametric Cobb-Douglas, translog and CES cost functions, household gasoline consumption, hedonic housing prices, option prices and state price density estimation. The book should be of interest to a broad range of economists including those working in industrial organization, labor, development, urban, energy and financial economics. A variety of testing procedures are covered including simple goodness of fit tests and residual regression tests. These procedures can be used to test hypotheses such as parametric and semiparametric specifications, significance, monotonicity and additive separability. Other topics include endogeneity of parametric and nonparametric effects, as well as heteroskedasticity and autocorrelation in the residuals. Bootstrap procedures are provided.

Nonparametric Regression Analysis of Longitudinal Data

Hans-Georg Müller
Nonparametric Regression Analysis of Longitudinal Data Book PDF

Author : Hans-Georg Müller
Publisher : Springer Science & Business Media
Page : 208 pages
File Size : 41,5 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461239260

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Nonparametric Regression Analysis of Longitudinal Data by Hans-Georg Müller Pdf

This monograph reviews some of the work that has been done for longitudi nal data in the rapidly expanding field of nonparametric regression. The aim is to give the reader an impression of the basic mathematical tools that have been applied, and also to provide intuition about the methods and applications. Applications to the analysis of longitudinal studies are emphasized to encourage the non-specialist and applied statistician to try these methods out. To facilitate this, FORTRAN programs are provided which carry out some of the procedures described in the text. The emphasis of most research work so far has been on the theoretical aspects of nonparametric regression. It is my hope that these techniques will gain a firm place in the repertoire of applied statisticians who realize the large potential for convincing applications and the need to use these techniques concurrently with parametric regression. This text evolved during a set of lectures given by the author at the Division of Statistics at the University of California, Davis in Fall 1986 and is based on the author's Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpublished work. Completeness is not attempted, neither in the text nor in the references. The following persons have been particularly generous in sharing research or giving advice: Th. Gasser, P. Ihm, Y. P. Mack, V. Mammi tzsch, G . G. Roussas, U. Stadtmuller, W. Stute and R.

Nonparametric and Semiparametric Models

Wolfgang Karl Härdle,Marlene Müller,Stefan Sperlich,Axel Werwatz
Nonparametric and Semiparametric Models Book PDF

Author : Wolfgang Karl Härdle,Marlene Müller,Stefan Sperlich,Axel Werwatz
Publisher : Springer Science & Business Media
Page : 317 pages
File Size : 54,6 Mb
Release : 2012-08-27
Category : Mathematics
ISBN : 9783642171468

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Nonparametric and Semiparametric Models by Wolfgang Karl Härdle,Marlene Müller,Stefan Sperlich,Axel Werwatz Pdf

The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.

Flexible Regression and Smoothing

Mikis D. Stasinopoulos,Robert A. Rigby,Gillian Z. Heller,Vlasios Voudouris,Fernanda De Bastiani
Flexible Regression and Smoothing Book PDF

Author : Mikis D. Stasinopoulos,Robert A. Rigby,Gillian Z. Heller,Vlasios Voudouris,Fernanda De Bastiani
Publisher : CRC Press
Page : 513 pages
File Size : 41,9 Mb
Release : 2017-04-21
Category : Mathematics
ISBN : 9781351980371

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Flexible Regression and Smoothing by Mikis D. Stasinopoulos,Robert A. Rigby,Gillian Z. Heller,Vlasios Voudouris,Fernanda De Bastiani Pdf

This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. In particular, the GAMLSS statistical framework enables flexible regression and smoothing models to be fitted to the data. The GAMLSS model assumes that the response variable has any parametric (continuous, discrete or mixed) distribution which might be heavy- or light-tailed, and positively or negatively skewed. In addition, all the parameters of the distribution (location, scale, shape) can be modelled as linear or smooth functions of explanatory variables. Key Features: Provides a broad overview of flexible regression and smoothing techniques to learn from data whilst also focusing on the practical application of methodology using GAMLSS software in R. Includes a comprehensive collection of real data examples, which reflect the range of problems addressed by GAMLSS models and provide a practical illustration of the process of using flexible GAMLSS models for statistical learning. R code integrated into the text for ease of understanding and replication. Supplemented by a website with code, data and extra materials. This book aims to help readers understand how to learn from data encountered in many fields. It will be useful for practitioners and researchers who wish to understand and use the GAMLSS models to learn from data and also for students who wish to learn GAMLSS through practical examples.

Introduction to Empirical Processes and Semiparametric Inference

Michael R. Kosorok
Introduction to Empirical Processes and Semiparametric Inference Book PDF

Author : Michael R. Kosorok
Publisher : Springer Science & Business Media
Page : 482 pages
File Size : 43,7 Mb
Release : 2007-12-29
Category : Mathematics
ISBN : 9780387749785

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Introduction to Empirical Processes and Semiparametric Inference by Michael R. Kosorok Pdf

Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.

Mixed Effects Models for Complex Data

Lang Wu
Mixed Effects Models for Complex Data Book PDF

Author : Lang Wu
Publisher : CRC Press
Page : 431 pages
File Size : 42,6 Mb
Release : 2009-11-11
Category : Mathematics
ISBN : 1420074083

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Mixed Effects Models for Complex Data by Lang Wu Pdf

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Semiparametric Modeling of Implied Volatility

Matthias R. Fengler
Semiparametric Modeling of Implied Volatility Book PDF

Author : Matthias R. Fengler
Publisher : Springer Science & Business Media
Page : 232 pages
File Size : 42,5 Mb
Release : 2005-12-19
Category : Business & Economics
ISBN : 9783540305910

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Semiparametric Modeling of Implied Volatility by Matthias R. Fengler Pdf

This book offers recent advances in the theory of implied volatility and refined semiparametric estimation strategies and dimension reduction methods for functional surfaces. The first part is devoted to smile-consistent pricing approaches. The second part covers estimation techniques that are natural candidates to meet the challenges in implied volatility surfaces. Empirical investigations, simulations, and pictures illustrate the concepts.

Nonlinear Regression with R

Christian Ritz,Jens Carl Streibig
Nonlinear Regression with R Book PDF

Author : Christian Ritz,Jens Carl Streibig
Publisher : Springer Science & Business Media
Page : 151 pages
File Size : 44,9 Mb
Release : 2008-12-11
Category : Mathematics
ISBN : 9780387096162

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Nonlinear Regression with R by Christian Ritz,Jens Carl Streibig Pdf

- Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.

Statistics and Data Analysis for Financial Engineering

David Ruppert,David S. Matteson
Statistics and Data Analysis for Financial Engineering Book PDF

Author : David Ruppert,David S. Matteson
Publisher : Springer
Page : 736 pages
File Size : 44,8 Mb
Release : 2015-04-21
Category : Business & Economics
ISBN : 9781493926145

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Statistics and Data Analysis for Financial Engineering by David Ruppert,David S. Matteson Pdf

The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.

Statistical Analysis of Panel Count Data

Jianguo Sun,Xingqiu Zhao
Statistical Analysis of Panel Count Data Book PDF

Author : Jianguo Sun,Xingqiu Zhao
Publisher : Springer Science & Business Media
Page : 283 pages
File Size : 51,8 Mb
Release : 2013-10-09
Category : Medical
ISBN : 9781461487159

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Statistical Analysis of Panel Count Data by Jianguo Sun,Xingqiu Zhao Pdf

Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.

Applied Econometrics with R

Christian Kleiber,Achim Zeileis
Applied Econometrics with R Book PDF

Author : Christian Kleiber,Achim Zeileis
Publisher : Springer Science & Business Media
Page : 229 pages
File Size : 49,8 Mb
Release : 2008-12-10
Category : Business & Economics
ISBN : 9780387773186

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Applied Econometrics with R by Christian Kleiber,Achim Zeileis Pdf

R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.