Flexible Regression And Smoothing

Author : Mikis D. Stasinopoulos,Robert A. Rigby,Gillian Z. Heller,Vlasios Voudouris,Fernanda De Bastiani
Publisher : CRC Press
Page : 513 pages
File Size : 52,5 Mb
Release : 2017-04-21
Category : Mathematics
ISBN : 9781351980371

Get Book

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.

Author : Mikis D. Stasinopoulos,Robert A. Rigby,Gillian Z. Heller,Vlasios Voudouris,Fernanda De Bastiani
Publisher : CRC Press
Page : 549 pages
File Size : 45,6 Mb
Release : 2017-04-21
Category : Mathematics
ISBN : 9781351980388

Get Book

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.

Author : Yanan Fan,David Nott,Mike Smith,Jean-Luc Dortet-Bernadet
Publisher : Academic Press
Page : 302 pages
File Size : 50,5 Mb
Release : 2019-10-30
Category : Business & Economics
ISBN : 9780128158630

Get Book

Flexible Bayesian Regression Modelling by Yanan Fan,David Nott,Mike Smith,Jean-Luc Dortet-Bernadet Pdf

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine. Introduces powerful new nonparametric Bayesian regression techniques to classically trained practitioners Focuses on approaches offering both superior power and methodological flexibility Supplemented with instructive and relevant R programs within the text Covers linear regression, nonlinear regression and quantile regression techniques Provides diverse disciplinary case studies for correlation and optimization problems drawn from Bayesian analysis ‘in the wild’

Author : Robert A. Rigby,Mikis D. Stasinopoulos,Gillian Z. Heller,Fernanda De Bastiani
Publisher : CRC Press
Page : 421 pages
File Size : 52,9 Mb
Release : 2019-10-08
Category : Mathematics
ISBN : 9781000701180

Get Book

Distributions for Modeling Location, Scale, and Shape by Robert A. Rigby,Mikis D. Stasinopoulos,Gillian Z. Heller,Fernanda De Bastiani Pdf

This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the distribution of a response variable on explanatory variables. It will be especially useful to applied statisticians and data scientists in a wide range of application areas, and also to those interested in the theoretical properties of distributions. This book follows the earlier book ‘Flexible Regression and Smoothing: Using GAMLSS in R’, [Stasinopoulos et al., 2017], which focused on the GAMLSS model and software. GAMLSS (the Generalized Additive Model for Location, Scale, and Shape, [Rigby and Stasinopoulos, 2005]), is a regression framework in which the response variable can have any parametric distribution and all the distribution parameters can be modelled as linear or smooth functions of explanatory variables. The current book focuses on distributions and their application. Key features: Describes over 100 distributions, (implemented in the GAMLSS packages in R), including continuous, discrete and mixed distributions. Comprehensive summary tables of the properties of the distributions. Discusses properties of distributions, including skewness, kurtosis, robustness and an important classification of tail heaviness. Includes mixed distributions which are continuous distributions with additional specific values with point probabilities. Includes many real data examples, with R code integrated in the text for ease of understanding and replication. Supplemented by the gamlss website. This book will be useful for applied statisticians and data scientists in selecting a distribution for a univariate response variable and modelling its dependence on explanatory variables, and to those interested in the properties of distributions.

Author : Samprit Chatterjee,Jeffrey S. Simonoff
Publisher : John Wiley & Sons
Page : 384 pages
File Size : 42,5 Mb
Release : 2020-08-18
Category : Mathematics
ISBN : 9781119392378

Get Book

Handbook of Regression Analysis With Applications in R by Samprit Chatterjee,Jeffrey S. Simonoff Pdf

Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors’ thorough treatment of “classical” regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data. The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include: Regularization methods Smoothing methods Tree-based methods In the new edition of the Handbook, the data analyst’s toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website. Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.

Author : Michel Denuit,Donatien Hainaut,Julien Trufin
Publisher : Springer Nature
Page : 441 pages
File Size : 50,5 Mb
Release : 2019-09-03
Category : Business & Economics
ISBN : 9783030258207

Get Book

Effective Statistical Learning Methods for Actuaries I by Michel Denuit,Donatien Hainaut,Julien Trufin Pdf

This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

Author : Paul H.C. Eilers,Brian D. Marx
Publisher : Cambridge University Press
Page : 213 pages
File Size : 47,9 Mb
Release : 2021-03-18
Category : Computers
ISBN : 9781108482950

Get Book

Practical Smoothing by Paul H.C. Eilers,Brian D. Marx Pdf

This user guide presents a popular smoothing tool with practical applications in machine learning, engineering, and statistics.

Author : Luregn J. Schlapbach,Eric Giannoni
Publisher : Frontiers Media SA
Page : 261 pages
File Size : 54,9 Mb
Release : 2021-01-04
Category : Medical
ISBN : 9782889663231

Get Book

Sepsis in Neonates and Children by Luregn J. Schlapbach,Eric Giannoni Pdf

Author : Wolfgang Härdle
Publisher : Cambridge University Press
Page : 356 pages
File Size : 49,5 Mb
Release : 1990
Category : Business & Economics
ISBN : 0521429501

Get Book

Applied Nonparametric Regression by Wolfgang Härdle Pdf

This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.

Author : Adrian W. Bowman,Adelchi Azzalini
Publisher : OUP Oxford
Page : 205 pages
File Size : 40,8 Mb
Release : 1997-08-14
Category : Mathematics
ISBN : 9780191545696

Get Book

Applied Smoothing Techniques for Data Analysis by Adrian W. Bowman,Adelchi Azzalini Pdf

The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can be applied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawn from a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level, as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scripts are provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.

Author : Sucharita Ghosh
Publisher : John Wiley & Sons
Page : 272 pages
File Size : 42,5 Mb
Release : 2018-01-09
Category : Mathematics
ISBN : 9781118456057

Get Book

Kernel Smoothing by Sucharita Ghosh Pdf

Comprehensive theoretical overview of kernel smoothing methods with motivating examples Kernel smoothing is a flexible nonparametric curve estimation method that is applicable when parametric descriptions of the data are not sufficiently adequate. This book explores theory and methods of kernel smoothing in a variety of contexts, considering independent and correlated data e.g. with short-memory and long-memory correlations, as well as non-Gaussian data that are transformations of latent Gaussian processes. These types of data occur in many fields of research, e.g. the natural and the environmental sciences, and others. Nonparametric density estimation, nonparametric and semiparametric regression, trend and surface estimation in particular for time series and spatial data and other topics such as rapid change points, robustness etc. are introduced alongside a study of their theoretical properties and optimality issues, such as consistency and bandwidth selection. Addressing a variety of topics, Kernel Smoothing: Principles, Methods and Applications offers a user-friendly presentation of the mathematical content so that the reader can directly implement the formulas using any appropriate software. The overall aim of the book is to describe the methods and their theoretical backgrounds, while maintaining an analytically simple approach and including motivating examples—making it extremely useful in many sciences such as geophysics, climate research, forestry, ecology, and other natural and life sciences, as well as in finance, sociology, and engineering. A simple and analytical description of kernel smoothing methods in various contexts Presents the basics as well as new developments Includes simulated and real data examples Kernel Smoothing: Principles, Methods and Applications is a textbook for senior undergraduate and graduate students in statistics, as well as a reference book for applied statisticians and advanced researchers.

Author : Garcia Marquez, Fausto Pedro
Publisher : IGI Global
Page : 478 pages
File Size : 53,7 Mb
Release : 2019-10-04
Category : Computers
ISBN : 9781799801078

Get Book

Handbook of Research on Big Data Clustering and Machine Learning by Garcia Marquez, Fausto Pedro Pdf

As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. The Handbook of Research on Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

Author : Yuedong Wang
Publisher : CRC Press
Page : 380 pages
File Size : 53,5 Mb
Release : 2011-06-22
Category : Computers
ISBN : 9781420077568

Get Book

Smoothing Splines by Yuedong Wang Pdf

A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent years and has been widely used in many application areas, from medicine to economics. Smoothing Splines: Methods and Applications covers basic smoothing spline models, including polynomial, periodic, spherical, t

Author : Randall L. Eubank
Publisher : CRC Press
Page : 368 pages
File Size : 44,6 Mb
Release : 1999-02-09
Category : Mathematics
ISBN : 0824793374

Get Book

Nonparametric Regression and Spline Smoothing, Second Edition by Randall L. Eubank Pdf

Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.

Author : Annika Kangas,Matti Maltamo
Publisher : Springer Science & Business Media
Page : 368 pages
File Size : 53,8 Mb
Release : 2006-02-19
Category : Technology & Engineering
ISBN : 9781402043819

Get Book

Forest Inventory by Annika Kangas,Matti Maltamo Pdf

This book has been developed as a forest inventory textbook for students and could also serve as a handbook for practical foresters. We have set out to keep the mathematics in the book at a fairly non-technical level, and therefore, although we deal with many issues that include highly sophisticated methodology, we try to present first and foremost the ideas behind them. For foresters who need more details, references are given to more advanced scientific papers and books in the fields of statistics and biometrics. Forest inventory books deal mostly with sampling and measurement issues, as found here in section I, but since forest inventories in many countries involve much more than this, we have also included material on forestry applications. Most applications nowadays involve remote sensing technology of some sort, so that section II deals mostly with the use of remote sensing material for this purpose. Section III deals with national inventories carried out in different parts of world, and section IV is an attempt to outline some future possibilities of forest inventory methodologies. The editors, Annika Kangas Professor of Forest Mensuration and Management, Department of Forest Resource Management, University of Helsinki. Matti Maltamo Professor of Forest Mensuration, Faculty of Forestry, University of Joensuu. ACKNOWLEDGEMENTS