Semiparametric Regression with R
Title | Semiparametric Regression with R PDF eBook |
Author | Jaroslaw Harezlak |
Publisher | Springer |
Pages | 331 |
Release | 2018-12-12 |
Genre | Mathematics |
ISBN | 1493988530 |
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
Title | Semiparametric Regression PDF eBook |
Author | David Ruppert |
Publisher | Cambridge University Press |
Pages | 408 |
Release | 2003-07-14 |
Genre | Mathematics |
ISBN | 9780521785167 |
Even experts on semiparametric regression should find something new here.
Linear Models with R
Title | Linear Models with R PDF eBook |
Author | Julian J. Faraway |
Publisher | CRC Press |
Pages | 284 |
Release | 2016-04-19 |
Genre | Mathematics |
ISBN | 1439887349 |
A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models
Semiparametric Regression for the Social Sciences
Title | Semiparametric Regression for the Social Sciences PDF eBook |
Author | Luke John Keele |
Publisher | John Wiley & Sons |
Pages | 230 |
Release | 2008-04-15 |
Genre | Mathematics |
ISBN | 9780470998120 |
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.
Nonlinear Regression with R
Title | Nonlinear Regression with R PDF eBook |
Author | Christian Ritz |
Publisher | Springer Science & Business Media |
Pages | 151 |
Release | 2008-12-11 |
Genre | Mathematics |
ISBN | 0387096167 |
- Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.
Semiparametric Modeling of Implied Volatility
Title | Semiparametric Modeling of Implied Volatility PDF eBook |
Author | Matthias R. Fengler |
Publisher | Springer Science & Business Media |
Pages | 232 |
Release | 2005-12-19 |
Genre | Business & Economics |
ISBN | 3540305912 |
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.
Nonparametric and Semiparametric Models
Title | Nonparametric and Semiparametric Models PDF eBook |
Author | Wolfgang Karl Härdle |
Publisher | Springer Science & Business Media |
Pages | 317 |
Release | 2012-08-27 |
Genre | Mathematics |
ISBN | 364217146X |
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.