Semiparametric Regression With R PDF Download
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Author | : Jaroslaw Harezlak |
Publisher | : Springer |
Total Pages | : 331 |
Release | : 2018-12-12 |
Genre | : Mathematics |
ISBN | : 1493988530 |
Download Semiparametric Regression with R Book in PDF, ePub and Kindle
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.
Author | : David Ruppert |
Publisher | : Cambridge University Press |
Total Pages | : 408 |
Release | : 2003-07-14 |
Genre | : Mathematics |
ISBN | : 9780521785167 |
Download Semiparametric Regression Book in PDF, ePub and Kindle
Even experts on semiparametric regression should find something new here.
Author | : Luke John Keele |
Publisher | : John Wiley & Sons |
Total Pages | : 230 |
Release | : 2008-04-15 |
Genre | : Mathematics |
ISBN | : 9780470998120 |
Download Semiparametric Regression for the Social Sciences Book in PDF, ePub and Kindle
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.
Author | : Jussi Sakari Klemelä |
Publisher | : John Wiley & Sons |
Total Pages | : 317 |
Release | : 2014-05-05 |
Genre | : Mathematics |
ISBN | : 1118593502 |
Download Multivariate Nonparametric Regression and Visualization Book in PDF, ePub and Kindle
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.
Author | : David Ruppert |
Publisher | : Springer |
Total Pages | : 736 |
Release | : 2015-04-21 |
Genre | : Business & Economics |
ISBN | : 1493926144 |
Download Statistics and Data Analysis for Financial Engineering Book in PDF, ePub and Kindle
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.
Author | : Wolfgang Karl Härdle |
Publisher | : Springer Science & Business Media |
Total Pages | : 317 |
Release | : 2012-08-27 |
Genre | : Mathematics |
ISBN | : 364217146X |
Download Nonparametric and Semiparametric Models Book in PDF, ePub and Kindle
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.
Author | : Michael R. Kosorok |
Publisher | : Springer Science & Business Media |
Total Pages | : 482 |
Release | : 2007-12-29 |
Genre | : Mathematics |
ISBN | : 0387749780 |
Download Introduction to Empirical Processes and Semiparametric Inference Book in PDF, ePub and Kindle
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.
Author | : Mikis D. Stasinopoulos |
Publisher | : CRC Press |
Total Pages | : 641 |
Release | : 2017-04-21 |
Genre | : Mathematics |
ISBN | : 1351980378 |
Download Flexible Regression and Smoothing Book in PDF, ePub and Kindle
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 | : Christian Kleiber |
Publisher | : Springer Science & Business Media |
Total Pages | : 229 |
Release | : 2008-12-10 |
Genre | : Business & Economics |
ISBN | : 0387773185 |
Download Applied Econometrics with R Book in PDF, ePub and Kindle
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.
Author | : Matthias R. Fengler |
Publisher | : Springer Science & Business Media |
Total Pages | : 232 |
Release | : 2005-12-19 |
Genre | : Business & Economics |
ISBN | : 3540305912 |
Download Semiparametric Modeling of Implied Volatility Book in PDF, ePub and Kindle
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.