Theory and Applications of Recent Robust Methods


Book Description

Intended for both researchers and practitioners, this book will be a valuable resource for studying and applying recent robust statistical methods. It contains up-to-date research results in the theory of robust statistics Treats computational aspects and algorithms and shows interesting and new applications.




Nonlinear Regression with R


Book Description

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




The New Palgrave Dictionary of Economics


Book Description

The award-winning The New Palgrave Dictionary of Economics, 2nd edition is now available as a dynamic online resource. Consisting of over 1,900 articles written by leading figures in the field including Nobel prize winners, this is the definitive scholarly reference work for a new generation of economists. Regularly updated! This product is a subscription based product.




Linear Probability, Logit, and Probit Models


Book Description

After showing why ordinary regression analysis is not appropriate for investigating dichotomous or otherwise 'limited' dependent variables, this volume examines three techniques which are well suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models.




Nonparametric and Semiparametric Models


Book Description

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.




Modern Methods for Robust Regression


Book Description

Offering an in-depth treatment of robust and resistant regression, this volume takes an applied approach and offers readers empirical examples to illustrate key concepts.




Fitting Models to Biological Data Using Linear and Nonlinear Regression


Book Description

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.




Computational Surgery and Dual Training


Book Description

The future of surgery is intrinsically linked to the future of computational sciences: the medical act will be computer assisted at every single step, from planning to post-surgery recovery and through the surgical procedure itself. Looking back at the history of surgery, surgery practice has changed drama- cally with the extensive use of revolutionary techniques, such as medical imaging, laparoscopy, endoscopy, sensors and actuators, and robots. This trend is dependent on the use of computer processing, computational method, and virtualization. Computational surgery will not only improve the ef?ciency and quality of surgery, but will also give new access to very complex operations that require extreme precision and minimum intrusion. Such examples are today’s inoperable cancer tumors that have invaded critical tissues or nervous centers. In order for this milestone to be reached quicker and more ef?ciently, surgeons will have to become very familiar with computing methods, such as image analysis, augmented re- ity, and/or robotics. It will be critical for surgeons to assimilate computers in their training, understand how computers work, understand the limitations/advantages of these computer tools, and be able to interpret computer imaging and simulations.




Applied Econometrics with R


Book Description

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.




Robust Multivariate Analysis


Book Description

This text presents methods that are robust to the assumption of a multivariate normal distribution or methods that are robust to certain types of outliers. Instead of using exact theory based on the multivariate normal distribution, the simpler and more applicable large sample theory is given. The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor analysis. A simple way to bootstrap confidence regions is also provided. Much of the research on robust multivariate analysis in this book is being published for the first time. The text is suitable for a first course in Multivariate Statistical Analysis or a first course in Robust Statistics. This graduate text is also useful for people who are familiar with the traditional multivariate topics, but want to know more about handling data sets with outliers. Many R programs and R data sets are available on the author’s website.