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BookDOI

Modern Applied Statistics with S

W. N. Venables, +1 more
- Iss: 1
TLDR
A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.
Abstract
A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods The emphasis is on presenting practical problems and full analyses of real data sets

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Book

Analysis of Financial Time Series

TL;DR: The author explains how the Markov Chain Monte Carlo Methods with Applications and Principal Component Analysis and Factor Models changed the way that conventional Monte Carlo methods were applied to time series analysis.
Journal ArticleDOI

Do we need hundreds of classifiers to solve real world classification problems

TL;DR: The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in theTop-20, respectively).
Book

Learning to Rank for Information Retrieval

TL;DR: Three major approaches to learning to rank are introduced, i.e., the pointwise, pairwise, and listwise approaches, the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures are analyzed, and the performance of these approaches on the LETOR benchmark datasets is evaluated.
Book

Model-based Geostatistics

TL;DR: An overview of model-based geostatistics can be found in this paper, where a generalized linear model is proposed for estimating geometrical properties of geometrically constrained data.
Journal ArticleDOI

The Split-Apply-Combine Strategy for Data Analysis

TL;DR: This paper gives rise to a new R package that allows you to smoothly apply a split-apply-combine strategy, without having to worry about the type of structure in which your data is stored.