Journal ArticleDOI
Experimental design
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Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning.Abstract:
Maximizing data information requires careful selection, termed design, of the points at which data are observed. Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.read more
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A Kenward-Roger approximation and parametric bootstrap methods for tests in linear mixed models: The R Package pbkrtest
Ulrich Halekoh,Søren Højsgaard +1 more
TL;DR: The pbkrtest package as discussed by the authors implements two alternatives to such approximate?2 tests: the package implements (1) a Kenward-Roger approximation for performing F tests for reduction of the mean structure and (2) parametric bootstrap methods for achieving the same goal.
Journal ArticleDOI
A Survey of Predictive Modeling on Imbalanced Domains
TL;DR: The main challenges raised by imbalanced domains are discussed, a definition of the problem is proposed, the main approaches to these tasks are described, and a taxonomy of the methods are proposed.
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Non-normal data: Is ANOVA still a valid option?
TL;DR: This study provides a systematic examination of F‐test robustness to violations of normality in terms of Type I error, considering a wide variety of distributions commonly found in the health and social sciences.
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On Effect Size
TL;DR: A definition of effect size is proposed, which is purposely more inclusive than the way many have defined and conceptualized effect size, and it is unique with regard to linking effect size to a question of interest.
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Mind over matter: reappraising arousal improves cardiovascular and cognitive responses to stress.
TL;DR: In this article, the authors examined whether reappraisalating stress-induced arousal could improve cardiovascular outcomes and decrease attentional bias for emotionally negative information, and found that participants who were instructed to reappraise their arousal exhibited more adaptive cardiovascular stress responses.
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TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
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A Stochastic Approximation Method
Herbert Robbins,Sutton Monro +1 more
TL;DR: In this article, a method for making successive experiments at levels x1, x2, ··· in such a way that xn will tend to θ in probability is presented.
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Journal ArticleDOI
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Jianqing Fan,Runze Li +1 more
TL;DR: In this article, penalized likelihood approaches are proposed to handle variable selection problems, and it is shown that the newly proposed estimators perform as well as the oracle procedure in variable selection; namely, they work as well if the correct submodel were known.