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Resampling Plans and the Estimation of Prediction Error
Stats, 2021 This article was prepared for the Special Issue on Resampling methods for statistical inference of the 2020s. Modern algorithms such as random forests and deep learning are automatic machines for producing prediction rules from training data.
Bradley Efron
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A new distribution-free approach to constructing the confidence region for multiple parameters. [PDF]
PLoS ONE, 2013 Construction of confidence intervals or regions is an important part of statistical inference. The usual approach to constructing a confidence interval for a single parameter or confidence region for two or more parameters requires that the distribution ...
Zhiqiu Hu, Rong-Cai Yang
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An integrated approach for the analysis of biological pathways using mixed models. [PDF]
PLoS Genetics, 2008 Gene class, ontology, or pathway testing analysis has become increasingly popular in microarray data analysis. Such approaches allow the integration of gene annotation databases, such as Gene Ontology and KEGG Pathway, to formally test for subtle but ...
Lily Wang +3 more
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Effects of dating errors on nonparametric trend analyses of speleothem time series [PDF]
Climate of the Past, 2012 A fundamental problem in paleoclimatology is to take fully into account the various error sources when examining proxy records with quantitative methods of statistical time series analysis.
M. Mudelsee, J. Fohlmeister, D. Scholz
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MATS: Inference for potentially Singular and Heteroscedastic MANOVA [PDF]
, 2017 In many experiments in the life sciences, several endpoints are recorded per subject. The analysis of such multivariate data is usually based on MANOVA models assuming multivariate normality and covariance homogeneity.
Friedrich, Sarah, Pauly, Markus
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Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases [PDF]
, 2010 In many applications, such as physiology and finance, large time series data bases are to be analyzed requiring the computation of linear, nonlinear and other measures.
Kugiumtzis, Dimitris +1 more
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Particle Learning for General Mixtures [PDF]
, 2009 This paper develops particle learning (PL) methods for the estimation of general mixture models. The approach is distinguished from alternative particle filtering methods in two major ways.
Carvalho, Carlos M. +3 more
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Robust estimation of risks from small samples [PDF]
, 2017 Data-driven risk analysis involves the inference of probability distributions from measured or simulated data. In the case of a highly reliable system, such as the electricity grid, the amount of relevant data is often exceedingly limited, but the impact
Strbac, Goran, Tindemans, Simon H.
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Journal of Statistical Software, 2013 We introduce and examine dbEmpLikeGOF, an R package for performing goodness-of-fit tests based on sample entropy. This package also performs the two sample distribution comparison test.
Jeffrey Miecznikowski +2 more
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A Functional Wavelet-Kernel Approach for Continuous-time Prediction [PDF]
, 2005 We consider the prediction problem of a continuous-time stochastic process on an entire time-interval in terms of its recent past. The approach we adopt is based on functional kernel nonparametric regression estimation techniques where observations are ...
Antoniadis, Anestis +2 more
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