Results 21 to 30 of about 334,334 (190)
Pseudo-Ranks: How to Calculate Them Efficiently in R
Journal of Statistical Software, 2020 Many popular nonparametric inferential methods are based on ranks. Among the most commonly used and most famous tests are for example the Wilcoxon-Mann-Whitney test for two independent samples, and the Kruskal-Wallis test for multiple independent groups.
Martin Happ +3 more
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NaRnEA: An Information Theoretic Framework for Gene Set Analysis
Entropy, 2023 Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of ...
Aaron T. Griffin +3 more
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Nonparametric regression in exponential families [PDF]
, 2010 Most results in nonparametric regression theory are developed only for the case of additive noise. In such a setting many smoothing techniques including wavelet thresholding methods have been developed and shown to be highly adaptive.
Brown, Lawrence D. +2 more
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Nonparametric Statistical Methods
, 2010 Nonparametric methods are appropriate when certain assumptions about distributions that common parametric methods make are questionable. In this entry, we review nonparametric statistical tests based on exact or simulated sampling distributions. We also discuss methods for nonparametric data exploration and nonparametric regression and we explain ...
Sijtsma, K., Emons, W.H.M.
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Penalized variable selection procedure for Cox models with semiparametric relative risk
, 2010 We study the Cox models with semiparametric relative risk, which can be partially linear with one nonparametric component, or multiple additive or nonadditive nonparametric components.
Du, Pang, Liang, Hua, Ma, Shuangge
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Genetics and Molecular Biology, 2008 Evaluation of performance stability and high yield is essential for yield trials conducted in different environments. We determined the stability of 10 bread wheat (Triticum aestivum L.) genotypes (5 cultivars and 5 advanced lines) using nonparametric ...
Mevlut Akcura, Yuksel Kaya
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Specification testing in nonlinear and nonstationary time series autoregression
, 2009 This paper considers a class of nonparametric autoregressive models with nonstationarity. We propose a nonparametric kernel test for the conditional mean and then establish an asymptotic distribution of the proposed test. Both the setting and the results
Gao, Jiti +3 more
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Asymptotically Sufficient Statistics in Nonparametric Regression Experiments with Correlated Noise
Journal of Probability and Statistics, 2009 We find asymptotically sufficient statistics that could help simplify inference in nonparametric regression problems with correlated errors. These statistics are derived from a wavelet decomposition that is used to whiten the noise process and to ...
Andrew V. Carter
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Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping
Frontiers in Neuroscience, 2014 Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced.
Johannes eStelzer +8 more
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Proceedings of the International Florida Artificial Intelligence Research Society ConferenceWe present a nonparametric statistical test for determining whether an agent is following a given mixed strategy in a repeated strategic-form game given samples of the agent's play.
Sam Ganzfried
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