文章基本信息

标题：Covariate-adjusted nonparametric analysis of magnetic resonance images using Markov chain Monte Carlo
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作者：Susan Spear Bassett ; Brian Caffo ; Haley Hedlin 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2010
卷号：3
期号：1
页码：113-123
DOI：10.4310/SII.2010.v3.n1.a11
出版社：International Press
摘要：Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain under minimal assumptions. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our nonparametric method to remove potential bias due to the observed covariates is presented.
关键词：covariate control; permutation testing; nonparametric inference; Markov chain Monte Carlo; imaging data