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- W2022778119 abstract "Abstract A growing number of modern scientific problems in areas such as genomics, neurobiology, and spatial epidemiology involve the measurement and analysis of thousands of related features that may be stochastically dependent at arbitrarily strong levels. In this work, we consider the scenario where the features follow a multivariate Normal distribution. We demonstrate that dependence is manifested as random variation shared among features, and that standard methods may yield highly unstable inference due to dependence, even when the dependence is fully parameterized and utilized in the procedure. We propose a “cross-dimensional inference” framework that alleviates the problems due to dependence by modeling and removing the variation shared among features, while also properly regularizing estimation across features. We demonstrate the framework on both simultaneous point estimation and multiple hypothesis testing in scenarios derived from the scientific applications of interest. KEY WORDS: Dependent dataFalse discovery rateHigh-dimensional biologyMultiple hypothesis testingSimultaneous inference Acknowledgments This research was supported in part by the National Institutes of Health (NIH) grant R01 HG002913." @default.
- W2022778119 created "2016-06-24" @default.
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- W2022778119 date "2012-03-01" @default.
- W2022778119 modified "2023-10-03" @default.
- W2022778119 title "Cross-Dimensional Inference of Dependent High-Dimensional Data" @default.
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- W2022778119 doi "https://doi.org/10.1080/01621459.2011.645777" @default.
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