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pro vyhledávání: '"Du, Lilun"'
Many important tasks of large-scale recommender systems can be naturally cast as testing multiple linear forms for noisy matrix completion. These problems, however, present unique challenges because of the subtle bias-and-variance tradeoff of and an
Externí odkaz:
http://arxiv.org/abs/2312.00305
Autor:
Yang, Xinxin, Du, Lilun
Large-scale multiple testing under static factor models is commonly used to select skilled funds in financial market. However, static factor models are arguably too stringent as it ignores the serial correlation, which severely distorts error rate co
Externí odkaz:
http://arxiv.org/abs/2303.07631
Modern technological advances have expanded the scope of applications requiring analysis of large-scale datastreams that comprise multiple indefinitely long time series. There is an acute need for statistical methodologies that perform online inferen
Externí odkaz:
http://arxiv.org/abs/2111.01339
We develop a new class of distribution--free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence struct
Externí odkaz:
http://arxiv.org/abs/2002.11992
In a thought-provoking paper, Efron (2011) investigated the merit and limitation of an empirical Bayes method to correct selection bias based on Tweedie's formula first reported by \cite{Robbins:1956}. The exceptional virtue of Tweedie's formula for
Externí odkaz:
http://arxiv.org/abs/1903.00776
Akademický článek
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In this article, we propose a factor-adjusted multiple testing (FAT) procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable factors, for the purpose of selecting skilled funds in empirical fina
Externí odkaz:
http://arxiv.org/abs/1407.5515
Autor:
Du, Lilun, Zhang, Chunming
Publikováno v:
Annals of Statistics 2014, Vol. 42, No. 4, 1262-1311
In the context of large-scale multiple testing, hypotheses are often accompanied with certain prior information. In this paper, we present a single-index modulated (SIM) multiple testing procedure, which maintains control of the false discovery rate
Externí odkaz:
http://arxiv.org/abs/1407.0185
Akademický článek
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