Li Hsu
Bootstrap Inference for Network Construction.
Wed, 11/23/2011 - 15:01 — SomNambulAuthors: Pei Wang, Li Hsu, Jie Peng, Shuang Li
Subjects: Methodology
AbstractRegularization techniques are widely used for tackling
high-dimension-low-sample-size problems. Yet, finding the right amount of
regularization can be challenging, especially in the unsupervised setting such
as structure learning problems where traditional methods such as BIC or
cross-validation often do not work well. In this paper, we propose a new method
--- Bootstrap Inference for Network COnstruction (BINCO) --- to infer networks
by directly controlling the false discovery rates (FDRs) of the selected edges.
This method utilizes the idea of model aggregation.Learning networks from high dimensional binary data: An application to genomic instability data.
Thu, 08/27/2009 - 13:23 — SomNambulAuthors: Pei Wang, Dennis L. Chao, Li Hsu
Subjects: Methodology
AbstractGenomic instability, the propensity of aberrations in chromosomes, plays a
critical role in the development of many diseases. High throughput genotyping
experiments have been performed to study genomic instability in diseases. The
output of such experiments can be summarized as high dimensional binary
vectors, where each binary variable records aberration status at one marker
locus. It is of keen interest to understand how these aberrations interact with
each other. In this paper, we propose a novel method, \texttt{LogitNet}, to
infer the interactions among aberration events.
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