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Abstract Details
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Activity Number:
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672
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #305107 |
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Title:
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CDfdr: Direct Approach to Signal and Noise Separation
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Author(s):
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Subhadeep Mukhopadhyay*+
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Companies:
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Texas A&M University
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Address:
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Department of Statistics, College Station, TX, 77843, United States
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Keywords:
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comparison density ;
quantile transformation ;
pre-flattened smoothing ;
false discovery rate
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Abstract:
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Can we do a large scale simultaneous hypothesis testing to separate signal or non-null cases from noise or null cases, using local false discovery method (Efron, 2004) under the following situation:
(A1.) Null distribution is not necessarily Normal.
(A2.) Do not need to specify/estimate alternative model.
(A3.) Can we handle discrete data ?
(A3.) Can we "Directly" estimate ratio of two density rather than estimating the null and marginal density Separately and then taking the ratio (Inefficient!).
The aim of this paper to introduce a NEW method, CDfdr based on several key ideas like comparison density, density quantile function, Pre-flattened smoothing, which accomplish the goal.
Joint Work with Prof. Emanuel Parzen.
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Authors who are presenting talks have a * after their name.
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