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Full-Text Articles in Statistics and Probability
A Statistical Framework For The Analysis Of Chip-Seq Data, Pei Fen Kuan, Dongjun Chung, Guangjin Pan, James A. Thomson, Ron Stewart, Sunduz Keles
A Statistical Framework For The Analysis Of Chip-Seq Data, Pei Fen Kuan, Dongjun Chung, Guangjin Pan, James A. Thomson, Ron Stewart, Sunduz Keles
Sunduz Keles
Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) has revolutionalized experiments for genome-wide profiling of DNA-binding proteins, histone modifications, and nucleosome occupancy. As the cost of sequencing is decreasing, many researchers are switching from microarray-based technologies (ChIP-chip) to ChIP-Seq for genome-wide study of transcriptional regulation. Despite its increasing and well-deserved popularity, there is little work that investigates and accounts for sources of biases in the ChIP-Seq technology. These biases typically arise from both the standard pre-processing protocol and the underlying DNA sequence of the generated data.
We study data from a naked DNA sequencing experiment, which sequences non-cross-linked DNA after deproteinizing and …
Bootstrap P-Values In Discrete Models: Asymptotic And Non-Asymptotic Effects, Chris Lloyd
Bootstrap P-Values In Discrete Models: Asymptotic And Non-Asymptotic Effects, Chris Lloyd
Chris J. Lloyd
(This paper is a major revision of http://works.bepress.com/chris_lloyd/15/.) Standard first order P-values suffer from two important drawbacks. First, even for quite large sample sizes they can misrepresent the exact significance which depends on nuisance parameters unspecified under the null. For most discrete models is that accuracy is variable and breaks down completely at the boundary. Second, different test statistics can give practically different results.
The bootstrap P-value is the exact significance with the null maximum estimate (ML) of the nuisance parameter substituted. We show that bootstrap P-values based on different first order statistics differ to second order. We also show …