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Full-Text Articles in Statistical Models
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 …
A Mathematical Regression Of The U.S. Gross Private Domestic Investment 1959-2001, Byron E. Bell
A Mathematical Regression Of The U.S. Gross Private Domestic Investment 1959-2001, Byron E. Bell
Byron E. Bell
SUMMARY OF PROJECT What did I do? A study of the role the U.S. stock markets and money markets have possibly played in the Gross Private Domestic Investment (GPDI) of the United States from the year 1959 to the year 2001 and I created a Multiple Linear Regression Model (MLRM).
Derivation Of A Scaled Binomial As An Instance Of A General Discrete Exponential Distribution, Joseph Hilbe
Derivation Of A Scaled Binomial As An Instance Of A General Discrete Exponential Distribution, Joseph Hilbe
Joseph M Hilbe
No abstract provided.
Log-Negative Binomial Regression As A Generalized Linear Model, Joseph Hilbe
Log-Negative Binomial Regression As A Generalized Linear Model, Joseph Hilbe
Joseph M Hilbe
The negative binomial (NB) is a member of the exponential family of discrete probability distributions. The nature of the distribution is itself well understood, but its contribution to regression modeling, in particular as a generalized linear model (GLM), has not been appreciated. The mathematical properties of the negative binomial are derived and GLM algorithms are developed for both the canonical and log form. Geometric regression is seen as an instance of the NB. The log forms of both may be effectively used to model types of POisson-overdispersed count data. A GLM-type algorithm is created for a general log-negative binomial regression …