A Semiparametric Regression Model For Oligonucleotide Arrays

A Semiparametric Regression Model For Oligonucleotide Arrays

Semiparametric Poisson Regression Model for Clustered Data

A semiparametric Poisson regression is proposed in modeling spatially clustered count data. The heterogeneous covariate effect across the clusters is formulated in the context of nonparametric regression while the random clustering effect is based on a parametric specification. We propose two estimation procedures: (1) the parametric and nonparametric parts are estimated simultaneously via pena...

A Model Based Background Adjustment for Oligonucleotide Expression Arrays

High density oligonucleotide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further pre-processing and data reduction occurs following the image processing ∗Zhijin Wu is graduate student and Rafael A. Irizarry is Associate Professor of Biostatistics (E-mail: [email protected]), Franci...

A Semiparametric Change-Point Regression Model for Longitudinal Observations.

Many longitudinal studies involve relating an outcome process to a set of possibly time-varying covariates, giving rise to the usual regression models for longitudinal data. When the purpose of the study is to investigate the covariate effects when experimental environment undergoes abrupt changes or to locate the periods with different levels of covariate effects, a simple and easy-to-interpre...

Semiparametric Regression

Semiparametric regression is concerned with the flexible incorporation of nonlinear functional relationships in regression analyses. Any application area that uses regression analysis can benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a con...