Semi-Parametric Differential Expression Analysis via Partial Mixture Estimation

Semi-parametric differential expression analysis via partial mixture estimation.

We develop an approach for microarray differential expression analysis, i.e. identifying genes whose expression levels differ between two or more groups. Current approaches to inference rely either on full parametric assumptions or on permutation-based techniques for sampling under the null distribution. In some situations, however, a full parametric model cannot be justified, or the sample siz...

R routines for partial mixture estimation and differential expression analysis

Semi-Parametric Estimation for Conditional Independence Multivariate Finite Mixture Models

Abstract: The conditional independence assumption for nonparametric multivariate finite mixture models, a weaker form of the well-known conditional independence assumption for random effects models for longitudinal data, is the subject of an increasing number of theoretical and algorithmic developments in the statistical literature. After presenting a survey of this literature, including an in-...

Semi-parametric difference-based estimation of partial linear regression models

This article describes the plreg Stata command, which implements the difference-based algorithm for estimating the partial linear regression models.

Generalized EM estimation for semi-parametric mixture distributions with discretized non-parametric component

We consider independent sampling from a two-component mixture distribution, where one component (called the parametric component) is from a known distributional family and the other component (called the non-parametric component) is unknown. This is a semi-parametric mixture distribution. We discretize the non-parametric component and estimate the parameters of this mixture model, namely the mi...