Semiparametric principal component poisson regression on clustered data

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...

Semiparametric Regression For Clustered Data With a Nonparametric Cluster-Level Component

Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model

In this paper we focus on the principal component regression and its application to high dimension non-Gaussian data. The major contributions are two folds. First, in low dimensions and under the Gaussian model, by borrowing the strength from recent development in minimax optimal principal component estimation, we first time sharply characterize the potential advantage of classical principal co...

Semiparametric estimation in general repeated measures problems

The paper considers a wide class of semiparametric problems with a parametric part for some covariate effects and repeated evaluations of a nonparametric function. Special cases in our approach include marginal models for longitudinal or clustered data, conditional logistic regression for matched case–control studies, multivariate measurement error models, generalized linear mixed models with a...

Estimated estimating equations: Semiparametric inference for clustered/longitudinal data

We introduce a flexible marginal modelling approach for statistical inference for clustered/longitudinal data under minimal assumptions. This estimated estimating equations (EEE) approach is semiparametric and the proposed models are fitted by quasi-likelihood regression, where the unknown marginal means are a function of the fixed-effects linear predictor with unknown smooth link, and variance...