Generalized Additive Partial Linear Models for Clustered Data with Diverging Number of Covariates Using Gee

Gee Analysis of Clustered Binary Data with Diverging Number of Covariates

Clustered binary data with a large number of covariates have become increasingly more common in many scientific disciplines. This paper develops an asymptotic theory for generalized estimating equations (GEE) analysis of clustered binary data when the number of covariates grows to infinity with the number of clusters. In this “large n, diverging p” framework, we provide appropriate regularity c...

Efficient semiparametric estimation in generalized partially linear additive models for longitudinal/clustered data

We consider efficient estimation of the Euclidean parameters in a generalized partially linear additive models for longitudinal/clustered data when multiple covariates need to be modeled nonparametrically, and propose an estimation procedure based on a spline approximation of the nonparametric part of the model and the generalized estimating equations (GEE). Although the model in consideration ...

Estimation and Model Selection in Generalized Additive Partial Linear Models for Correlated Data with Diverging Number of Covariates1 By

We propose generalized additive partial linear models for complex data which allow one to capture nonlinear patterns of some covariates, in the presence of linear components. The proposed method improves estimation efficiency and increases statistical power for correlated data through incorporating the correlation information. A unique feature of the proposed method is its capability of handlin...

Supplement to “gee Analysis of Clustered Binary Data with Diverging Number of Covariates”’

Partial Generalized Additive Models: An Information-Theoretic Approach for Dealing With Concurvity and Selecting Variables

Scientists are often interested in which covariates are important, and how these covariates affect the response variable, rather than just making predictions. This requires inputs from both statistical modeling and background knowledge. Generalized additive models (GAMs) are a class of interpretable, multivariate nonparametric regression models which are very useful data exploration tools for t...