Focused information criterion and model averaging for generalized additive partial linear models

Estimation and Variable Selection for Generalized Additive Partial Linear Models.

We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus ...

Bayesian Model Averaging for Generalized Linear Models with Missing Covariates

We address the problem of estimating generalized linear models (GLMs) when the outcome of interest is always observed, the values of some covariates are missing for some observations, but imputations are available to fill-in the missing values. Under certain conditions on the missing-data mechanism and the imputation model, this situation generates a trade-off between bias and precision in the ...

A focused information criterion for graphical models

A new method for model selection for Gaussian Bayesian networks and Markov networks, with extensions towards ancestral graphs, is constructed to have good mean squared error properties. The method is based on the focused information criterion, and offers the possibility of fitting individualtailored models. The focus of the research, that is, the purpose of the model, directs the selection. It ...

Computations for Linear and Generalized Additive Models

Model averaging estimation of generalized linear models with imputed covariates

We address the problem of estimating generalized linear models when some covariate values aremissing but imputations are available to fill-in the missing values. This situation generates a bias-precision tradeoff in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a p...