Aalen’s additive hazards model has gained increasing attention in recently years because it model all covariate effects as time-varying. In this thesis, our goal is to explore the application of Aalen’s model in assessing treatment effect at a given time point with varying covariate effects. First, based on Aalen’s model, we utilize the direct adjustment method to obtain the adjusted survival o...

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We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a se...

Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unitor cluster specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. In this paper, we discuss a hierarchical version of regression models with stru...

Models with structured additive predictor provide a very broad and rich framework for complex regression modeling. They can deal simultaneously with nonlinear covariate effects and time trends, unitor cluster specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different type. In this paper, we discuss a hierarchical version of regression models with stru...

The Cox proportional hazards model has become the standard in biomedical studies, particularly for settings in which the estimation covariate effects (as opposed to prediction) is the primary objective. In spite of the obvious flexibility of this approach and its wide applicability, the model is not usually chosen for its fit to the data, but by convention and for reasons of convenience. It is ...

Often, the functional form of covariate effects in an additive model varies across groups defined by levels of a categorical variable. This structure represents a factor-by-curve interaction. This article presents penalized spline models that incorporate factor-by-curve interactions into additive models. A mixed model formulation for penalized splines allows for straightforward model fitting an...

objective: the main objective of this research was estimation of genetic parameters for five consecutive measurements of egg weights in isfahan fowl using multi trait model and random regression models. methods: the statistical models included generation-hatch as a fixed effect, weeks of age as a covariate and additive genetic and individual permanent environmental effects as random effects. th...

In recent years, considerable research has been devoted to developing complex regression models that can deal simultaneouslywith nonlinear covariate effects and time trends, unitor cluster specific heterogeneity, spatial heterogeneity and complex interactions between covariates of different ∧ types. Much less effort, however, has been devoted to model and variable selection. The paper develops ...

An additive risk model is a useful alternative to the Cox model (Cox, 1972) and may be adopted when the absolute effects, instead of the relative effects, of multiple predictors on the hazard function are of interest. In this article, we propose using the threshold gradient descent regularization (TGDR) method for covariate selection, estimation and prediction in the additive risk model for rig...