When modeling covariate measurement error in regression, it is typically assumed that the measurement error acts in an additive or multiplicative manner on the true covariate value. However, such assumptions do not hold for the measurement error of sleep-disordered breathing (SDB). The true covariate is the severity of SDB, and the observed surrogate covariate is the number of breathing pauses ...

We consider a nonparametric regression model with a parametric family of dependent variable transformations one of which induces additive covariate eeects. The asymptotic distribution of this regression estimate is given. The practical performance is investigated via an application. 1

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 propose a hierarchical or multilevel version of regression mo...

In many scientific studies, the response variable bears a generalized nonlinear regression relationship with a certain covariate of interest, which may, however, be confounded by other covariates with unknown functional form. We propose a new class of models, the partly parametric generalized additive model (PPGAM) for doing generalized nonlinear regression with the confounding covariate effect...

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...

This paper proposes the Additive Risk Model (ARM), first used by Aalen (1980), to explain households’ interpurchase times. Unlike the Proportional Hazard Model (PHM), first proposed by Cox (1972), the ARM incorporates the effects of covariates on the individual hazard function in an additive (as opposed to multiplicative) manner. While a large number of previous studies on interpurchase timing ...

##An example using dglm.Pvalues(.) with 200 simulated observations set.seed(123) require(dglm) n = 200 a <rbinom(n, 1, 0.5) ##Covariate for additive genetic effects sex <rbinom(n, 1, 0.5) ##Covariate for a non-genetic effect add.effect = 0 sex.effect = 1 res.var = exp( a*add.effect ) ##Residual variance y <10 + a*add.effect + sex*sex.effect + rnorm(n ,0 , sqrt(res.var)) ##The additive genetic e...

We have an intercept β0, a main effect of the measured covariate Mij , β1, a main effect of the unmeasured genotypic value Aij , β2, an interaction effect of the measured covariate and the genotypic value, β3, and a residual term with variance e. This model can be extended with a main effect of the shared environment, β4Cij , plus an additional interaction effect, β5CijMij , but for our purpose...

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...