Non-normal variation across repeated measurements leads to nonlinear and heteroskedastic regression to the mean unlike the simple linear and homoskedastic regression to the mean found in normal models. This paper investigates the nature of the regression to the mean phenomenon in non-normal settings using (a) small variance approximations and (b) exact results obtained using normal mixtures to approximate non-normal distributions.
We present a latent regression model in which the regression function is possibly nonlinear, and not...
This paper discusses the problem of statistical inference in multivariate linear regression models w...
We discuss an extansion of the nonlinear random effects model from Lindstrom and Bates (1990) by add...
Regression to the mean is the term used to describe the effect by which individuals selected on the ...
A number of statistical procedures involve the, comparison of a 'regression ' mean square ...
We model a regression density nonparametrically so that at each value of the covariates the density ...
Abstract. We model a regression density nonparametrically so that at each value of the covariates th...
Applications of nonlinear and non normal regression models are in increasing order for appropriate i...
This paper proposes a new approach to modeling heteroskedastidty which enables the modeler to utiliz...
In many situations, the distribution of the error terms of a linear regression model departs signifi...
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametr...
An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression mo...
We review the alternative proposals introduced recently in the literature to update the standard for...
In this study, we explore the effects of non-normality and heteroscedasticity when testing the hypot...
We present a latent regression model in which the regression function is possibly nonlinear, and not...
We present a latent regression model in which the regression function is possibly nonlinear, and not...
This paper discusses the problem of statistical inference in multivariate linear regression models w...
We discuss an extansion of the nonlinear random effects model from Lindstrom and Bates (1990) by add...
Regression to the mean is the term used to describe the effect by which individuals selected on the ...
A number of statistical procedures involve the, comparison of a 'regression ' mean square ...
We model a regression density nonparametrically so that at each value of the covariates the density ...
Abstract. We model a regression density nonparametrically so that at each value of the covariates th...
Applications of nonlinear and non normal regression models are in increasing order for appropriate i...
This paper proposes a new approach to modeling heteroskedastidty which enables the modeler to utiliz...
In many situations, the distribution of the error terms of a linear regression model departs signifi...
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametr...
An extension of some standard likelihood based procedures to heteroscedastic nonlinear regression mo...
We review the alternative proposals introduced recently in the literature to update the standard for...
In this study, we explore the effects of non-normality and heteroscedasticity when testing the hypot...
We present a latent regression model in which the regression function is possibly nonlinear, and not...
We present a latent regression model in which the regression function is possibly nonlinear, and not...
This paper discusses the problem of statistical inference in multivariate linear regression models w...
We discuss an extansion of the nonlinear random effects model from Lindstrom and Bates (1990) by add...