Add to ORKGWhen dealing with the association between some random variable and two covariates, extensive experience with smoothers indicates that often a linear model poorly reflects the nature of the association. A simple approach via quantile grids that reflects the nature of the association is given. The two main goals are to illustrate this approach can make a practical difference, and to describe R functions for applying it. Included are comments on dealing with more than two covariates
Parametric models often require strong distributional assumptions about the data and are usually sen...
We propose a generalization of the linear quantile regression model to accommodate possibilities aff...
Linear regression is treated in the first section of the document. After that, logicits regression i...
The paper compares the small-sample properties of two non-parametric quantile regression estimators....
The analysis of covariance provides a common approach to adjusting for a baseline covariate in medic...
The problem of testing hypotheses about the slope of a quantile regression line when the sample size...
The coefficients of a quantile regression model are one-to-one functions of the order of the quantil...
In many fields of applications, linear regression is the most widely used statistical method to anal...
Dependent data arise in many studies. For example, children with the same parents or living in neigh...
University of Minnesota Ph.D. dissertation. May 2014. Major: Statistics. Advisor: Lan Wang. 1 comput...
Many real data sets that would normally lend themselves to being analyzed by an analysis of covarian...
There is a substantial collection of robust analysis of covariance (ANCOVA) methods that effectively...
Consider three random variables, Y , X1 and X2, having some unknown trivariate distribution and let ...
This dissertation addresses two problems. First, we study joint quantile regression at multiple quan...
In the social sciences, and beyond, we are often interested in the impact of factors on some outcome...
Parametric models often require strong distributional assumptions about the data and are usually sen...
We propose a generalization of the linear quantile regression model to accommodate possibilities aff...
Linear regression is treated in the first section of the document. After that, logicits regression i...
The paper compares the small-sample properties of two non-parametric quantile regression estimators....
The analysis of covariance provides a common approach to adjusting for a baseline covariate in medic...
The problem of testing hypotheses about the slope of a quantile regression line when the sample size...
The coefficients of a quantile regression model are one-to-one functions of the order of the quantil...
In many fields of applications, linear regression is the most widely used statistical method to anal...
Dependent data arise in many studies. For example, children with the same parents or living in neigh...
University of Minnesota Ph.D. dissertation. May 2014. Major: Statistics. Advisor: Lan Wang. 1 comput...
Many real data sets that would normally lend themselves to being analyzed by an analysis of covarian...
There is a substantial collection of robust analysis of covariance (ANCOVA) methods that effectively...
Consider three random variables, Y , X1 and X2, having some unknown trivariate distribution and let ...
This dissertation addresses two problems. First, we study joint quantile regression at multiple quan...
In the social sciences, and beyond, we are often interested in the impact of factors on some outcome...
Parametric models often require strong distributional assumptions about the data and are usually sen...
We propose a generalization of the linear quantile regression model to accommodate possibilities aff...
Linear regression is treated in the first section of the document. After that, logicits regression i...