Add to ORKGAbstract: We introduce Domain Splitting as a new tool for regression analysis. This device corresponds to splitting the domain of a regression function into m subdomains, where m is varied, and fitting a linear model on each subdomain. The residual sums of squares from these various fits are compared graphically. Domain Splitting provides a visual diagnostic, as well as a model-independent estimate of the error variance. We investigate the asymptotic behavior of Domain Splitting for the cases of an underlying linear model and that of a smooth regression function. The asymptotic findings are illustrated in simulations and examples. Key words and phrases: Diagnostic plot, goodness-of-fit, linear model, model selec-tion, smooth regression, var...
The analysis of a statistical large data-set can be led by the study of a particularly interesting ...
International audienceThis article and its sequel form an introduction to the field of regression an...
A general regression problem is one in which a response variable can be expressed as some function o...
RT: Linear smoothers for dimension estimation Abstract: Sliced Inverse Regression (Li, 1991) is a si...
Data splitting divides the training data set into two sets H and the validation set V.Data splitting...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
It is common to split a dataset into training and testing sets before fitting a statistical or machi...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-...
This thesis presents a new approach to fitting linear models, called “pace regression”, which also o...
Since its introduction in the early 90's, the Sliced Inverse Regression (SIR) methodology has evolve...
A number of different kinds of residuals are used in the analysis of generalized linear models. Gene...
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional cova...
Results of simple linear regression analysis examining the relationship between the areas of the reg...
The analysis of a statistical large data-set can be led by the study of a particularly interesting ...
International audienceThis article and its sequel form an introduction to the field of regression an...
A general regression problem is one in which a response variable can be expressed as some function o...
RT: Linear smoothers for dimension estimation Abstract: Sliced Inverse Regression (Li, 1991) is a si...
Data splitting divides the training data set into two sets H and the validation set V.Data splitting...
Regression is the study of the dependence of a response variable y on a collection predictors p coll...
It is common to split a dataset into training and testing sets before fitting a statistical or machi...
Summary. Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromont...
In statistics, dimension reduction is a method to reduce the number of variables, which will then be...
Sliced inverse regression is one of the widely used dimension reduction methods. Chiaromonte and co-...
This thesis presents a new approach to fitting linear models, called “pace regression”, which also o...
Since its introduction in the early 90's, the Sliced Inverse Regression (SIR) methodology has evolve...
A number of different kinds of residuals are used in the analysis of generalized linear models. Gene...
A semiparametric regression model of a q-dimensional multivariate response y on a p-dimensional cova...
Results of simple linear regression analysis examining the relationship between the areas of the reg...
The analysis of a statistical large data-set can be led by the study of a particularly interesting ...
International audienceThis article and its sequel form an introduction to the field of regression an...
A general regression problem is one in which a response variable can be expressed as some function o...