3 citations · 11 across the 11 of their papers we have counts for
7 papers · 1 filter
An Approximation Based Theory of Linear Regression
Laurie Davies
The goal of this paper is to provide a theory linear regression based entirely on approximations. It will be argued that the standard linear regression model based theory whether f…
Covariate Selection Based on a Model-free Approach to Linear Regression with Exact Probabilities
Laurie Davies, Lutz Dümbgen
In this paper we give a completely new approach to the problem of covariate selection in linear regression. A covariate or a set of covariates is included only if it is better in t…
Linear Regression, Covariate Selection and the Failure of Modelling
Laurie Davies
It is argued that all model based approaches to the selection of covariates in linear regression have failed. This applies to frequentist approaches based on P-values and to Bayesi…
Covariate Selection Based on a Assumpton-free Approach to Linear Regression with Exact Probabilities
Laurie Davies, Lutz Dümbgen
In this paper we give a completely new approach to the problem of covariate selection in linear regression. A covariate or a set of covariates is included only if it is better in t…
Lasso, knockoff and Gaussian covariates: a comparison
Laurie Davies
Given data and covariates one problem in linear regression is to decide which if any of the covariates to include when regressing the dependent vari…
Stepwise Choice of Covariates in High Dimensional Regression
Laurie Davies
Given data y(n) and p(n)covariates x(n) one problem in linear regression is to decide which if any of the covariates to include. There are many articles on this problem but all are…
Locally adaptive image denoising by a statistical multiresolution criterion
Thomas Hotz, Philipp Marnitz, Rahel Stichtenoth +3
We demonstrate how one can choose the smoothing parameter in image denoising by a statistical multiresolution criterion, both globally and locally. Using inhomogeneous diffusion an…