In this note, we provide an elementary analysis of the prediction error of ridge regression with random design. The proof is short and self-contained. In particular, it bypasses the use of Rudelson’s deviation inequality for covariance matrices, through a combination of exchangeability arguments, matrix perturbation and operator convexity
We consider a linear model where the coefficients - intercept and slopes - are random with a distrib...
Let $(X_1,\ldots ,X_n)$ be an i.i.d. sequence of random variables in $\R^d$, $d\geq 1$, for some fun...
<p>In statistical prediction, classical approaches for model selection and model evaluation based on...
In this note, we provide an elementary analysis of the prediction error of ridge regression with ran...
Abstract This work gives a simultaneous analysis of both the ordinary least squares estimator and th...
This paper provides a comprehensive error analysis of learning with vector-valued random features (R...
Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generaliz...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one ...
In recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (...
In this paper we review some existing and propose some new estimators for estimating the ridge param...
International audienceWe consider experimental design for the prediction of a realization of a secon...
In this paper we describe a computer intensive method to find the ridge parameter in a prediction or...
We consider a linear model where the coecients - intercept and slopes - are random and independent f...
We analyze the prediction error of ridge re- gression in an asymptotic regime where the sample size ...
We consider a linear model where the coefficients - intercept and slopes - are random with a distrib...
Let $(X_1,\ldots ,X_n)$ be an i.i.d. sequence of random variables in $\R^d$, $d\geq 1$, for some fun...
<p>In statistical prediction, classical approaches for model selection and model evaluation based on...
In this note, we provide an elementary analysis of the prediction error of ridge regression with ran...
Abstract This work gives a simultaneous analysis of both the ordinary least squares estimator and th...
This paper provides a comprehensive error analysis of learning with vector-valued random features (R...
Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generaliz...
In this study, the techniques of ridge regression model as alternative to the classical ordinary lea...
We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one ...
In recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (...
In this paper we review some existing and propose some new estimators for estimating the ridge param...
International audienceWe consider experimental design for the prediction of a realization of a secon...
In this paper we describe a computer intensive method to find the ridge parameter in a prediction or...
We consider a linear model where the coecients - intercept and slopes - are random and independent f...
We analyze the prediction error of ridge re- gression in an asymptotic regime where the sample size ...
We consider a linear model where the coefficients - intercept and slopes - are random with a distrib...
Let $(X_1,\ldots ,X_n)$ be an i.i.d. sequence of random variables in $\R^d$, $d\geq 1$, for some fun...
<p>In statistical prediction, classical approaches for model selection and model evaluation based on...