P -Values for High-Dimensional Regression Nicolai Meinshausen y Lukas Meier zPeter Buhlmann June 12, 2009 Abstract Assigning signi cance in high-dimensional regression is challenging. Most computa-tionally e cient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p -values are not available.
P -Values for High-Dimensional Regression Nicolai Meinshausen y Lukas Meier zPeter Bu hlmann November 13, 2008 Abstract Assigning signi cance in high-dimensional regression is challenging. Most computationally e cient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p -values are not available.
W e consider the usual high-dimensional linear regression setup with a resp onse vector Y = ( Y 1 , . . . , Y n ) and an n × p ?xed design matrix X such that, p -Values for High-Dimensional Regression Nicolai Meinshausen, Lukas Meier, and Peter Bühlmann Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid /7-values are not available.
1/24/2012 · Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder that splits the data into two parts.
11/13/2008 · Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p -values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two parts. The number of variables is then.
p -Values for High-Dimensional Regression Nicolai MEINSHAUSEN, Lukas MEIER, and Peter BÜHLMANN Assigning signi?cance in high-dimensional regression is challenging. Most computationally ef?cient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p -values are not available. An exception is a recent …
11/13/2008 · So far, p-values were notavailable in high-dimensional situations, except for the proposal ofWasserman andRoeder (2008). An ad-hoc solution for assigning relevance isto use the bootstrap to analyze the stability of the selected predictorsand to focus on those which are selected most often (or evenalways).
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p -values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two parts.
It is easy to get p -values from a linear regression and related methods (t-test, anova, logistic regression ), but how can one get p -values in a high dimensional setting (p >> n)? I understand that these problems wouldn’t even be solvable, because the necessary collineairity in the data and for the low power.
