منابع مشابه
Robust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
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Machine learning and statistics typically focus on building models that capture the vast majority of the data, possibly ignoring a small subset of data as “noise” or “outliers.” By contrast, here we consider the problem of jointly identifying a significant (but perhaps small) segment of a population in which there is a highly sparse linear regression fit, together with the coefficients for the ...
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We consider the online sparse linear regression problem, which is the problem of sequentially making predictions observing only a limited number of features in each round, to minimize regret with respect to the best sparse linear regressor, where prediction accuracy is measured by square loss. We give an inefficient algorithm that obtains regret bounded by Õ( √ T ) after T prediction rounds. We...
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In the Sparse Linear Regression (SLR) problem, given a d×n matrix M and a d-dimensional vector q, we want to compute a k-sparse vector τ such that the error ‖Mτ − q‖ is minimized. In this paper, we present algorithms and conditional lower bounds for several variants of this problem. In particular, we consider (i) the Affine SLR where we add the constraint that ∑ i τi = 1 and (ii) the Convex SLR...
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ژورنال
عنوان ژورنال: Biometrika
سال: 2012
ISSN: 0006-3444,1464-3510
DOI: 10.1093/biomet/ass043