Computationally tractable statistical estimation when there are more variables than observations
Seminar Room 1, Newton Institute
We consider the fundamental problem of estimating the mean of a vector y = X beta + z, where X is an n by p design matrix in which one can have far more variables than observations and z is a stochastic error term---the so-called `p > n' setup. When \beta is sparse, or more generally, when there is a sparse subset of covariates providing a close approximation to the unknown mean response, we ask whether or not it is possible to accurately estimate the mean using a computationally tractable algorithm.
We show that in a surprisingly wide range of situations, the lasso happens to nearly select the best subset of variables. Quantitatively speaking, we prove that solving a simple quadratic program achieves a squared error within a logarithmic factor of the ideal mean squared error one would achieve with an oracle supplying perfect information about which variables should be included in the model and which variables should not. Interestingly, our results describe the average performance of the lasso; that is, the performance one can expect in an overwhelming majority of cases where X\beta is a sparse or nearly sparse superposition of variables, but not in all cases.
Our results are sharp, nonasymptotic and widely applicable since they simply require that pairs of predictor variables be not overly collinear.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.