TY - JOUR

T1 - Regularity Properties for Sparse Regression

T2 - A tribute to Professor Xiru Chen

AU - Dobriban, Edgar

AU - Fan, Jianqing

N1 - Publisher Copyright:
© 2016, School of Mathematical Sciences, University of Science and Technology of China and Springer-Verlag Berlin Heidelberg.

PY - 2016/3/1

Y1 - 2016/3/1

N2 - Statistical and machine learning theory has developed several conditions ensuring that popular estimators such as the Lasso or the Dantzig selector perform well in high-dimensional sparse regression, including the restricted eigenvalue, compatibility, and ℓq sensitivity properties. However, some of the central aspects of these conditions are not well understood. For instance, it is unknown if these conditions can be checked efficiently on any given dataset. This is problematic, because they are at the core of the theory of sparse regression. Here we provide a rigorous proof that these conditions are NP-hard to check. This shows that the conditions are computationally infeasible to verify, and raises some questions about their practical applications. However, by taking an average-case perspective instead of the worst-case view of NP-hardness, we show that a particular condition, ℓq sensitivity, has certain desirable properties. This condition is weaker and more general than the others. We show that it holds with high probability in models where the parent population is well behaved, and that it is robust to certain data processing steps. These results are desirable, as they provide guidance about when the condition, and more generally the theory of sparse regression, may be relevant in the analysis of high-dimensional correlated observational data.

AB - Statistical and machine learning theory has developed several conditions ensuring that popular estimators such as the Lasso or the Dantzig selector perform well in high-dimensional sparse regression, including the restricted eigenvalue, compatibility, and ℓq sensitivity properties. However, some of the central aspects of these conditions are not well understood. For instance, it is unknown if these conditions can be checked efficiently on any given dataset. This is problematic, because they are at the core of the theory of sparse regression. Here we provide a rigorous proof that these conditions are NP-hard to check. This shows that the conditions are computationally infeasible to verify, and raises some questions about their practical applications. However, by taking an average-case perspective instead of the worst-case view of NP-hardness, we show that a particular condition, ℓq sensitivity, has certain desirable properties. This condition is weaker and more general than the others. We show that it holds with high probability in models where the parent population is well behaved, and that it is robust to certain data processing steps. These results are desirable, as they provide guidance about when the condition, and more generally the theory of sparse regression, may be relevant in the analysis of high-dimensional correlated observational data.

KW - Computational complexity

KW - High-dimensional statistics

KW - Restricted eigenvalue

KW - Sparse regression

KW - ℓ sensitivity

UR - http://www.scopus.com/inward/record.url?scp=84976406289&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84976406289&partnerID=8YFLogxK

U2 - 10.1007/s40304-015-0078-6

DO - 10.1007/s40304-015-0078-6

M3 - Article

C2 - 27330929

AN - SCOPUS:84976406289

VL - 4

JO - Communications in Mathematics and Statistics

JF - Communications in Mathematics and Statistics

SN - 2194-6701

IS - 1

ER -