ON THE EXPONENTIALLY WEIGHTED AGGREGATE WITH THE LAPLACE PRIOR

• 成果类型: Article
• 署名作者: Dalalyan, Arnak S.; Grappin, Edwin; Paris, Quentin
• 署名单位: Institut Polytechnique de Paris; ENSAE Paris; Universite Paris Saclay; HSE University (National Research University Higher School of Economics)
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/17-AOS1626
• 发表日期: 2018
• 页码: 2452-2478
• 关键词: sharp oracle inequalities; pac-bayesian bounds; variable selection; matrix completion; posterior concentration; statistical-inference; unknown smoothness; Optimal Rates; rank; regression

摘要

In this paper, we study the statistical behaviour of the Exponentially Weighted Aggregate (EWA) in the problem of high-dimensional regression with fixed design. Under the assumption that the underlying regression vector is sparse, it is reasonable to use the Laplace distribution as a prior. The resulting estimator and, specifically, a particular instance of it referred to as the Bayesian lasso, was already used in the statistical literature because of its computational convenience, even though no thorough mathematical analysis of its statistical properties was carried out. The present work fills this gap by establishing sharp oracle inequalities for the EWA with the Laplace prior. These inequalities show that if the temperature parameter is small, the EWA with the Laplace prior satisfies the same type of oracle inequality as the lasso estimator does, as long as the quality of estimation is measured by the prediction loss. Extensions of the proposed methodology to the problem of prediction with low-rank matrices are considered.