高维Lp分位数回归的通信高效低维参数估计和推理

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Junzhuo Gao, Lei Wang
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引用次数: 0

摘要

Lp‐分位数回归概括了分位数回归和期望回归,并因其稳健性和有效性而广受欢迎,尤其是当1本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Communication‐efficient low‐dimensional parameter estimation and inference for high‐dimensional Lp‐quantile regression
The Lp‐quantile regression generalizes both quantile regression and expectile regression, and has become popular for its robustness and effectiveness especially when 1 < p ≤ 2. In this paper, we consider the data that are inherently distributed and propose two distributed Lp‐quantile regression estimators for a preconceived low‐dimensional parameter in the presence of high‐dimensional extraneous covariates. To handle the impact of high‐dimensional nuisance parameters, we first investigate regularized projection score for estimating low‐dimensional parameter of main interest in Lp‐quantile regression. To deal with the distributed data, we further propose two communication‐efficient surrogate projection score estimators and establish their theoretical properties. The finite‐sample performance of the proposed estimators is studied through simulations and an application to Communities and Crime data set is also presented.This article is protected by copyright. All rights reserved.
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来源期刊
Scandinavian Journal of Statistics
Scandinavian Journal of Statistics 数学-统计学与概率论
CiteScore
1.80
自引率
0.00%
发文量
61
审稿时长
6-12 weeks
期刊介绍: The Scandinavian Journal of Statistics is internationally recognised as one of the leading statistical journals in the world. It was founded in 1974 by four Scandinavian statistical societies. Today more than eighty per cent of the manuscripts are submitted from outside Scandinavia. It is an international journal devoted to reporting significant and innovative original contributions to statistical methodology, both theory and applications. The journal specializes in statistical modelling showing particular appreciation of the underlying substantive research problems. The emergence of specialized methods for analysing longitudinal and spatial data is just one example of an area of important methodological development in which the Scandinavian Journal of Statistics has a particular niche.
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