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Consistency of l1 penalized negative binomial regressions

被引：0
|
作者：
Xie, Fang [1 ]
Xiao, Zhijie [2 ]
机构：
[1] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Hubei, Peoples R China
[2] Boston Coll, Dept Econ, Chestnut Hill, MA 02467 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2020年 / 165卷
关键词：
Negative binomial regression; l(1) consistency; Penalized maximum likelihood; High-dimensional regression; COUNT DATA; POISSON;
D O I：
10.1016/j.spl.2020.108816
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We prove the consistency of the l(1) penalized negative binomial regression (NBR). A real data application about German health care demand shows that the l(1) penalized NBR produces a more concise but more accurate model, comparing to the classical NBR. (C) 2020 Elsevier B.V. All rights reserved.
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页数：7
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