Weighted likelihood transfer learning for high-dimensional generalized linear models
IF 1.2
4区 数学
Q2 STATISTICS & PROBABILITY
引用次数: 0
Abstract
To simultaneously improve parameter estimation and variable selection for a target model by the auxiliary information from source models, a weighted likelihood transfer learning (WL-TL), together w...高维广义线性模型的加权似然转移学习
为了同时利用源模型的辅助信息改进目标模型的参数估计和变量选择，一种加权似然迁移学习（WL-TL）与一种加权似然转移学习（WL-TL）相结合，可用于目标模型的参数估计和变量选择。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
求助全文
约1分钟内获得全文
求助全文
来源期刊
Statistics
数学-统计学与概率论
CiteScore
1.00
自引率
0.00%
发文量
59
审稿时长
12 months
期刊介绍：
Statistics publishes papers developing and analysing new methods for any active field of statistics, motivated by real-life problems. Papers submitted for consideration should provide interesting and novel contributions to statistical theory and its applications with rigorous mathematical results and proofs. Moreover, numerical simulations and application to real data sets can improve the quality of papers, and should be included where appropriate. Statistics does not publish papers which represent mere application of existing procedures to case studies, and papers are required to contain methodological or theoretical innovation. Topics of interest include, for example, nonparametric statistics, time series, analysis of topological or functional data. Furthermore the journal also welcomes submissions in the field of theoretical econometrics and its links to mathematical statistics.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
