Sparse inference of structural equation modeling with latent variables for diffusion processes

被引：1
|
作者：
Kusano, Shogo [1 ]
Uchida, Masayuki [1 ,2 ,3 ]
机构：
[1] Osaka Univ, Grad Sch Engn Sci, Osaka, Japan
[2] Osaka Univ, Ctr Math Modeling & Data Sci MMDS, Osaka, Japan
[3] JST CREST, Osaka, Japan
来源：
JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE | 2024年 / 7卷 / 01期
关键词：
Structural equation modeling; Asymptotic theory; High-frequency data; Stochastic differential equation; Quasi-maximum likelihood estimation; Sparse inference; PENALIZED LIKELIHOOD; STATISTICAL-ANALYSIS; ADAPTIVE LASSO; SELECTION; MULTIPLE;
D O I：
10.1007/s42081-023-00230-1
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 consider structural equation modeling (SEM) with latent variables for diffusion processes based on high-frequency data. The quasi-likelihood estimators for parameters in the SEM are proposed. The goodness-of-fit test is derived from the quasi-likelihood ratio. We also treat sparse inference in the SEM. The goodness-of-fit test for the sparse inference in the SEM is developed. Furthermore, the asymptotic properties of our proposed estimators and test statistics are examined.
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页码：101 / 150
页数：50
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