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

被引：0

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.

引用

页码：101 / 150

页数：50

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