The dimension-wise quadrature estimation of dynamic latent variable models for count data

被引：1

作者：

Bianconcini, Silvia ^{[1
]}

Cagnone, Silvia ^{[1
]}

机构：

[1] Univ Bologna, Dept Stat Sci, Via Belle Arti 41, I-40126 Bologna, Italy

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2023年 / 177卷

关键词：

Latent autoregressive models; Count data; Pairwise likelihood; Approximate likelihood inference; TIME-SERIES MODELS; STATE-SPACE MODELS; MAXIMUM-LIKELIHOOD; INFERENCE; INTEGRATION;

D O I：

10.1016/j.csda.2022.107585

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

When dynamic latent variable models are specified for discrete and/or mixed observations, problems related to the integration of the likelihood function arise since analytical solutions do not exist. A recently developed dimension-wise quadrature is applied to deal with these likelihoods with high-dimensional integrals. A comparison is performed with the pairwise likelihood method, one of the most often used remedies. Both a real data application and a simulation study show the superior performance of the dimension-wise quadrature with respect to the pairwise likelihood in estimating the parameters of the latent autoregressive process. (c) 2022 Elsevier B.V. All rights reserved.

引用

页数：7

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