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
收藏
页数：7
相关论文
共 50 条
  • [31] SEMIPARAMETRIC ESTIMATION OF HETEROGENEOUS COUNT DATA MODELS
    BRANNAS, K
    ROSENQVIST, G
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 1994, 76 (02) : 247 - 258
  • [32] AR(1) latent class models for longitudinal count data
    Henderson, Nicholas C.
    Rathouz, Paul J.
    [J]. STATISTICS IN MEDICINE, 2018, 37 (29) : 4441 - 4456
  • [33] DYNAMIC COUNT DATA MODELS OF TECHNOLOGICAL INNOVATION
    BLUNDELL, R
    GRIFFITH, R
    VANREENEN, J
    [J]. ECONOMIC JOURNAL, 1995, 105 (429): : 333 - 344
  • [34] Quasi-Maximum Likelihood Estimation For Latent Variable Models With Mixed Continuous And Polytomous Data
    Eickhoff, Jens C.
    [J]. JOURNAL OF MODERN APPLIED STATISTICAL METHODS, 2005, 4 (02) : 473 - 481
  • [35] On the role of latent variable models in the era of big data
    Bartolucci, Francesco
    Bacci, Silvia
    Mira, Antonietta
    [J]. STATISTICS & PROBABILITY LETTERS, 2018, 136 : 165 - 169
  • [36] Discriminative Embeddings of Latent Variable Models for Structured Data
    Dai, Hanjun
    Dai, Bo
    Song, Le
    [J]. INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 48, 2016, 48
  • [37] Latent variable models in the understanding of animal monitoring data
    Stephen, Bruce
    Michie, Craig
    Andonovic, Ivan
    [J]. Lecture Notes in Electrical Engineering, 2012, 146 LNEE : 119 - 134
  • [38] LATENT VARIABLE MODELS OF DICHOTOMOUS DATA - THE STATE OF THE METHOD
    SCHOENBERG, R
    ARMINGER, G
    [J]. SOCIOLOGICAL METHODS & RESEARCH, 1989, 18 (01) : 164 - 182
  • [39] Generating structure of latent variable models for nested data
    Ishihata, Masakazu
    Iwata, Tomoharu
    [J]. UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, 2014, : 350 - 359
  • [40] Latent variable models with mixed continuous and polytomous data
    Shi, JQ
    Lee, SY
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2000, 62 : 77 - 87
12345