Bayesian Estimation of Multiple Covariate of Autoregressive (MC-AR) Model

被引：0
|
作者：
Kumar J. [1 ]
Kumar A. [2 ]
Agiwal V. [3 ]
机构：
[1] Department of Statistics, Central University of Rajasthan, NH-8, Bandersindri, Rajasthan, Ajmer
[2] Department of Applied Science and Humanities, School of Engineering & Science, MIT Art, Design & Technology University, Loni-Kalbhor, Pune
[3] Indian Institute of Public Health, Telangana, Hyderabad
来源：
Annals of Data Science | 2024年 / 11卷 / 04期
关键词：
Autoregressive model; Bayesian inference; Covariate;
D O I：
10.1007/s40745-023-00468-2
中图分类号：
学科分类号：
摘要：
In present scenario, handling covariate/explanatory variable with the model is one of most important factor to study with the models. The main advantages of covariate are it’s dependency on past observations. So, study variable is modelled after explaining both on own past and past and future observation of covariates. Present paper deals estimation of parameters of autoregressive model with multiple covariates under Bayesian approach. A simulation and empirical study is performed to check the applicability of the model and recorded the better results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
引用
收藏
页码：1291 / 1301
页数：10
相关论文
共 50 条
  • [1] Bayesian estimation for threshold autoregressive model with multiple structural breaks
    Agiwal, Varun
    Kumar, Jitendra
    METRON-INTERNATIONAL JOURNAL OF STATISTICS, 2020, 78 (03): : 361 - 382
  • [2] Bayesian estimation for threshold autoregressive model with multiple structural breaks
    Varun Agiwal
    Jitendra Kumar
    METRON, 2020, 78 : 361 - 382
  • [3] Bayesian analysis of multiple thresholds autoregressive model
    Pan, Jiazhu
    Xia, Qiang
    Liu, Jinshan
    COMPUTATIONAL STATISTICS, 2017, 32 (01) : 219 - 237
  • [4] Bayesian analysis of multiple thresholds autoregressive model
    Jiazhu Pan
    Qiang Xia
    Jinshan Liu
    Computational Statistics, 2017, 32 : 219 - 237
  • [5] MC-AR - A software suite for comparative mocap analysis in an augmented reality environment
    Campo, Adriaan
    Van Kerrebroeck, Bavo
    Leman, Marc
    SOFTWARE IMPACTS, 2024, 19
  • [6] Bayesian Estimation and Model Selection for the Spatiotemporal Autoregressive Model with Autoregressive Conditional Heteroscedasticity Errors
    Bing SU
    Fu-kang ZHU
    Ju HUANG
    Acta Mathematicae Applicatae Sinica, 2023, 39 (04) : 972 - 989
  • [7] Bayesian Estimation and Model Selection for the Spatiotemporal Autoregressive Model with Autoregressive Conditional Heteroscedasticity Errors
    Bing Su
    Fu-kang Zhu
    Ju Huang
    Acta Mathematicae Applicatae Sinica, English Series, 2023, 39 : 972 - 989
  • [8] Bayesian Estimation and Model Selection for the Spatiotemporal Autoregressive Model with Autoregressive Conditional Heteroscedasticity Errors
    Su, Bing
    Zhu, Fu-kang
    Huang, Ju
    ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES, 2023, 39 (04): : 972 - 989
  • [9] Estimation for single-index spatial autoregressive model with covariate measurement errors
    Wang, Ke
    Wang, Dehui
    SPATIAL STATISTICS, 2024, 59
  • [10] Bayesian inference in a multiple contaminated autoregressive model with trend
    Mohammed, Noura Ait
    Guerbyenne, Hafida
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2023, 93 (12) : 2067 - 2109
12345