A probit regression model is a regression with categorical dependent variable in dichotomy or binary form. The dependent variable value of the probit regression states the probability of certain issue. In some cases, the application of this model considers the influence of area (spatial effect). The dependency tendency to the close regions is known as autocorrelation in spatial data. Due to this matter, the parameter estimations of ordinary least square (OLS) method can not be used thus it is substituted by the simulation technique, which is a method in randomly raising data. The method includes direct and indirect simulations. The latter has Markov Chain Monte Carlo (MCMC) method with Gibbs Sampling algorithm, which is the order in conducting certain distributed random data sampling by understanding the required distribution. In this case, the beta binomial distribution is applied. A data simulation with Gibbs Sampling algorithm can be conducted by knowing the required distributions of each variable used for R software beforehand. This research purposes to define the parameter estimation value of spatial probit regression by applying MCMC and Gibbs sampling methods with R software. The results show that the parameter estimations of spatial bivariate probit regression model by simulating through Gibbs sampling algorithm (R software), in which β̂ is the independent variable parameter and ρ̂ is the spatial lag autoregressive coefficient. The simulation with the first-value determination rise result data β = (0, 1, -1), ρ = 0.7 deciding n = 400, 10, and k = 6 show the estimations for β̂ = (0.01205, 0.98709, -0.9675) and β̂ = 0.68523.
Skip Nav Destination
Article navigation
8 February 2021
THE THIRD INTERNATIONAL CONFERENCE ON MATHEMATICS: Education, Theory and Application
20 October 2020
Surakarta, Indonesia
Research Article|
February 08 2021
Data simulation with Markov Chain Monte Carlo, Gibbs sampling, and Bayes (beta-binomial) methods as the parameter estimations of spatial bivariate probit regression model
Dewi Retno Sari Saputro;
Dewi Retno Sari Saputro
a)
Department of Mathematics, Faculty of Mathematics and Natural Scienes, Universitas Sebelas Maret
, Surakarta, Indonesia
a)Corresponding author: [email protected]
Search for other works by this author on:
Yuanita Kusuma Wardani;
Yuanita Kusuma Wardani
b)
Department of Mathematics, Faculty of Mathematics and Natural Scienes, Universitas Sebelas Maret
, Surakarta, Indonesia
Search for other works by this author on:
Nafisa Berliana Indah Pratiwi;
Nafisa Berliana Indah Pratiwi
c)
Department of Mathematics, Faculty of Mathematics and Natural Scienes, Universitas Sebelas Maret
, Surakarta, Indonesia
Search for other works by this author on:
Purnami Widyaningsih
Purnami Widyaningsih
d)
Department of Mathematics, Faculty of Mathematics and Natural Scienes, Universitas Sebelas Maret
, Surakarta, Indonesia
Search for other works by this author on:
a)Corresponding author: [email protected]
AIP Conf. Proc. 2326, 020028 (2021)
Citation
Dewi Retno Sari Saputro, Yuanita Kusuma Wardani, Nafisa Berliana Indah Pratiwi, Purnami Widyaningsih; Data simulation with Markov Chain Monte Carlo, Gibbs sampling, and Bayes (beta-binomial) methods as the parameter estimations of spatial bivariate probit regression model. AIP Conf. Proc. 8 February 2021; 2326 (1): 020028. https://doi.org/10.1063/5.0040332
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
459
Views
Citing articles via
Inkjet- and flextrail-printing of silicon polymer-based inks for local passivating contacts
Zohreh Kiaee, Andreas Lösel, et al.
Effect of coupling agent type on the self-cleaning and anti-reflective behaviour of advance nanocoating for PV panels application
Taha Tareq Mohammed, Hadia Kadhim Judran, et al.
Design of a 100 MW solar power plant on wetland in Bangladesh
Apu Kowsar, Sumon Chandra Debnath, et al.
Related Content
Monte‐Carlo analysis of two logical premises to avoid the Probit algorithm for determination of sensory threshold by psychophysics
J Acoust Soc Am (April 2016)
Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size
AIP Conference Proceedings (December 2017)
A comparison of some link functions for binomial regression models with application to school drop-out rates in East Java
AIP Conf. Proc. (December 2019)
Bayesian inference in an item response theory model with a generalized student t link function
AIP Conference Proceedings (October 2012)
Bayesian Analysis of High Dimensional Classification
AIP Conference Proceedings (December 2009)