Results 21 to 30 of about 21,585,736 (352)

GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model [PDF]

Statistics and computing, 2016
Gibbs sampling is a widely used Markov chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences.
Alexander Terenin, Shawfeng Dong, D. Draper   +2 more
semanticscholar   +1 more source

PERBANDINGAN ESTIMASI MODEL RESPON KUALITATIF MENGGUNAKAN METODE OLS, GMM DAN MAXIMUM LIKELIHOOD: PADA KASUS PROBABILITAS KEPEMILIKAN MOBIL RUMAH TANGGA DI KELURAHAN PAHLAWAN KOTA PALEMBANG

Jurnal Ekonomi Pembangunan, 2013
This study aim to comparing accuracy in the analysis of qualitative response data especially to analyze the probability of car ownership households by using LPM models , GMM,  Probit and Logit Models.
Syamsurijal AK, imam Asngari
doaj   +1 more source

Estimation of Multivariate Probit Models via Bivariate Probit [PDF]

The Stata Journal: Promoting communications on statistics and Stata, 2016
In this article, I suggest the utility of fitting multivariate probit models using a chain of bivariate probit estimators. This approach is based on Stata's biprobit and suest commands and is driven by a Mata function, bvpmvp(). I discuss two potential advantages of the approach over the mvprobit command (Cappellari and Jenkins, 2003, Stata Journal 3:
openaire   +3 more sources

Child poverty and its determinants; the case of Negele Arsi Woreda, Oromia regional state, Ethiopia

Cogent Food & Agriculture, 2023
Child poverty is widespread and anticipated to endure in the future, particularly in developing countries. By 2030, it is predicted that a considerable number of children would be living in poverty. As a result, addressing child poverty is a top priority
Bogale Gemede, Bamlaku Alamirew, Bezabih Emana   +2 more
doaj   +1 more source

Mixture of normals probit models [PDF]

, 1997
This paper generalizes the normal probit model of dichotomous choice by introducing mixtures of normals distributions for the disturbance term. By mixing on both the mean and variance parameters and by increasing the number of distributions in the mixture these models effectively remove the normality assumption and are much closer to semiparametric ...
John F. Geweke, Michael P. Keane
openaire   +3 more sources

Geographically Weighted Probit Ordinal Regression Model Estimation [PDF]

E3S Web of Conferences
Geographically Weighted Probit Ordinal Regression (GWPOR) is a combined method between Geographically Weighted Regression and Probit Ordinal Regression. This study estimates the percentage of poor people using the GWPOR method.
Kurniawan Muh. Idham, Purhadi, Rahayu Santi Puteri   +2 more
doaj   +1 more source

Objective Bayesian Inference in Probit Models with Intrinsic Priors Using Variational Approximations

Entropy, 2020
There is not much literature on objective Bayesian analysis for binary classification problems, especially for intrinsic prior related methods. On the other hand, variational inference methods have been employed to solve classification problems using ...
Ang Li, Luis Pericchi, Kun Wang
doaj   +1 more source

Analysis of Work-Zone Crashes Using the Ordered Probit Model with Factor Analysis in Egypt

Journal of Advanced Transportation, 2018
Work-zones, due to their nature, are predisposed to hazardous situations. This is a consequence of conducting construction work within the vicinity of, or near, vehicular traffic.
Kairan Zhang, M. Hassan, M. Yahaya, Shupeng Yang   +3 more
semanticscholar   +1 more source

A multinomial probit model of stochastic evolution [PDF]

Journal of Economic Theory, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Myatt, DP, Wallace, C
openaire   +8 more sources

Probit Normal Correlated Topic Model

Open Journal of Statistics, 2014
11 pages, 2 figures and 2 ...
Yu, Xingchen, Fokoué, Ernest
openaire   +3 more sources