Results 11 to 20 of about 331,548 (294)
Simulated Multivariate Random-Effects Probit Models for Unbalanced Panels [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2014 This article develops a method for implementing a simulated multivariate random-effects probit model for unbalanced panels (with gaps) and illustrates the model by using artificial data. Halton draws generated by mdraws are used to simulate multivariate normal probabilities with the mvnp() egen function.
Alexander Plum
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Bayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit Models [PDF]
Journal of Mathematics and Statistics, 2022 : Multivariate probit models have been explored for analyzing longitudinal ordinal data. However, the inherent identification issue in multivariate probit models requires the covariance matrix of the underlying latent multivariate normal variables to be ...
Xiao Zhang
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The Scientist, 2019 This study assessed farmers’ perception of climate change, and estimated the determinants of, and evaluated the relationship among, adaptation practices using the multivariate probit model. A survey in 300 agricultural households was carried out covering
Arun GC, Yeo
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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:
John Mullahy
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Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models [PDF]
Computational Statistics & Data Analysis, 2008 The multinomial probit model has emerged as a useful framework for modeling nominal categorical data, but extending such models to multivariate measures presents computational challenges. Following a Bayesian paradigm, we use a Markov chain Monte Carlo (MCMC) method to analyze multivariate nominal measures through multivariate multinomial probit models.
Xiao Zhang +2 more
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Pairwise likelihood inference for the multivariate ordered probit model [PDF]
, 2019 This paper provides a closed form expression for the pairwise score vector for the multivariate ordered probit model. This result has several implications in likelihood-based inference. It is indeed used both to speed-up gradient based optimization routines for point estimation, and to provide a building block to compute standard errors and confidence ...
Martina Bravo, Antonio Canale
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Prediction of Patient-Reported Outcome Measures Via Multivariate Ordered Probit Models [PDF]
Journal of the Royal Statistical Society Series A: Statistics in Society, 2014 SummaryThe assessment of patient-reported outcome measures (PROMs) is of central importance in many areas of research and public policy. Unfortunately, it is quite common for clinical studies to employ different PROMs, thus limiting the comparability of the evidence base that they contribute to.
Caterina Conigliani +2 more
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Adoption of Coffee Technologies: A Multivariate Probit Model
International Journal of Theoretical and Applied MathematicsThe sector of agriculture in Ethiopia is a source of livelihood for over 80% population residing in rural areas. It contributes about 50% to the national value of production. The country has huge potential to increase coffee production as it endowed with suitable elevation, temperature, and soil fertility, indigenous quality plantation materials, and ...
Megdelawit Temesgen, Sisay Debeb
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Journal of Innovation and EntrepreneurshipSelecting appropriate market outlets offer the opportunity for farmers to capture a bigger share of the price paid by final consumers. However, smallholder farmers in developing countries are still confronted with myriad challenges regarding selecting ...
Tibebu Legesse +4 more
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Bayesian inference for multivariate probit model with latent envelope
BiometricsABSTRACTThe response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has
Kwangmin Lee, Yeonhee Park
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