In this article we propose a practical and efficient computational framework for maximum likelihood estimation of multivariate probit regression models. This approach uses the Monte Carlo expectation ...

The probit regression model is a model used to analyze the relationship between categorical response variables, with predictive variables that are numerical, categorical, or the combination of both.

Ordered probit regression was used to analyze data of sensory acceptance tests designed to study the effect of brand name on the acceptability of beer samples. Eight different brands of Pilsen beer ...

Linear regression, a fundamental statistical method, serves as the backbone for predictive modeling in various fields. Whether you're a data scientist, analyst, or just someone curious about making ...

Probit and logit regression are family of regression model that model a process that output a value between 0 and 1. Usually, the process is the probability of an event happening. Both are similar in ...

This article conducts regression analysis of such data with the semiparametric probit model, which serves as an important alternative to existing semiparametric models and has recently received ...

The innovation lies in using the probit regression model to test the robustness of the questionnaire data, aiming to explore the relationship between farmers’ entrepreneurial behavior and ...

The innovation lies in using the probit regression model to test the robustness of the questionnaire data, aiming to explore the relationship between farmers’ entrepreneurial behavior and ...

Probit ("probability unit") regression is a classical machine learning technique that can be used for binary classification -- predicting an outcome that can only be one of two discrete values. For ...