Results 11 to 20 of about 23,934,423 (243)
Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model. [PDF]
Int J Environ Res Public Health, 2019 The existing studies on drivers’ injury severity include numerous statistical models that assess potential factors affecting the level of injury. These models should address specific concerns tailored to different crash characteristics.
Chen F, Song M, Ma X.
europepmc +4 more sources
Sparse Probit Linear Mixed Model [PDF]
Machine Learning, 2017 Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various ...
Cunningham, John P. +5 more
core +2 more sources
Compliance Indicators of COVID-19 Prevention and Vaccines Hesitancy in Kenya: A Random-Effects Endogenous Probit Model [PDF]
Vaccines, 2021 Vaccine hesitancy remains a major public health concern in the effort towards addressing the COVID-19 pandemic. This study analyzed the effects of indicators of compliance with preventive practices on the willingness to take COVID-19 vaccines in Kenya ...
Abayomi Samuel Oyekale
doaj +2 more sources
Analytic Methods in Accident Research, 2018 In accident injury-severity analysis, an inherent limitation of the traditional ordered probit approach arises from the a priori consideration of a homogeneous source for the accidents that result in a no-injury (or zero-injury) outcome. Conceptually, no-
Grigorios Fountas +1 more
semanticscholar +3 more sources
Prediction of Road Accident Severity Using the Ordered Probit Model
Transportation Research Procedia, 2014 The ordered probit model is used to examine the contribution of several factors to the injury severity faced by motor-vehicle occupants involved in road accidents.
Rui Garrido +3 more
semanticscholar +3 more sources
Computational aspects of probit model [PDF]
Mathematical Communications, 2001 Sometimes the maximum likelihood estimation procedure for the probit model fails. There may be two reasons: the maximum likelihood estimate (MLE) just does not exist or computer overflow error occurs during the computation of the cumulative distribution ...
E. Demidenko
core +3 more sources
Selection-endogenous ordered probit and dynamic ordered probit models [PDF]
, 2009 In this presentation we define two qualitatitive response models: 1) Selection Endogenous Dummy Ordered Probit model (SED-OP); 2) a Selection Endogenous Dummy Dynamic Selection Ordered Probit model (SED- DOP).
Alfonso Miranda, Massimiliano Bratti
core +1 more source
Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model [PDF]
International Joint Conference on Artificial Intelligence, 2020 Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling.
Junwen Bai, Shufeng Kong, C. Gomes
semanticscholar +1 more source
Transportmetrica A: Transport Science, 2021 To account for the spatial correlation of crashes that are in close proximity, this study proposes a Bayesian spatial generalized ordered probit (SGOP) model with Leroux conditional autoregressive (CAR) prior for crash severity analysis.
Q. Zeng +3 more
semanticscholar +1 more source
New estimators for the probit regression model with multicollinearity
Scientific African, 2023 The probit regression model (PRORM) aims to model a binary response with one or more explanatory variables. The parameter of the PRORM is estimated using an estimation method called the maximum likelihood (ML), like a logistic model.
Mohamed R. Abonazel +3 more
doaj +1 more source