probit


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probit

(ˈprɒbɪt)
n
(Units) a statistical measurement
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We estimate the following probit model via maximum-likelihood estimation (MLE):
This study compares the performance of longstanding methodological techniques of multinomial logit and ordinal probit models with more recent methods of decision tree and artificial neural network models, and combines individual models into ensembles to test if the amalgamation of the multiple methodologies enhances the classification accuracy of crash injury severity outcomes.
We used probit models to estimate the probability of detection and linear models to estimate concentration conditional on a detectable value (Gelman and Hill 2007).
This uses Probit and Logit models based on data compiled for the 2015 ERIA-ISEAS Enterprise Survey of Thailand.
Data were subjected to probit analysis (PoloPlus, Petaluma, California).
All the bioassay data were subjected to probit analysis (Finney, 1971) using PoloPlus probit analysis software.
We use Probit (Model 1) and Gamma Regression (Model 2) to model the relationship between procuring an independent audit with the likelihood of having a restatement and the magnitude of those restatements, respectively.
We use three estimation methods to account for this nonrandom sample problem: first, a bivariate probit model with endogenous selection, second, a Heckman two-step model and, third, a nonparametric sample selection model from Das, Newey, and Vella (2003, hereafter DNV).
Thus, PROBIt aims to assess the utility of brain imaging biomarkers to predict individual neuropsychological and neurobehavioural outcomes following paediatric brain injury, and to identify those factors that combine optimally to classify outcomes.
The dependent variable is binary and can be modeled by standard logistic regression models or probit models.