Results 201 to 210 of about 7,508 (250)

A mixed-effects Bayesian regression model for multivariate group testing data. [PDF]

Biometrics
McMahan CS, Joyner CN, Tebbs JM, Bilder CR.   +3 more
europepmc   +1 more source

Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data [PDF]

Psychometrika, 2010
A new class of parametric models that generalize the multivariate probit model and the errors-in-variables model is developed to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters.
Poon, Wai-Yin, Wang, Hai-Bin
openaire   +2 more sources

Multivariate probit analysis: A neglected procedure in medical statistics

Statistics in Medicine, 1991
AbstractThe multivariate probit model is designed to regress a vector of correlated quantal variables on a mixture of continuous and discrete predictors. Various applications can be found in the biological, economical and psychosociological literature, but the method is not yet widely used in medical applications.
E, Lesaffre, G, Molenberghs
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A limited information estimator for the multivariate ordinal probit model

Applied Economics, 2000
A limited information estimator for the multivariate ordinal probit model is developed. The main advantage of the estimator is that even for high dimensional models, the estimation procedure requires the evaluation of bivariate normal integrals only. The proposed estimator also avoids the potential problem of encountering local maxima in the estimation
Fu, Tsu-Tan, Li, Lung-An, Lin, Yih-Ming, Kan, Kamhon   +3 more
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Multivariate probit analysis of binary familial data using stochastic representations

Computational Statistics & Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yihao Deng, Roy T. Sabo, N. Rao Chaganty
openaire   +2 more sources

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A Multivariate Generalization of Probit Analysis

Biometrics, 1981
A generalized form of probit analysis is applied to the problem of the inference of the value of all unobservable continuous variable from quantal variables. The observational units are assumed to belong to known groups that are homogeneous with respect to the value of the continuous variable, and responses within and between units in the same group ...
Kolakowski, Donald, Bock, R. Darrell
openaire   +2 more sources

Estimation of multivariate probit models by exact maximum likelihood [PDF]

, 2009
In this paper, we develop a new numerical method to estimate a multivariate probit model. To this end, we derive a new decomposition of normal multivariate integrals that has two appealing properties. First, the decomposition may be written as the sum of normal multivariate integrals, in which the highest dimension of the integrands is reduced relative
Jacques Huguenin, Florian Pelgrin, Alberto Holly   +2 more
openaire   +1 more source

High-Dimensional Multivariate Probit Analysis

Biometrics, 1996
A computationally practical form of probit analysis for multiple response variables based on an assumed common factor model for the latent tolerances is proposed. Numerical integration over the factor space provides maximum likelihood estimation of the probit regression parameters and of the probabilities of response combinations under the model.
Bock, R. Darrell, Gibbons, Robert D.
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Analysis of multivariate probit models

Biometrika, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chib, Siddhartha, Greenberg, Edward
openaire   +2 more sources