Latent variable models for clustered ordinal data.
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[1] M. Piedmonte,et al. Small sample validity of latent variable models for correlated binary data , 1994 .
[2] J. R. Landis,et al. The analysis of longitudinal polytomous data: generalized estimating equations and connections with weighted least squares. , 1993, Biometrics.
[3] Andrea Rotnitzky,et al. Regression Models for Discrete Longitudinal Responses , 1993 .
[4] Y. Qu,et al. Latent Variable Models for Clustered Dichotomous Data with Multiple Subclusters , 1992 .
[5] S. Zeger,et al. Multivariate Regression Analyses for Categorical Data , 1992 .
[6] Louise Ryan,et al. Bivariate Latent Variable Models for Clustered Discrete and Continuous Outcomes , 1992 .
[7] G Molenberghs,et al. Multivariate probit analysis: a neglected procedure in medical statistics. , 1991, Statistics in medicine.
[8] S. Lipsitz,et al. Generalized estimating equations for correlated binary data: Using the odds ratio as a measure of association , 1991 .
[9] R. Prentice,et al. Correlated binary regression with covariates specific to each binary observation. , 1988, Biometrics.
[10] J. Dale. Global cross-ratio models for bivariate, discrete, ordered responses. , 1986, Biometrics.
[11] S. Zeger,et al. Longitudinal data analysis using generalized linear models , 1986 .
[12] K Y Liang,et al. Longitudinal data analysis for discrete and continuous outcomes. , 1986, Biometrics.
[13] P. McCullagh. Regression Models for Ordinal Data , 1980 .
[14] J. Ashford,et al. Multi-variate probit analysis. , 1970, Biometrics.
[15] BIVARIATE PROBIT, LOGIT, AND BURRIT ANALYSIS , 1969 .
[16] Strother H. Walker,et al. Estimation of the probability of an event as a function of several independent variables. , 1967, Biometrika.