Multilevel logistic regression for polytomous data and rankings

By A. Skrondal, Sophia Rabe-Hesketh. 2003

190 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Genetics

View Paper: https://www.semanticscholar.org/paper/8a69c02c1588a563e58f4048744a1bb248d995fe

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Citations in the corpus

The tragedy of the commons in international fisheries: an empirical examination. (2009, methodology): “…Average Risk Ratingilt, High Seas Dummyilt, Y ear Dummy) With the data as an unbalanced two-period panel I also include a dummy variable for the later period and use the GLLAMM programme (Skrondal and Rabe-Hesketh 2003) for Stata 8 (2003) to estimate a random-effects panel ordered probit.”
Multilevel modelling of complex survey data (2006, background): “Responses can be continuous, dichotomous, ordinal or nominal variables (Skrondal and Rabe-Hesketh, 2003), as well as counts and durations.”
Structural Equation Modeling: Categorical Variables (2005, unknown): “, [1], [2], [4], [9], [11], [12] and [13] for theory and applications.”
Multilevel Models for Ordinal and Nominal Variables (2008, methodology): “More general regression models of multilevel nominal data have been considered by Daniels and Gatsonis [25], Revelt and Train [88], Bhat [13], Skrondal and Rabe-Hesketh [97], and in Goldstein [38, chap.”
Latent Variable Modelling: A Survey * (2007, methodology): “For polytomous responses or discrete choices and rankings, a multinomial logit link is used (Skrondal & Rabe-Hesketh, 2003) which is an extension of a generalized linear model.”
Reliable Estimation of Generalized Linear Mixed Models using Adaptive Quadrature (2002, background): “In addition to counts and dichotomous outcomes, gllamm can handle continuous, censored, ordinal, and nominal responses and rankings, see Skrondal and Rabe-Hesketh (2002), as well as continuous and discrete time survival data, see Rabe-Hesketh et al. (2001d).”
Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects (2005, background): “See Skrondal and Rabe-Hesketh (2003) for a treatment of multilevel random effects models for discrete choices and rankings.”
Maximum Likelihood Estimation of Generalized Linear Models with Covariate Measurement Error (2003, unknown): “The gllamm command is also useful for other problems involving latent variables, such as multilevel random effects models, multilevel structural equation models (including factor and item response models), latent class models and multilevel selection and endogenous treatment models, see Skrondal and”
Generalized multilevel structural equation modeling (2004, background): “Finally, composite links can be useful for specifying proportional odds models for right-censored responses, for handling missing categorical covariates and many other model types; see Skrondal and Rabe-Hesketh (2004b).”
Handbook of multilevel analysis (2008, background): “The two major research groups in educational statistics led, respectively, by Goldstein and by Raudenbush produced and maintained major software packages [97, 102].”
Correcting for covariate measurement error in logistic regression using nonparametric maximum likelihood estimation (2003, background): “Currently, gllamm allows continuous, dichotomous, ordered and unordered categorical responses to be modelled, as well as rankings (Skrondal and Rabe-Hesketh, 2003), counts and continuous or discrete durations (Rabe-Hesketh et al., 2001c), see also Skrondal and Rabe-Hesketh (2004) for application.”