By Ludwig Fahrmeir, Brian Francis, Robert Gilchrist, Gerhard Tutz
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Additional info for Advances in GLIM and Statistical Modelling: Proceedings of the GLIM92 Conference and the 7th International Workshop on Statistical Modelling, Munich, 13–17 July 1992
The estimation is given through the optimization problem max L t/>;(v;, 5;, 0', (3, e) P,e, Ot i:;;:;l with Uk; = E(Vk; I R\" 00', R~), k::::; n;, with E(Vk; I R\"oo. ,R~) the conditional expectation of with respect to the multivariate distribution function given the generalized rank vectors. ha) I ",,~I-H"nld(Aolta) AOI=E~~l iikleke"'lzkl (see Brecht (1991)). The estimation is carried out iteratively, and n~ is the generalized risk-set according to the generalized rank vector. For the iterative procedure a starting value is necessary and is given by the 39 expression based on an independent multi-episode model.
Model fitting in GLIM4. In Proceedings of GUM92 and the 7th International Workshop on Statistical Modelling, Lecture Notes in Statistics, 78, Springer Verlag, Berlin. Iyer, R. 1985. Continuation-odds models in ordinal variable regression. GUM Newsletter, 10, 4-8. McCullagh, P. 1980. Regression models for ordinal data (with discussion). J. Roy. Statist. Soc. B, 43,109-142. O. Box 5560, FRG-7750 Konstanz ABSTRACT: This contribution is concerned with the statistical analysis of multi-episode models.
S. (1989). Errors in variables regression using Stein estimates. American Statistician, 43, 226-228. Whittemore, A. S. & Keller, J. B. (1988). Approximations for regression with covariate measurement error. Journal of the American Statistical Association, 83, 1057-1066. Zeger, S. L. & Karim, M. R. (1991). Generalized linear models with random effects: a Gibbs sampling approach. Journal of the American Statistical Association, 86, 79-86. THE FRONTIT MODEL: A STOCHASTIC FRONTIER FOR DICHOTOMIC RANDOM VARIABLES Roberto Colombi Istituto di Statistica Universita Cattolica del Sacra euore Milano - Italy I-summaryl This paper is aimed to generalize the Stochastic Frontier Model to the case of dichotomic response variables.