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B.G. Lindsay 95ReferencesBahadur, R.R. (1971). Some Limit Theorems in Statistics. SocietyforIndustrialand Applied Mathematics, Philadelphia, PA.Breslow, N.E. and Day, N.E. (1980). Statistical Methods in Cancer Research.Vol. 1. The Analysis of Case-control Studies, vol. 1. Distributed for IARCby the World Health Organization, Geneva, Switzerland.Cox, D.R. (1972). Regression models and life-tables (with discussion). Journalof the Royal Statistical Society, Series B, 34:187–220.Cox, D.R. (1975). Partial likelihood. Biometrika, 62:269–276.Dempster, A.P., Laird, N.M., and Rubin, D.B. (1977). Maximum likelihoodfrom incomplete data via the EM algorithm. Journal of the Royal StatisticalSociety, Series B, 39:1–38.Efron, B. (1977). The efficiency of Cox’s likelihood function for censoreddata. Journal of the American Statistical Association, 72:557–565.Efron, B. (1979). Bootstrap methods: Another look at the jackknife. TheAnnals of Statistics, 7:1–26.Kiefer, J. and Wolfowitz, J. (1956). Consistency of the maximum likelihoodestimator in the presence of infinitely many incidental parameters. TheAnnals of Mathematical Statistics, 27:887–906.Laird, N.M. (1978). Nonparametric maximum likelihood estimation of a mixingdistribution. Journal of the American Statistical Association, 73:805–811.Lindsay, B.G. (1980). Nuisance parameters, mixture models, and the efficiencyof partial likelihood estimators. Philosophical Transactions of theRoyal Society of London, Series A, 296:639.Lindsay, B.G. (1983). The geometry of mixture likelihoods: A general theory.The Annals of Statistics, 11:86–94.Lindsay, B.G., Clogg, C.C., and Grego, J. (1991). Semiparametric estimationin the Rasch model and related exponential response models, includingasimplelatentclassmodelforitemanalysis.Journal of the AmericanStatistical Association, 86:96–107.Naylor, J.C. and Smith, A.F.M. (1982). Applications of a method for theefficient computation of posterior distributions. Applied Statistics,31:214–225.

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