-
作者：Fort, G; Moulines, E
作者单位：Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom Paris
摘要：The Monte Carlo expectation maximization (MCEM) algorithm is a versatile tool for inference in incomplete data models, especially when used in combination with Markov chain Monte Carlo simulation methods. In this contribution, the almost-sure convergence of the MCEM algorithm is established. It is shown, using uniform versions of ergodic theorems for Markov chains, that MCEM converges under weak conditions on the simulation kernel. Practical illustrations are presented, using a hybrid random w...
-
作者：Breslow, N; McNeney, B; Wellner, JA
作者单位：University of Washington; University of Washington Seattle; Simon Fraser University; University of Washington; University of Washington Seattle
摘要：Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the p...
-
作者：Cai, TT; Low, MG
作者单位：University of Pennsylvania
摘要：Precise asymptotic descriptions of the minimax affine risks and biasvariance tradeoffs for estimating linear functionals are given for a broad class of moduli. The results are complemented by illustrative examples including one where it is possible to construct an estimator which is fully adaptive over a range of parameter spaces.
