Notes on the tightness of the hybrid Cramér-Rao lower bound
IEEE Transactions on Signal Processing
Bayesian estimation of network-wide mean failure probability in 3g cellular networks
PERFORM'10 Proceedings of the 2010 IFIP WG 6.3/7.3 international conference on Performance Evaluation of Computer and Communication Systems: milestones and future challenges
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The joint maximum a posteriori-maximum likelihood (JMAP-ML) estimation criterion can serve as an alternative to the maximum likelihood (ML) criterion when estimating parameters from an observed data vector whenever another unobserved data vector is involved. Rather than maximize the probability of the observed data with respect to the parameters, JMAP-ML maximizes the joint probability of the observed and unobserved data with respect to both the unknown parameters and the unobserved data. In this paper, we characterize the relation between the ML and JMAP-ML estimates in the Gaussian case and provide insight into the apparent bias of JMAP-ML. Although JMAP-ML is an inconsistent estimator, we show that with short data records, it is often preferable to ML in terms of both bias and variance. We also identify JMAP-ML as a special case of the deterministic extended least squares (XLS) criterion. We indicate a general relation between a possible maximization algorithm for JMAP-ML and the well-known estimation-maximization (EM) algorithm.