Consistent non-Gaussian pseudo maximum likelihood estimators

Maximum-likelihood blind deconvolution: non-white Bernoulli-Gaussian case

Consistent Confidence Intervals for Maximum Pseudolikelihood Estimators

Maximum pseudolikelihood estimation (MPLE) constitutes a computationally efficient and easily implemented alternative to maximum likelihood and simulationbased methods. The MPLE has been shown to be consistent and asymptotically normally distributed in a number of interesting cases. However, the coverage probability of the conventional confidence interval for the MPLE is biased downward. We pro...

Maximum Likelihood Decoding of Convolutional Codes for Non-Gaussian Channels

The Viterbi algorithm plays a fundamental role in the design of receivers for digital communication systems corrupted by Gaussian noise. This algorithm arises as the maximum likelihood sequence detector of the transmitted data symbols in several applications, including equalization for channels subject to intersymbol interference, multiuser communications, and the detection of convolutionally e...

The Convergence of Lossy Maximum Likelihood Estimators

Given a sequence of observations (Xn)n≥1 and a family of probability distributions {Qθ}θ∈Θ, the lossy likelihood of a particular distribution Qθ given the data Xn 1 := (X1,X2, . . . ,Xn) is defined as Qθ(B(X 1 ,D)), where B(Xn 1 ,D) is the distortion-ball of radius D around the source sequence X n 1 . Here we investigate the convergence of maximizers of the lossy likelihood.

Asymptotic Distributions of Quasi-Maximum Likelihood Estimators

Asymptotic properties of MLEs and QMLEs of mixed regressive, spatial autoregressive models are investigated. The stochastic rates of convergence of the MLE and QMLE for such models may be less than the √ n-rate under some circumstances even though its limiting distribution is asymptotically normal. When spatially varying regressors are relevant, the MLE and QMLE of the mixed regressive, autoreg...