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.

We consider estimation in a particular semiparametric regression model for the mean of a counting process under the assumption of “panel count” data. The basic model assumption is that the conditional mean function of the counting process is of the form E{N(t)|Z} = exp(θ′Z)Λ(t) where Z is a vector of covariates and Λ is the baseline mean function. The “panel count” observation scheme involves o...

this article examines statistical inference for  where and are independent but not identically distributed pareto of the first kind (pareto (i)) random variables with same scale parameter but different shape parameters. the maximum likelihood, uniformly minimum variance unbiased and bayes estimators with gamma prior are used for this purpose. simulation studies which compare the estimators are ...

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...

Voting is a very general method of preference aggregation. A voting rule takes as input every voter’s vote (typically, a ranking of the alternatives), and produces as output either just the winning alternative or a ranking of the alternatives. One potential view of voting is the following. There exists a “correct” outcome (winner/ranking), and each voter’s vote corresponds to a noisy perception...

Images of the MRI signal intensity are normally constructed by taking the magnitude of the complex-valued data. This results in a biased estimate of the true signal intensity. We consider this as a problem of parameter estimation with a nuisance parameter. Using several standard techniques for this type of problem, we derive a variety of estimators for the MRI signal, some previously published ...

The maximum likelihood estimators, based on Type-II censored samples, of a twoparameter Birnbaum-Saunders distribution are discussed. We propose a simple biasreduction method to reduce the bias of the maximum likelihood estimators. We also discuss a Monte Carlo EM-algorithm for the determination of the maximum likelihood estimators. Monte Carlo simulation is used to compare the performance of a...