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A sulficient condition for an invariant quadratic estimator of a linear function of the vector of variance components to be admissible under the mean square &or among all translation invariant esti...
We study the nonparametric spline density estimators of probability density. The equivalence of weak convergence for L,-consistency of one density and completely for L,-consistency of all densities...
Minimum L(1)-penalized distance estimators of a density and its derivatives
The suggested ordering of estimators uses the separation measure between the population with true and estimated values of parameter. It allows us ta choose one estimator as better in the cases wher...
M-estimators of regression parameter vector in linear model, studentized by a suitable afine-invariant and scale-equivariant scale statistic, become a n e - and scale-equivariant. We study some asy...
MAP estimators and HPD credible sets are often criticized in the literature because of paradoxical behaviour due to a lack of invariance under reparametrization. In this paper, we propose a new vers...
We propose a class of weighted least-squares estimators for the tail index of a regularly varying upper tail of a distribution. Universal asymptotic normality of the estimators is established over ...
Under the symmetric a-stable distributional assumption for the disturbances, Blattberg and Sargent [3] consider unbiased linear estimators for a regression model with non-stochastic regressors. We ...
Proper Priors Yielding Linear Bayes Estimators for the Natural Parameter of an Exponential Family。
Jackknifing a General Class of Estimators
Kernel Estimators for Semi-Markov Processes
Asymptotic Efficiency of Method of Moments Estimators Under Null Intercept Measurement Error Regression Models。
A Class of Shrinkage Estimators for Variance of a Normal Population。
Bias Corrected Maximum Likelihood Estimators in Nonlinear Overdispersed Models。
When a series of (related) linear models has to be estimated it is often appropriate to combine the different data-sets to construct more efficient estimators. We use ℓ₁-penalized estimato...

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