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We propose a quantile--based method to estimate the parameters (i.e. locations, dispersions, co--dispersions and the tail index) of an elliptical distribution, and a battery of tests for model adequacy. The method is suitable for vast dimensions since the estimators for the location vector and...
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Several estimators of the expectation, median and mode of the lognormal distribution are derived. They aim to be approximately unbiased, efficient, or have a minimax property in the class of estimators we introduce. The small-sample properties of these estimators are assessed by simulations and,...
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induced by sampling errors. We show that the LDWB replicates first and second-order limit theory from the usual empirical … process and the stochastic scale estimate, respectively, as well as an asymptotic bias. Moreover, we design the LDWB … here, up to second-order distribution theory. Finally, we introduce LDWB-aided Kolmogorov-Smirnov tests for local …
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distributional setting, and suggest the two-scale distributional nearest neighbors (DNN) estimator with reduced finite-sample bias … regularity conditions. We then proceed with combining DNN estimators with different subsampling scales to further reduce bias …
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