Displaying similar documents to “Monte Carlo detection of outliers of data and cryptographic applications.”

Estimation of nuisance parameters for inference based on least absolute deviations

Wojciech Niemiro (1995)

Applicationes Mathematicae

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Statistical inference procedures based on least absolute deviations involve estimates of a matrix which plays the role of a multivariate nuisance parameter. To estimate this matrix, we use kernel smoothing. We show consistency and obtain bounds on the rate of convergence.

On estimation of parameters in the bivariate linear errors-in-variables model

Anna Czapkiewicz (1999)

Applicationes Mathematicae

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We discuss some methods of estimation in bivariate errors-in-variables linear models. We also suggest a method of constructing consistent estimators in the case when the error disturbances have the normal distribution with unknown parameters. It is based on the theory of estimating variance components in linear models. A simulation study is presented which compares this estimator with the maximum likelihood one.

Least empirical risk procedures in statistical inference

Wojciech Niemiro (1993)

Applicationes Mathematicae

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We consider the empirical risk function Q n ( α ) = 1 n i = 1 n · f ( α , Z i ) (for iid Z i ’s) under the assumption that f(α,z) is convex with respect to α. Asymptotics of the minimum of Q n ( α ) is investigated. Tests for linear hypotheses are derived. Our results generalize some of those concerning LAD estimators and related tests.

Minimax Prediction for the Multinomial and Multivariate Hypergeometric Distributions

Alicja Jokiel-Rokita (1998)

Applicationes Mathematicae

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A problem of minimax prediction for the multinomial and multivariate hypergeometric distribution is considered. A class of minimax predictors is determined for estimating linear combinations of the unknown parameter and the random variable having the multinomial or the multivariate hypergeometric distribution.