An integrated selection formulation for the best normal mean: The unequal and unknown variance case.
Chen, Pinyuen, Zhang, Jun-Lue (2002)
Journal of Applied Mathematics and Decision Sciences
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Chen, Pinyuen, Zhang, Jun-Lue (2002)
Journal of Applied Mathematics and Decision Sciences
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Hutson, Alan D. (2002)
Journal of Applied Mathematics and Decision Sciences
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Sarhan, Ammar M., Zaindin, Mazen (2009)
APPS. Applied Sciences
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Wojciech Niemiro (1993)
Applicationes Mathematicae
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We consider the empirical risk function (for iid ’s) under the assumption that f(α,z) is convex with respect to α. Asymptotics of the minimum of is investigated. Tests for linear hypotheses are derived. Our results generalize some of those concerning LAD estimators and related tests.
Tomasz Rychlik (1995)
Applicationes Mathematicae
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We propose a class of unbiased and strongly consistent nonparametric kernel estimates of a probability density function, based on a random choice of the sample size and the kernel function. The expected sample size can be arbitrarily small and mild conditions on the local behavior of the density function are imposed.
Szabó, Zoltán (2006)
Acta Mathematica Academiae Paedagogicae Nyí regyháziensis. New Series [electronic only]
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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.
Wong, Wing-Keung, Bian, Guorui (2000)
Journal of Applied Mathematics and Decision Sciences
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Mahdi, Smail (2000)
Matematichki Vesnik
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Quiroz, A.J., Tapia, J.M. (2007)
Divulgaciones Matemáticas
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Przemysław Grzegorzewski (1995)
Applicationes Mathematicae
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Nonparametric tests for the two-sample location problem are investigated. It is shown that the supremum of the size of any test can be arbitrarily close to 1. None of these tests is most robust against dependence.