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On testing a shape parameter in the presence of a location and a scale parameter

M.A.J. van Montfort, A. Otten

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Abstract

A test on a shape parameter in the presence of two nuisance parameters (a location parameter μ and a scale parameter σ)is proposed. Explicit formulas and approximations are given for three applications where the general and restricted distributions are:Student-vs-Gauss-distribution, λ-vs logistic distribution, and type Iior III vs. type I distribution of largest extremes. The last application can be translated in to the situation of smallest extremes;a numerical example is given in which the hypothesis to be tested states that the data come from a 2-parameter weibull distribution. Critical values of the test and its power for some alternatives are obtained by simulation methods.

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What this paper is about

A test on a shape parameter in the presence of two nuisance parameters (a location parameter μ and a scale parameter σ)is proposed. Explicit formulas and approximations are given for three applications where the general and restricted distributions are:Student-vs-Gauss-distribution, λ-vs logistic distribution, and type Iior III vs. type I distribution of largest extremes. The last application can be translated in to the situation of smallest extremes;a numerical example is given in which the hypothesis to be tested states that the data come from a 2-parameter weibull distribution. Critical values of the test and its power for some alternatives are obtained by simulation methods.

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Available abstract

A test on a shape parameter in the presence of two nuisance parameters (a location parameter μ and a scale parameter σ)is proposed. Explicit formulas and approximations are given for three applications where the general and restricted distributions are:Student-vs-Gauss-distribution, λ-vs logistic distribution, and type Iior III vs. type I distribution of largest extremes. The last application can be translated in to the situation of smallest extremes;a numerical example is given in which the hypothesis to be tested states that the data come from a 2-parameter weibull distribution. Critical values of the test and its power for some alternatives are obtained by simulation methods.

Key concepts: Weibull distribution, Shape parameter, Scale parameter, Nuisance parameter, Mathematics, Scale (ratio), Distribution (mathematics), Location parameter

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