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Testing the Equality of Scale Parameters against Restrictive Alternatives with Optimal Choice of Weights

Manish Goyal, Narinder Kumar

Abstract



Testing the equality of population parameters is one of the basic problems in Natural Sciences, Social Sciences and Formal Sciences. For testing the equality of populations, generally two types of population parameters are studied as location and scale parameter in nonparametric
inference. The problem related with scale parameter arises, when experimenter’s interest is to know whether populations follow the same distribution or there is difference in their scale parameters. This type of problem is usually encountered in Agriculture, Engineering, Business, Trade, Industries, and Medicine. Any consistent method always has a preference over other methods, so testing of scale parameter is used in practice. For testing the equality of scale parameters, we propose two classes of nonparametric tests when the common quantile may be
different from median. The proposed classes of tests performs better than or as good as the relative tests. The application of the proposed classes of tests is illustrated using real life data set. Statistical power of the proposed classes of tests is computed using Monte Carlo simulation study.

Keywords


Nonparametric inference, Scale parameters, Common quantile.

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