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Nonparametric Phase-II monitoring for detecting monotone trend based on inverse sampling

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Abstract

Recently, Mukherjee and Bandyopadhyay (J Stat Plan Inference, 2011, doi:10.1016/j.jspi.2011.02.017) introduced some partially sequential tests for detecting liner trend among the incoming series of observations when a training sample is available a-priori. Their work is very useful in econometric or environmental monitoring under certain situations. The present work is intended for generalization of their tests for any monotone trend. We develop two nonparametric tests for the identity of some unknown univariate continuous distribution functions against monotone or unidirectional trend in location. One of these two tests is based on usual ranks and the other is based on sequential ranks. These are typical nonparametric tests for monitoring structural changes. Performance of the two tests are compared using asymptotic studies as well as through some numerical results based on Monte-Carlo simulations. An illustration is offered using a real data on monthly production of certain beverage.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Madras, Chennai, 600036, India

    Amitava Mukherjee

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  1. Amitava Mukherjee
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Correspondence to Amitava Mukherjee.

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Mukherjee, A. Nonparametric Phase-II monitoring for detecting monotone trend based on inverse sampling. Stat Methods Appl 22, 131–153 (2013). https://doi.org/10.1007/s10260-012-0210-7

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