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On large deviation theorem for data-driven Neyman's statistic

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  • Inglot, Tadeusz

Abstract

The aim of the paper is to show that for data-driven Neyman's statistic large deviation theorem does not hold. We derive an explicit estimate from below for probabilities of large and moderate deviations. The main tool is a version of a lower exponential inequality recently obtained by Mogulskii.

Suggested Citation

  • Inglot, Tadeusz, 2000. "On large deviation theorem for data-driven Neyman's statistic," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 411-419, May.
    • Handle: RePEc:eee:stapro:v:47:y:2000:i:4:p:411-419
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    References listed on IDEAS

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    1. Yurinskii, V. V., 1976. "Exponential inequalities for sums of random vectors," Journal of Multivariate Analysis, Elsevier, vol. 6(4), pages 473-499, December.
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    Cited by:

    1. Tadeusz Inglot & Teresa Ledwina, 2001. "Intermediate Approach to Comparison of Some Goodness-of-Fit Tests," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(4), pages 810-834, December.

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