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

DOI10.1016/S0167-7152(99)00187-XzbMath0945.62054OpenAlexW2045106296MaRDI QIDQ1977641

Tadeusz Inglot

Publication date: 19 September 2000

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(99)00187-x

zbMATH Keywords

exponential inequalitydata-driven Neyman statisticmoderate deviation theorem

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Large deviations (60F10)

Related Items (1)

Intermediate efficiency in nonparametric testing problems with an application to some weighted statistics

Cites Work

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• Exponential inequalities for sums of random vectors
• Estimating the dimension of a model
• Asymptotic optimality of data-driven Neyman's tests for uniformity
• A probability inequality for obtaining lower bounds in the large deviation principle
• Data driven smooth tests for composite hypotheses
• Generalized intermediate efficiency of goodness-of-fit tests.
• Consistency and Monte Carlo simulation of a data driven version of smooth goodness-of-fit tests
• On Sums of Random Vectors
• Data-Driven Version of Neyman's Smooth Test of Fit
• Testing Uniformity Via Log-Spline Modeling
• Data-Driven Smooth Tests When the Hypothesis Is Composite

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