A central limit theorem for bootstrap sample sums from non-i.i.d. models

Rosalsky, Andrew; Li, Deli

doi:10.1016/j.jspi.2016.08.002

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Implementation Of the Test Statistic1

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2023

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“…(2) For the ave perm R(d, n) given in (12), we consider B = 50 random re-orderings, i.e., bootstrap samples of size n without replacement, and apply Theorem 1 to the average of the Csorgo andNasari 2013 andRosalsky andLi 2017).…”

Section: Implementation Of the Test Statisticmentioning

confidence: 99%

An Exponentiality Test of Fit Based on a Tail Characterization against Heavy and Light-Tailed Alternatives

Karagrigoriou,

Mavrogiannis,

Papasotiriou

et al. 2023

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Log-concavity and log-convexity play a key role in various scientific fields, especially in those where the distinction between exponential and non-exponential distributions is necessary for inferential purposes. In the present study, we introduce a testing procedure for the tail part of a distribution which can be used for the distinction between exponential and non-exponential distributions. The conspiracy and catastrophe principles are initially used to establish a characterization of (the tail part of) the exponential distribution, which is one of the main contributions of the present work, leading the way for the construction of the new test of fit. The proposed test and its implementation are thoroughly discussed, and an extended simulation study has been undertaken to clarify issues related to its implementation and explore the extent of its capabilities. A real data case is also investigated.

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Section: Implementation Of the Test Statisticmentioning

confidence: 99%