Displaying similar documents to “On a two-sample procedure for testing Student's hypothesis using mean range”

Bootstrap method for central and intermediate order statistics under power normalization

Haroon Mohamed Barakat, E. M. Nigm, O. M. Khaled (2015)

Kybernetika

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It has been known for a long time that for bootstrapping the distribution of the extremes under the traditional linear normalization of a sample consistently, the bootstrap sample size needs to be of smaller order than the original sample size. In this paper, we show that the same is true if we use the bootstrap for estimating a central, or an intermediate quantile under power normalization. A simulation study illustrates and corroborates theoretical results.

On two families of tests for normality with empirical description of their performances

Dominik Szynal, Waldemar Wołyński (2014)

Discussiones Mathematicae Probability and Statistics

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We discuss two families of tests for normality based on characterizations of continuous distributions via order statistics and record values. Simulations of their powers show that they are competitive to widely recommended tests in the literature.

Tests for profile analysis based on two-step monotone missing data

Mizuki Onozawa, Sho Takahashi, Takashi Seo (2013)

Discussiones Mathematicae Probability and Statistics

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In this paper, we consider profile analysis for the observations with two-step monotone missing data. There exist three interesting hypotheses - the parallelism hypothesis, level hypothesis, and flatness hypothesis - when comparing the profiles of some groups. The T²-type statistics and their asymptotic null distributions for the three hypotheses are given for two-sample profile analysis. We propose the approximate upper percentiles of these test statistics. When the data do not have...

Five Turning Points in the Historical Progress of Statistics - My Personal Vision

von Collani, Elart (2014)

Serdica Journal of Computing

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Statistics has penetrated almost all branches of science and all areas of human endeavor. At the same time, statistics is not only misunderstood, misused and abused to a frightening extent, but it is also often much disliked by students in colleges and universities. This lecture discusses/covers/addresses the historical development of statistics, aiming at identifying the most important turning points that led to the present state of statistics and at answering the questions “What went...