Monte Carlo comparison of seven normality tests

Noughabi, Hadi Alizadeh; Arghami, Nasser Reza

doi:10.1080/00949650903580047

Cited by 52 publications

(32 citation statements)

References 18 publications

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“…Ibrahim Al-Omari (2014) proposed three new entropy estimators which are the modification of the estimators propounded by Noughabi & Arghami (2011) and Ebrahimi, Pflughoeft & Soofi (1994). The new estimator of the continuous random variable is introduced by using ranked set sampling (RSS), double ranked set sampling (DRSS) and simple random sampling (SRS) techniques.…”

Section: Introductionmentioning

confidence: 99%

A power comparison of various normality tests

Ahmad¹,

Khan²

2015

Pak.j.stat.oper.res.

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Standard statistical procedures often require data to be normally distributed and the results of these methods will be inappropriate when the assumption of normality is not satisfied. Therefore, the postulation of normality is strictly required before proceeding statistical analysis. Although a number of criteria's have been available to assess the assumption of normality, but these tests do not have the same nature and power to diagnose the departures of a data set from normality, thus the choice of appropriate test always remains a

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Section: Introductionmentioning

confidence: 99%

A power comparison of various normality tests

Ahmad¹,

Khan²

2015

Pak.j.stat.oper.res.

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“…These alternatives were used by [1,3] and [2] in their study of power comparisons of several tests for normality. The alternatives, depending on the support and shape of their densities, can be divided into four groups.…”

Section: The Cramer-von Mises Statistic Ismentioning

confidence: 99%

“…where m is positive integer, m ≤ n 2 , and X (1) ≤ X (2) ≤ · · · ≤ X (n) are the order statistics and X (i) = X (1) …”

Section: Introductionmentioning

confidence: 99%

Informational Energy and Its Application in Testing Normality

Noughabi

Chahkandi

2015

Ann. Data. Sci.

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“…We want to compare the powers among the five graphical tests and the two nongraphical tests AD and SW. The AD and SW tests are very popular due to their availability in many statistical packages and have also been shown to be quite powerful generally by some previous power studies (see, e.g., Noughabi and Arghami, ).…”

Section: The Testsmentioning

confidence: 99%

Normal probability plots with confidence

et al. 2014

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Normal probability plots are widely used as a statistical tool for assessing whether an observed simple random sample is drawn from a normally distributed population. The users, however, have to judge subjectively, if no objective rule is provided, whether the plotted points fall close to a straight line. In this paper, we focus on how a normal probability plot can be augmented by intervals for all the points so that, if the population distribution is normal, then all the points should fall into the corresponding intervals simultaneously with probability 1-α. These simultaneous 1-α probability intervals provide therefore an objective mean to judge whether the plotted points fall close to the straight line: the plotted points fall close to the straight line if and only if all the points fall into the corresponding intervals. The powers of several normal probability plot based (graphical) tests and the most popular nongraphical Anderson-Darling and Shapiro-Wilk tests are compared by simulation. Based on this comparison, recommendations are given in Section 3 on which graphical tests should be used in what circumstances. An example is provided to illustrate the methods.

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Monte Carlo comparison of seven normality tests

Cited by 52 publications

References 18 publications

A power comparison of various normality tests

A power comparison of various normality tests

Informational Energy and Its Application in Testing Normality

Normal probability plots with confidence

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