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    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Applied Statistics on 12/02/2016, available online: http://www.tandfonline.com/10.1080/02664763.2015.1117584

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Analysis of means: a generalized approach using R

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Analysis of means : a generalized approach using R. / Pallmann, Philip Steffen; Hothorn, Ludwig A.

In: Journal of Applied Statistics, Vol. 43, No. 8, 2016, p. 1541-1560.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Pallmann, PS & Hothorn, LA 2016, 'Analysis of means: a generalized approach using R', Journal of Applied Statistics, vol. 43, no. 8, pp. 1541-1560. https://doi.org/10.1080/02664763.2015.1117584

APA

Pallmann, P. S., & Hothorn, L. A. (2016). Analysis of means: a generalized approach using R. Journal of Applied Statistics, 43(8), 1541-1560. https://doi.org/10.1080/02664763.2015.1117584

Vancouver

Author

Pallmann, Philip Steffen ; Hothorn, Ludwig A. / Analysis of means : a generalized approach using R. In: Journal of Applied Statistics. 2016 ; Vol. 43, No. 8. pp. 1541-1560.

Bibtex

@article{58d07580a3864e7495efc775b4e35bf7,
title = "Analysis of means: a generalized approach using R",
abstract = "Papers on the analysis of means (ANOM) have been circulating in the quality control literature for decades, routinely describing it as a statistical stand-alone concept. Therefore we clarify that ANOM should rather be regarded as a special case of a much more universal approach known as multiple contrast tests (MCTs). Perceiving ANOM as a grand-mean-type MCT paves the way for implementing it in the opensource software R. We give a brief tutorial on how to exploit R's versatility and introduce R package ANOM for drawing the familiar decision charts. Beyond that, we illustrate two practical aspects of data analysis with ANOM: rstly, we compare merits and drawbacks of ANOM-type MCTs and ANOVA F-test and assess their respective statistical powers, and secondly, we show that the benet of using critical values from multivariate t-distributions for ANOM instead of simple Bonferroni quantiles is oftentimes negligible.",
keywords = "ANOVA F-test, multiple contrast test, multivariate t-distribution, control chart, industrial quality assessment",
author = "Pallmann, {Philip Steffen} and Hothorn, {Ludwig A.}",
note = "This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Applied Statistics on 12/02/2016, available online: http://www.tandfonline.com/10.1080/02664763.2015.1117584",
year = "2016",
doi = "10.1080/02664763.2015.1117584",
language = "English",
volume = "43",
pages = "1541--1560",
journal = "Journal of Applied Statistics",
issn = "0266-4763",
publisher = "Routledge",
number = "8",

}

RIS

TY - JOUR

T1 - Analysis of means

T2 - a generalized approach using R

AU - Pallmann, Philip Steffen

AU - Hothorn, Ludwig A.

N1 - This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Applied Statistics on 12/02/2016, available online: http://www.tandfonline.com/10.1080/02664763.2015.1117584

PY - 2016

Y1 - 2016

N2 - Papers on the analysis of means (ANOM) have been circulating in the quality control literature for decades, routinely describing it as a statistical stand-alone concept. Therefore we clarify that ANOM should rather be regarded as a special case of a much more universal approach known as multiple contrast tests (MCTs). Perceiving ANOM as a grand-mean-type MCT paves the way for implementing it in the opensource software R. We give a brief tutorial on how to exploit R's versatility and introduce R package ANOM for drawing the familiar decision charts. Beyond that, we illustrate two practical aspects of data analysis with ANOM: rstly, we compare merits and drawbacks of ANOM-type MCTs and ANOVA F-test and assess their respective statistical powers, and secondly, we show that the benet of using critical values from multivariate t-distributions for ANOM instead of simple Bonferroni quantiles is oftentimes negligible.

AB - Papers on the analysis of means (ANOM) have been circulating in the quality control literature for decades, routinely describing it as a statistical stand-alone concept. Therefore we clarify that ANOM should rather be regarded as a special case of a much more universal approach known as multiple contrast tests (MCTs). Perceiving ANOM as a grand-mean-type MCT paves the way for implementing it in the opensource software R. We give a brief tutorial on how to exploit R's versatility and introduce R package ANOM for drawing the familiar decision charts. Beyond that, we illustrate two practical aspects of data analysis with ANOM: rstly, we compare merits and drawbacks of ANOM-type MCTs and ANOVA F-test and assess their respective statistical powers, and secondly, we show that the benet of using critical values from multivariate t-distributions for ANOM instead of simple Bonferroni quantiles is oftentimes negligible.

KW - ANOVA F-test

KW - multiple contrast test

KW - multivariate t-distribution

KW - control chart

KW - industrial quality assessment

U2 - 10.1080/02664763.2015.1117584

DO - 10.1080/02664763.2015.1117584

M3 - Journal article

VL - 43

SP - 1541

EP - 1560

JO - Journal of Applied Statistics

JF - Journal of Applied Statistics

SN - 0266-4763

IS - 8

ER -