Geometric aspects of robust testing for normality and sphericity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43910817" target="_blank" >RIV/62156489:43110/17:43910817 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/07362994.2016.1273785" target="_blank" >http://dx.doi.org/10.1080/07362994.2016.1273785</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/07362994.2016.1273785" target="_blank" >10.1080/07362994.2016.1273785</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Geometric aspects of robust testing for normality and sphericity

Popis výsledku v původním jazyce

Stochastic robustness of control systems under random excitation motivates challenging developments in geometric approach to robustness. The assumption of normality is rarely met when analyzing real data and thus the use of classic parametric methods with violated assumptions can result in the inaccurate computation of p-values, effect sizes, and confidence intervals. Therefore, quite naturally, research on robust testing for normality has become a new trend. Robust testing for normality can have counterintuitive behavior, some of the problems have been introduced in Stehlík et al. [Chemometrics and Intelligent Laboratory Systems 130 (2014): 98-108]. Here we concentrate on explanation of small-sample effects of normality testing and its robust properties, and embedding these questions into the more general question of testing for sphericity. We give geometric explanations for the critical tests. It turns out that the tests are robust against changes of the density generating function within the class of all continuous spherical sample distributions.

Název v anglickém jazyce

Geometric aspects of robust testing for normality and sphericity

Popis výsledku anglicky

Stochastic robustness of control systems under random excitation motivates challenging developments in geometric approach to robustness. The assumption of normality is rarely met when analyzing real data and thus the use of classic parametric methods with violated assumptions can result in the inaccurate computation of p-values, effect sizes, and confidence intervals. Therefore, quite naturally, research on robust testing for normality has become a new trend. Robust testing for normality can have counterintuitive behavior, some of the problems have been introduced in Stehlík et al. [Chemometrics and Intelligent Laboratory Systems 130 (2014): 98-108]. Here we concentrate on explanation of small-sample effects of normality testing and its robust properties, and embedding these questions into the more general question of testing for sphericity. We give geometric explanations for the critical tests. It turns out that the tests are robust against changes of the density generating function within the class of all continuous spherical sample distributions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA16-07089S" target="_blank" >GA16-07089S: Robustní přístup testování normality chybového členu v ekonometrických modelech</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Stochastic Analysis and Applications

ISSN

0736-2994

e-ISSN

—

Svazek periodika

35

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

511-532

Kód UT WoS článku

000394450600008

EID výsledku v databázi Scopus

2-s2.0-85011710518