Notes on Power of Normality Tests of Error Terms in Regression Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F15%3A43906560" target="_blank" >RIV/62156489:43110/15:43906560 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1063/1.4912950" target="_blank" >http://dx.doi.org/10.1063/1.4912950</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1063/1.4912950" target="_blank" >10.1063/1.4912950</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Notes on Power of Normality Tests of Error Terms in Regression Models

Popis výsledku v původním jazyce

Normality is one of the basic assumptions in applying statistical procedures. For example in linear regression most of the inferential procedures are based on the assumption of normality, i.e. the disturbance vector is assumed to be normally distributed.Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Thus, error terms should be normally distributed in order to allow us to make exact inferences. As a consequence, normally distributed stochastic errors are necessary in order to make a not misleading inferences which explains a necessity and importance of robust tests of normality. Therefore, the aim of this contribution is to discuss normality testing oferror terms in regression models. In this contribution, we introduce the general RT class of robust tests for normality, and present and discuss the trade-off between power and robustness of selected classical and robust normality tests

Název v anglickém jazyce

Notes on Power of Normality Tests of Error Terms in Regression Models

Popis výsledku anglicky

Normality is one of the basic assumptions in applying statistical procedures. For example in linear regression most of the inferential procedures are based on the assumption of normality, i.e. the disturbance vector is assumed to be normally distributed.Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Thus, error terms should be normally distributed in order to allow us to make exact inferences. As a consequence, normally distributed stochastic errors are necessary in order to make a not misleading inferences which explains a necessity and importance of robust tests of normality. Therefore, the aim of this contribution is to discuss normality testing oferror terms in regression models. In this contribution, we introduce the general RT class of robust tests for normality, and present and discuss the trade-off between power and robustness of selected classical and robust normality tests

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2014 (ICNAAM 2014)

ISBN

978-0-7354-1287-3

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

—

Název nakladatele

American Institute of Physics (AIP)

Místo vydání

Melville

Místo konání akce

Rhodes

Datum konání akce

22. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000355339704080