Estimation for large non-centrality parameters

Sónia Inácio, João Mexia, Miguel Fonseca, Francisco Carvalho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce the concept of estimability for models for which accurate estimators can be obtained for the respective parameters. The study was conducted for model with almost scalar matrix using the study of estimability after validation of these models. In the validation of these models we use F statistics with non centrality parameter τ=||λ||σ2 when this parameter is sufficiently large we obtain good estimators for λ and α so there is estimability. Thus, we are interested in obtaining a lower bound for the non-centrality parameter. In this context we use for the statistical inference inducing pivot variables, see Ferreira et al. 2013, and asymptotic linearity, introduced by Mexia & Oliveira 2011, to derive confidence intervals for large non-centrality parameters (see Inácio et al. 2015). These results enable us to measure relevance of effects and interactions in multifactors models when we get highly statistically significant the values of F tests statistics.

Original languageEnglish
Title of host publicationInternational Conference of Numerical Analysis and Applied Mathematics 2015, ICNAAM 2015
PublisherAmerican Institute of Physics Inc.
Volume1738
ISBN (Electronic)978-0-7354-1392-4
DOIs
Publication statusPublished - 8 Jun 2016
EventInternational Conference of Numerical Analysis and Applied Mathematics 2015, ICNAAM 2015 - Rhodes, Greece
Duration: 23 Sep 201529 Sep 2015

Conference

ConferenceInternational Conference of Numerical Analysis and Applied Mathematics 2015, ICNAAM 2015
CountryGreece
CityRhodes
Period23/09/1529/09/15

Keywords

  • Asymptotic linearity
  • highly significant F tests
  • measure relevance
  • non-centrality parameters

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