Stochastic analysis of covariance when the error distribution is long-tailed symmetric

KASAP P., ŞENOĞLU B., ARSLAN O.

JOURNAL OF APPLIED STATISTICS, vol.43, no.11, pp.1977-1997, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 11
  • Publication Date: 2016
  • Doi Number: 10.1080/02664763.2015.1125866
  • Journal Name: JOURNAL OF APPLIED STATISTICS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1977-1997
  • Keywords: ANCOVA, stochastic covariate, long-tailed symmetric, robustness, iteratively reweighting algorithm, BIVARIATE NONNORMAL DISTRIBUTIONS, ESTIMATING PARAMETERS, ROBUST ESTIMATION, CENSORED SAMPLES, SHAPE PARAMETER, REGRESSION, DESIGN, ESTIMATORS, VARIANCE, LOCATION
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.