On the performance of the flexible maximum entropy distributions within partially adaptive estimation

USTA İ., MERT KANTAR Y.

COMPUTATIONAL STATISTICS & DATA ANALYSIS, cilt.55, sa.6, ss.2172-2182, 2011 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 55 Sayı: 6
  • Basım Tarihi: 2011
  • Doi Numarası: 10.1016/j.csda.2011.01.010
  • Dergi Adı: COMPUTATIONAL STATISTICS & DATA ANALYSIS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2172-2182
  • Anahtar Kelimeler: Partially adaptive estimator, Maximum entropy distribution, Efficiency, Non-normal error term, REGRESSION-MODELS, CROSS-VALIDATION, ROBUST
  • Anadolu Üniversitesi Adresli: Evet

Özet

The partially adaptive estimation based on the assumed error distribution has emerged as a popular approach for estimating a regression model with non-normal errors. In this approach, if the assumed distribution is flexible enough to accommodate the shape of the true underlying error distribution, the efficiency of the partially adaptive estimator is expected to be close to the efficiency of the maximum likelihood estimator based on knowledge of the true error distribution. In this context, the maximum entropy distributions have attracted interest since such distributions have a very flexible functional form and nest most of the statistical distributions. Therefore, several flexible MaxEnt distributions under certain moment constraints are determined to use within the partially adaptive estimation procedure and their performances are evaluated relative to well-known estimators. The simulation results indicate that the determined partially adaptive estimators perform well for non-normal error distributions. In particular, some can be useful in dealing with small sample sizes. In addition, various linear regression applications with non-normal errors are provided. (C) 2011 Elsevier B.V. All rights reserved.