LAD, LASSO and related strategies in regression models

YÜZBAŞI B., Ahmed S. E., Arashi M., Norouzirad M.

13th International Conference on Management Science and Engineering Management, ICMSEM 2019, St. Catharines, Kanada, 5 - 08 Ağustos 2019, cilt.1001, ss.429-444 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 1001
  • Doi Numarası: 10.1007/978-3-030-21248-3_32
  • Basıldığı Şehir: St. Catharines
  • Basıldığı Ülke: Kanada
  • Sayfa Sayıları: ss.429-444
  • Anahtar Kelimeler: LAD estimator, LAD-LASSO estimator, Outliers, Soft and hard thresh-holdings, SHRINKAGE, SELECTION, ESTIMATORS
  • İnönü Üniversitesi Adresli: Evet

Özet

© Springer Nature Switzerland AG 2020.In the context of linear regression models, it is well-known that the ordinary least squares estimator is very sensitive to outliers whereas the least absolute deviations (LAD) is an alternative method to estimate the known regression coefficients. Selecting significant variables is very important; however, by choosing these variables some information may be sacrificed. To prevent this, in our proposal, we can combine the full model estimates toward the candidate sub-model, resulting in improved estimators in risk sense. In this article, we consider shrinkage estimators in a sparse linear regression model and study their relative asymptotic properties. Advantages of the proposed estimators over the usual LAD estimator are demonstrated through a Monte Carlo simulation as well as a real data example.