title: Robust Estimation of the Parameters ofg - and - hDistributions, with Applications to Outlier Detection.
abstract: Theg - and - hdistributional family is generated from a relatively simple transformation of the standard normal and can approximate a broad spectrum of distributions. Consequently, it is easy to use in simulation studies and has been applied in multiple areas, including risk management, stock return analysis and missing data imputation studies. A rapidly convergent quantile based least squares (QLS) estimation method to fit theg - and - hdistributional family parameters is proposed and then extended to a robust version. The robust version is then used as a more general outlier detection approach. Several properties of the QLS method are derived and comparisons made with competing methods through simulation. Real data examples of microarray and stock index data are used as illustrations.
authors.affiliation: ImClone LLC, A Wholly Owned Subsidiary of Eli Lilly and Company, 440 Route 22, Bridgewater, NJ 08807.
authors.forename: Yihuan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Department of Statistics, The Fox School of Business, Temple University, 1810 North 13 Street 06-012, Philadelphia, PA 19122.
authors.forename: Boris
authors.initials: B
authors.lastname: Iglewicz
authors.affiliation: Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, 1015 Chestnut St, Suite M100, Philadelphia, PA 19107.
authors.forename: Inna
authors.initials: I
authors.lastname: Chervoneva
keywords: g - and - h distribution; indirect inference; least squares; maximum likelihood; outlier detection; quantiles; robust