Algorithms and complexity for least median of squares regression
Discrete Applied Mathematics
SIAM Journal on Scientific Computing
Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions
Statistics and Computing
Weighted quantile-based estimation for a class of transformation distributions
Computational Statistics & Data Analysis
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The g-and-h distributional 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 the g-and-h distributional 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.