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With the progress of information technology, large amounts of asymmetric,leptokurtic and heavy-tailed data are arising in various fields, such asfinance, engineering, genetics and medicine. It is very challenging to modelthose kinds of data, especially for extremely skewed data, accompanied by veryhigh kurtosis or heavy tails. In this paper, we propose a class of novel skewedgeneralized t distribution (SkeGTD) as a scale mixture of skewed generalizednormal. The proposed SkeGTD has excellent adaptiveness to various data, becauseof its capability of allowing for a large range of skewness and kurtosis andits compatibility of the separated location, scale, skewness and shapeparameters. We investigate some important properties of this family ofdistributions. The maximum likelihood estimation, L-moments estimation andtwo-step estimation for the SkeGTD are explored. To illustrate the usefulnessof the proposed methodology, we present simulation studies and analyze two realdatasets.

On a Novel Skewed Generalized t Distribution: Properties, Estimations and its Applications