Likelihood Based Inference and Bias Reduction in the Modified Skew-t-Normal Distribution

In this paper, likelihood-based inference and bias correction based on Firth’s approach are developed in the modified skew-t-normal (MStN) distribution. The latter model exhibits a greater flexibility than skew-normal (MSN) distribution since it is able to heavily skewed data thick tails. addition, tails controlled by shape parameter degrees of freedom. We provide density new present some its more important properties including general expression for moments. Fisher’s information matrix together with observed associated log-likelihood also given. Furthermore, non-singularity MStN demonstrated when zero. As presents an inferential problem parameter, method reduction was applied scalar case location scale case.