Information Approach for the Change-Point Detection in the Skew Normal Distribution and Its Applications
The skew normal distribution is an extension of the normal distribution allowing for the presence of skewness. Such a distribution can model skewed data in many applied fields such as finance, economics and medical research. The skew distribution family has been studied extensively since it was introduced by Azzalini (1985) for the first time. However, few works have been done on the change-point problem for this distribution family. In this article, we will propose an information approach method based on the Schwarz information criterion (SIC) to detect changes in the parameters of a skew normal distribution. Performance of the proposed test is investigated through simulations under different changes among parameters and various sample sizes. We successfully apply the method to two Latin American emerging market data sets to detect multiple changes.