Flexible Closed Skew Normal Random Field to Analysis Skew Spatial Data

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Karimi Omid; Hosseini Fatemeh

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Journal Paper

Paper Information

Journal: JOURNAL OF STATISTICAL SCIENCES Year:2023 | Volume:17 | Issue:2 Page(s): 371-388

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Information Journal Paper

Title

Flexible Closed Skew Normal Random Field to Analysis Skew Spatial Data

Author(s)

Karimi Omid | Hosseini Fatemeh | Issue Writer Certificate

Keywords

Closed Skew Normal Distribution

Spatial Data

Identifiability

Stationarity

Abstract

Gaussian random field is usually used to model Gaussian Spatial Data. In practice, we may encounter non-Gaussian data that are skewed. One solution to model skew Spatial Data is to use a skew random field. Recently, many skew random fields have been proposed to model this type of data, some of which have problems such as complexity, non-identifiability, and non-stationarity. In this article, a flexible class of closed skew-normal distribution is introduced to construct valid stationary random fields, and some important properties of this class such as Identifiability and closedness under marginalization and conditioning are examined. The reasons for developing valid spatial models based on these skew random fields are also explained. Additionally, the Identifiability of the spatial correlation model based on empirical variogram is investigated in a simulation study with the stationary skew random field as a competing model. Furthermore, spatial predictions using a likelihood approach are presented on these skew random fields and a simulation study is performed to evaluate the likelihood estimation of their parameters.

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