The prediction of a spatial variable is of particular importance when analyzing spatial data.
The main objective of this study is to evaluate and compare the performance of several …

Climate change is caused by temperature, rainfall, and wind variation in locations that last a
long time. This change can be described and predicted using a spatial model, one of which …

The purpose of this paper is to treat the prediction problems where a number of observations
are missing to the quarter-plane past of a stationary random field. Our aim is to quantify the …

Nonparametric regression suffers from curse of dimensionality, requiring a relatively large
sample size for accurate estimation beyond the univariate case. In this article, we consider a …

Spatial autoregressive models are usually used for stationary lattice random fields with a
zero or fixed mean. However, many lattice random fields are non-stationary, because they …

Recently, some specific random fields have been defined based on multivariate
distributions. This paper will show that almost all these random fields have a deficiency in …

Forecast combination has been widely applied in various fields since the seminal article of
Bates and Granger (1969). However, these research were focused only on time series data …

The advancement of information technology and the telecommunications network has
revolutionized and generated new obstacles. Today consider an internet connection and the …

This thesis contributes to the scientific community in several aspects. We introduce both
spatial-and spatio-temporal extensions to the family of GARCH and ARMA-GARCH models …

A new solution for the important problem of estimating (interpolating) the missing values of a
second-order stationary random fields is given. It is obtained as an appropriate linear …