:: Volume 15, Issue 2 (3-2022) ::
JSS 2022, 15(2): 329-340 Back to browse issues page
Construction a Non-parametric Prediction Model for Spatial Random Field Using Projection Theorem
Issac Almasi, Mehdi Omidi *
Abstract:   (1726 Views)

Identifying the best prediction of unobserved observation is one of the most critical issues in spatial statistics. In this line, various methods have been proposed, that each one has advantages and limitations in application. Although the best linear predictor is obtained according to the Kriging method, this model is applied for the Gaussian random field. The uncertainty in the distribution of random fields makes researchers use a method that makes the nongaussian prediction possible. In this paper, using the Projection theorem, a non-parametric method is presented to predict a random field. Then some models are proposed for predicting the nongaussian random field using the nearest neighbors. Then, the accuracy and precision of the predictor will be examined using a simulation study. Finally, the application of the introduced models is examined in the prediction of rainfall data in Khuzestan province.

Keywords: Non-Gaussian Predictor, Random Field, Kriging, Projection Theorem.
Full-Text [PDF 243 kb]   (873 Downloads)    
Type of Study: Applied | Subject: Spatial Statistics
Received: 2021/04/8 | Accepted: 2022/03/1 | Published: 2021/10/4

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Volume 15, Issue 2 (3-2022) Back to browse issues page