Related Experiment Video
Updated: Sep 8, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Impact of missing data on the prediction of random fields
Abdelghani Hamaz1, Ouerdia Arezki1, Farida Achemine1
1Laboratoire de Mathématiques Pures et Appliquées, Mouloud Mammeri University of Tizi-Ouzou, Tizi Ouzou, Algeria.
Abstract:
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 influence of missing values on the prediction by giving the simple bounds for the prediction error variance. These bounds allow to characterize the random fields for which the missing observations do not affect the prediction. Simulation experiments and an application to real data are presented.
Related Concept Videos
Random Error
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Random Variables
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Censoring Survival Data
Survival Tree
Building a Survival Tree
Constructing a...
Random and Systematic Errors

