A variance analysis by an adaptive maximum likelihood approach

A. Azzedine; F.Z. Nouri; S. Bouhouche

Author(s)

A. Azzedine Mathematical Modeling and Numerical Simulation Research Laboratory, Mathematics Department, Badji Mokhtar University, PO. Box 12, Annaba- Algeria

F.Z. Nouri Mathematical Modeling and Numerical Simulation Research Laboratory, Mathematics Department, Badji Mokhtar University, PO. Box 12, Annaba- Algeria

S. Bouhouche Research Center of Industrial Technologies- CRTI PO. Box 64, Cheraga 16014 Algiers - Algeria

Abstract

This paper is focused on a method based on variance analysis using a mixed model and quality control data. For this purpose, statistic models, so called analysis of variances (ANOVA) are widely used to estimate variances of experiment data, provided from a real measurement process. It is based on an experiment plan as defined in the international organization for standardization (ISO 5725-2 standard) with a recommended new version to improve the computational procedure via a new concept based on a mixed model; by combining the deterministic and the statistic statement of the measurement process. The objective of this work is to develop and implement a mixed model based on the recursive least square (RLS) algorithm together with the residual maximum likelihood estimation (REML) and a new adaptive form of REML technique; using quality control data from an accredited laboratory for agro-industry. The novelty here is to use a sliding window strategy that is based on a computational technique considering a subset of the time series elements rather than the whole data. This window advances consecutively, keeping the same number of elements in the window as it moves along the time series data.

Publication Details

Page(s) 1-17

DOI DOI not available

Published Journal: Journal of Applied Probability and Statistics, Volume: 18, Issue: 2, Year: 2023

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Keywords

and phrases, mixed model, Quality Control and Proficiency testing, Recursive Least Square and ANOVA methods, Residual Maximum Likelihood