Construction of a Control Chart Using SSM for Multivariate t Distribution Data

Main Article Content

Hayfa Abdul Jawad Saieed
Delshad Shaker Ismail Botani
Adnan Mustafa Hussein AL-Sinjary

Abstract

The main purpose of this research is to construct a chart for controlling the mean and variance together of a data distributed multivariate t distribution using State Space Model (SSM) through applying Bayes' Factors (BF). The constructed control chart will undertake the case when parameters' vector is unknown and variance matrix is known. Then, drawing the series of these factors on the univariate modified EWMA chart after assuming that the series has an ARMA (1,1) model. Finally, applying assumed model on simulated data. 

Article Details

How to Cite
Hayfa Abdul Jawad Saieed, Delshad Shaker Ismail Botani, & Adnan Mustafa Hussein AL-Sinjary. (2023). Construction of a Control Chart Using SSM for Multivariate t Distribution Data. Tikrit Journal of Pure Science, 22(3), 156–165. https://doi.org/10.25130/tjps.v22i3.725
Section
Articles

References

[1] Pan, Xia and Jarrett Jeffrey, (2004), “Applying state space to SPC: monitoring multivariate time series”, Journal of Applied Statistics, Vol. 31, pp. 397-418. [2] Triantafyllopoulos, Kostas, (2006), “Multivariate control charts based on Bayesian state space models”, Quality and Reliability Engineering International, Vol. 22, pp. 693–707.

[3] Smith, Adrian and West, Mike, (1983), “Monitoring rental transplants: An application of the multiprocess Kalman filter”, Biometrics, Vol. 39, pp. 867-878.

[4] West, Mike, (1986), “Bayesian Model Monitoring”, Royal Statistical Society, Vol. 48, pp. 70-78.

[5] Jalil, Talib Sharif, (1988), “Sequential inspection and intervention policy for a manufacturing process”, Unpublished Ph. D. Thesis, Dept. of Mathematics, Wales University.

[9] Salvador, Manuel and Gargallob, Pilar, (2004), “Automatic Monitoring and Intervention in Multivariate Dynamic Linear Models”, Computational Statistics & Data Analysis, Vol. 47, pp. 401 – 431.

[11] Sorensen, Daniel and Gianola, Daniel, (2002), “ Likelihood, Bayesian, and MCMC methods in quantitative genetics”, Springer-Verlag, New York.

[12] Kotz, Samuel and Nadarajah, Saralees, (2004), “Multivariate t distributions and their applications”, cambridge university press, U.K. [13] Kanjilal, Partha Pratim, (1995), “Adaptive prediction and predictive control”, Peter Peregrinus Ltd., London.

[14] Akaike, Hirotugu, (1974), “Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes” Annals of the Institute of Statistical Mathematics, Vol. 26, pp.363-387. [15] Grewal , Mohinder S. and Andrews, Angus P., (2008), “Kalman filtering: theory and practice using MATLAB”, 3rd ed., John Wiley & Sons, Inc. .

[16] West, Mike, and Harrison, Jeff, (1997), “Bayesian forecasting and dynamic models”, 2th ed., Springer-Verlag, New York.

[17] Box, George and Tiao, George, (1973), “Bayesian inference in statistical analysis”, Addison – Wesley Publishing Company, London, U.K .

[18] Kass, Robert E. and Raftery, Adrian E., (1995), “Bayes factors”, Journal of the American Statistical Association, Vol. 90, pp. 773-795.

[19] Montgomery, Douglas C., (2009),“ Introduction to statistical quality control ”, 6th ed., John Wiley & Sons, Inc. . [20] Schmid, Wolfgang, (1997), “On EWMA charts for time series. in frontiers in statistical quality control”, Vol. 5, pp. 114-137.