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Machine Learning-based Control Charts with Variable Parameters for GLM Profiles

Vitos, Konstantinos LU (2026) STAN40 20261
Department of Statistics
Abstract
This thesis investigates machine learning-based control charts with Variable Pa-
rameters(VP)formonitoringGeneralizedLinearModel(GLM)profiles. Previous
studies have examined machine learning control charts under Fixed Parameters
(FP) schemes for several GLM distributions, while VP schemes have mainly been
studied for Binomial profiles with logit link. This thesis extends the VP frame-
work to two additional GLM settings: Poisson profiles with log link and Gamma
profiles with log link.
Three machine learning methods are considered: Neural Networks, XGBoost,
and Support Vector Regression. For each method, FP and VP control charts are
calibrated using simulated in-control profiles, and their performance is evaluated
under intercept... (More)
This thesis investigates machine learning-based control charts with Variable Pa-
rameters(VP)formonitoringGeneralizedLinearModel(GLM)profiles. Previous
studies have examined machine learning control charts under Fixed Parameters
(FP) schemes for several GLM distributions, while VP schemes have mainly been
studied for Binomial profiles with logit link. This thesis extends the VP frame-
work to two additional GLM settings: Poisson profiles with log link and Gamma
profiles with log link.
Three machine learning methods are considered: Neural Networks, XGBoost,
and Support Vector Regression. For each method, FP and VP control charts are
calibrated using simulated in-control profiles, and their performance is evaluated
under intercept shifts, slope shifts, and simultaneous shifts. FP performance is
measured using Average Run Length, while VP performance is measured using
Average Time to Signal.
The results show that VP schemes often reduce detection time compared
with FP schemes, but the size of the improvement depends on the distribution,
model, and shift size. For Poisson profiles, VP provides clear improvements for
Neural Networks and Support Vector Regression, especially at moderate shifts.
For Gamma profiles, the main VP advantage appears at very small shifts, since
larger shifts are usually detected almost immediately. Neural Networks and Sup-
portVectorRegressionshowstableperformanceinmostsettings, althoughNeural
Networks under the first training method show large-shift instability for Gamma
profiles. XGBoost is less competitive overall and appears more sensitive to cal-
ibration and tied prediction values. Overall, the thesis demonstrates that VP
schemes can improve machine learning-based GLM profile monitoring beyond
the previously studied Binomial case, particularly when the shift is not already
detected almost immediately by the FP chart. (Less)
Please use this url to cite or link to this publication:
author
Vitos, Konstantinos LU
supervisor
organization
course
STAN40 20261
year
type
H1 - Master's Degree (One Year)
subject
keywords
Neural Network, XGBoost, Support Vector Regression, GLM, Control Charts, Variable Parameters, Fixed Parameters, Profile Monitoring, Statistical Quality Control, Average Run Length, Poisson, Gamma, Log link
language
English
id
9231279
date added to LUP
2026-06-16 10:25:56
date last changed
2026-06-16 10:25:58
@misc{9231279,
  abstract     = {{This thesis investigates machine learning-based control charts with Variable Pa-
rameters(VP)formonitoringGeneralizedLinearModel(GLM)profiles. Previous
studies have examined machine learning control charts under Fixed Parameters
(FP) schemes for several GLM distributions, while VP schemes have mainly been
studied for Binomial profiles with logit link. This thesis extends the VP frame-
work to two additional GLM settings: Poisson profiles with log link and Gamma
profiles with log link.
Three machine learning methods are considered: Neural Networks, XGBoost,
and Support Vector Regression. For each method, FP and VP control charts are
calibrated using simulated in-control profiles, and their performance is evaluated
under intercept shifts, slope shifts, and simultaneous shifts. FP performance is
measured using Average Run Length, while VP performance is measured using
Average Time to Signal.
The results show that VP schemes often reduce detection time compared
with FP schemes, but the size of the improvement depends on the distribution,
model, and shift size. For Poisson profiles, VP provides clear improvements for
Neural Networks and Support Vector Regression, especially at moderate shifts.
For Gamma profiles, the main VP advantage appears at very small shifts, since
larger shifts are usually detected almost immediately. Neural Networks and Sup-
portVectorRegressionshowstableperformanceinmostsettings, althoughNeural
Networks under the first training method show large-shift instability for Gamma
profiles. XGBoost is less competitive overall and appears more sensitive to cal-
ibration and tied prediction values. Overall, the thesis demonstrates that VP
schemes can improve machine learning-based GLM profile monitoring beyond
the previously studied Binomial case, particularly when the shift is not already
detected almost immediately by the FP chart.}},
  author       = {{Vitos, Konstantinos}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Machine Learning-based Control Charts with Variable Parameters for GLM Profiles}},
  year         = {{2026}},
}