78 research outputs found
Change Point Estimation in Monitoring Survival Time
Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered
From Profile to Surface Monitoring: SPC for Cylindrical Surfaces Via Gaussian Processes
Quality of machined products is often related to the shapes of surfaces
that are constrained by geometric tolerances. In this case, statistical
quality monitoring should be used to quickly detect unwanted deviations
from the nominal pattern. The majority of the literature has focused on
statistical profile monitoring, while there is little research on
surface monitoring. This paper faces the challenging task of moving from
profile to surface monitoring. To this aim, different parametric
approaches and control-charting procedures are presented and compared
with reference to a real case study dealing with cylindrical surfaces
obtained by lathe turning. In particular, a novel method presented in
this paper consists of modeling the manufactured surface via Gaussian
processes models and monitoring the deviations of the actual surface
from the target pattern estimated in phase I. Regardless of the specific
case study in this paper, the proposed approach is general and can be
extended to deal with different kinds of surfaces or profiles
A computationally efficient self-starting scheme to monitor general linear profiles with abrupt changes
Robust parameter design using the weighted metric method - The case of 'the smaller the better'
In process robustness studies, it is desirable to minimize the influence of noise factors on the system and simultaneously determine the levels of controllable factors optimizing the overall response or outcome. In the cases when a random effects model is applicable and a fixed effects model is assumed instead, an increase in the variance of the coefficient vector should be expected. In this paper, the impacts of this assumption on the results of the experiment in the context of robust parameter design are investigated. Furthermore, two criteria are considered to determine the optimum settings for the control factors. In order to better understand the proposed method and to evaluate its performances, a numerical example for the case of 'the smaller the better' is included
Estimating efficient value of controllable variable using an adaptive neural network algorithm: Case of a railway system
45-50This study proposes a method, using adaptive neural network (ANN), to predict, estimate and evaluate performancevariables without requiring any restrictive assumptions, taking case of a railway system. Also, by means of this method, it wouldbe possible to compare actual performance data with estimated values and route their assignable causes in future periods. Energyconsumption norm of vehicles in case of energy railway and real data of energy consumption in Iranian railway is considered
Phase I monitoring of simple linear profiles in multistage processes with cascade property
Phase I monitoring and change point estimation of autocorrelated poisson regression profiles
Importance analysis considering time‐varying parameters and different perturbation occurrence times
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