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Estimating multivariate linear profiles change point with a monotonic change in the mean of response variables
- Source :
- The International Journal of Advanced Manufacturing Technology. 75:1537-1556
- Publication Year :
- 2014
- Publisher :
- Springer Science and Business Media LLC, 2014.
-
Abstract
- In this paper, a maximum likelihood estimator (MLE) is developed to estimate change point when monotonic change occurs in the mean of response variables in multivariate linear profiles in Phase II. Performance of the proposed estimator is compared to the performance of step change and linear drift estimators under different shift types. To conduct comparisons, accuracy and precision of the estimators are considered as performance measures. Simulation results show that the average change point estimate of the proposed estimator is less biased than the one for the step and drift estimators in small shifts, because $$ {\overline{\widehat{\tau}}}_{\mathrm{monotonic}} $$ is closer to the actual change point of 25 in small shifts. Also, the precision of the proposed estimator is approximately better than that of the step and drift estimators, because its precision values are higher. Hence, the proposed estimator has better performance in terms of both accuracy and precision in small shifts under any kinds of increasing changes. In single step and linear drift changes when the magnitude of shifts increases, the accuracy and precision of their corresponding estimators become better than the accuracy and precision of the proposed estimator. However, the proposed estimator has an advantage that it does not require assumptions about the change type, and its only assumption is that the mean of the response variables changes in an increasing manner. Additional evaluations on the effect of smoothing constant show that with smaller values of the smoothing constant, the proposed change point estimator has less biased estimates and smaller values of mean square error in small shifts rather than the step and drift estimators, leading to a better performance. Also, the larger values of smoothing constant lead to the better performance of the monotonic estimator in large shifts. Finally, the application of the proposed estimator is shown through a real case in the calibration process in the automotive industry.
- Subjects :
- Mean squared error
Mechanical Engineering
Estimator
Industrial and Manufacturing Engineering
Computer Science Applications
Efficient estimator
Minimum-variance unbiased estimator
Control and Systems Engineering
Consistent estimator
Statistics
Applied mathematics
Minimax estimator
Software
Smoothing
Invariant estimator
Mathematics
Subjects
Details
- ISSN :
- 14333015 and 02683768
- Volume :
- 75
- Database :
- OpenAIRE
- Journal :
- The International Journal of Advanced Manufacturing Technology
- Accession number :
- edsair.doi...........7ca0f19823b9bf6122812b8c0a069294
- Full Text :
- https://doi.org/10.1007/s00170-014-6208-6