جستجوی مقالات مرتبط با کلیدواژه « robust estimator » در نشریات گروه « صنایع »

The main objective of performed researches in the field of multivariate statistical process control is to consider the correlation between multiple qualitative attributes for one step of process. In the second phase of the multivariate process control procedure, the rest of process is being studied whether it is under control, using the achieved control limits from the first phase and future observations. So, having found the outlier points of the first phase before the control limits to be computed, it is considered as an important issue. In order to detect these outlier points, Variety of techniques, that the majority of them rely on primary random samples, are proposed. These primary random points can effect on the precision of algorithms and final solution of the problem. In this paper, a robust estimator is issued applying hierarchical clustering technique that is not affected by outlier data in sample or unusual data, rather than the model assumptions and will detect the outlier points in multivariate control charts of the first phase in order to get them removed. Then, the proposed method is evaluated by creating the variety of scenarios from outlier points and the final outcome is compared with the Classical Hoteling and the least determinant covariance estimator. The evaluations represent that the proposed method detects more outlier points in less time rather than the former performed researches.

Control charts are the most useful tools for controlling the processes statistically. The construction of the control charts requires the estimation of the process parameters using random sample data. Usually the classical estimators of the process parameters are used to construct the control charts. The classical estimators of the parameters of the processes generating autocorrelated data are sensitive to the presence of the outlier observations. Applying classical methods of estimation while outliers are present, introduce biased estimates of the model parameters which result in wrong interpretation of the control chart. In this research a method called Iteratively Robust Filtered Fast Tau (IRFFT) which is insensitive to the presence of the outliers is proposed for estimating the parameters of the autocorrelated models. The newly introduced estimators are used to construct robust control chart for autocorrelated data. The suggested robust control chart is compared with the control chart whose parameters are estimated using LS method. Results of the simulation study for the two methods indicate that the ARL for the suggested robust control chart is much smaller under different scenarios. The findings may be extended to the other time series models.