9 results on '"Mona Ayoubi"'
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2. Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering
- Author
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Mona Ayoubi
- Subjects
General Engineering - Published
- 2019
- Full Text
- View/download PDF
Catalog
3. Simultaneous monitoring of correlated multivariate linear and GLM regression profiles in Phase II
- Author
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Fatemeh Sogandi, Mona Ayoubi, and Amirhossein Amiri
- Subjects
Generalized linear model ,General linear model ,Multivariate statistics ,021103 operations research ,Information Systems and Management ,Proper linear model ,Computer science ,0211 other engineering and technologies ,Phase (waves) ,02 engineering and technology ,Management Science and Operations Research ,01 natural sciences ,Regression ,Correlation ,010104 statistics & probability ,Management of Technology and Innovation ,Bayesian multivariate linear regression ,Industrial relations ,Statistics ,Statistics::Methodology ,0101 mathematics ,Business and International Management - Abstract
In some applications, the quality of a process or product is characterized by correlated multivariate linear and generalized linear model (GLM) regression profiles. Monitoring these profiles separately leads to misleading results because the correlation structure among the multivariate linear and GLM profiles is neglected. In this paper, we specifically concentrate on Phase II and propose some procedures for monitoring multivariate linear and GLM regression profiles. Simulation studies are used to compare the performance of the proposed methods under different magnitudes of shifts in the regression parameters in terms of the average run length criterion. The results of simulation studies show the superior performance of the proposed methods compared to monitoring multivariate linear and GLM profiles separately. In addition, the performance of the proposed monitoring schemes is illustrated by a numerical example. Finally, the application of the proposed methods is shown by a real-world case. more...
- Published
- 2016
- Full Text
- View/download PDF
4. Change Point Estimation in the Mean of Multivariate Linear Profiles with No Change Type Assumption via Dynamic Linear Model
- Author
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Reza Baradaran Kazemzadeh, Mona Ayoubi, and Rassoul Noorossana
- Subjects
0209 industrial biotechnology ,Multivariate statistics ,Estimator ,Monotonic function ,02 engineering and technology ,Management Science and Operations Research ,Statistical process control ,01 natural sciences ,010104 statistics & probability ,020901 industrial engineering & automation ,Statistics ,Applied mathematics ,Control chart ,Point (geometry) ,Point estimation ,0101 mathematics ,Safety, Risk, Reliability and Quality ,Smoothing ,Mathematics - Abstract
Change point estimation is a useful concept that helps quality engineers to effectively search for assignable causes and improve quality of the process or product. In this paper, the maximum likelihood approach is developed to estimate change point in the mean of multivariate linear profiles in Phase II. After the change point, parameters are estimated through filtering and smoothing approaches in dynamic linear model. The proposed change point estimator can be applied without any prior knowledge about the change type against existing estimators which assume change type is known in advance. Besides, sporadic change point can be identified as well. Simulation results show the effectiveness of the proposed estimators to estimate step, drift and monotonic, as well as sporadic changes in small to large shifts. In addition, effect of different values of the Multivariate Exponentially Weighted Moving Average (MEWMA) control chart smoothing coefficient on the performance of the proposed estimator is investigated presenting that the smoothing estimator has more uniform performance. Copyright © 2015 John Wiley & Sons, Ltd. more...
- Published
- 2015
- Full Text
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5. Change Point Estimation of Multivariate Linear Profiles Under Linear Drift
- Author
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Reza Baradaran Kazemzadeh, Mona Ayoubi, and Rassoul Noorossana
- Subjects
Statistics and Probability ,General linear model ,Multivariate statistics ,Modeling and Simulation ,Bayesian multivariate linear regression ,Statistics ,Statistics::Methodology ,Estimator ,Control chart ,Statistical process control ,Missing data ,Smoothing ,Mathematics - Abstract
In this paper, maximum likelihood estimators (MLE) for both step and linear drift changes in the regression parameters of multivariate linear profiles are developed. Performance of the proposed estimators is compared under linear drift changes in the regression parameters when a combined MEWMA and Chi-square control charts method signals an out-of-control condition. The effect of smoothing parameter of MEWMA control charts, missing data, and multiple drift changes on the performance of the both estimators is also evaluated. The application of the proposed estimators is also investigated thorough a numerical example resulted from a real case. more...
- Published
- 2014
- Full Text
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6. Estimating multivariate linear profiles change point with a monotonic change in the mean of response variables
- Author
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Reza Baradaran Kazemzadeh, Mona Ayoubi, and Rassoul Noorossana
- 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 - 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. more...
- Published
- 2014
- Full Text
- View/download PDF
7. Profile Monitoring Using Nonparametric BootstrapT2Control Chart
- Author
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Mona Ayoubi and Rassoul Noorossana
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Statistics and Probability ,Variable (computer science) ,Computer simulation ,Control limits ,Modeling and Simulation ,Statistics ,X-bar chart ,Control chart ,Statistical process control ,Standard deviation ,Statistic ,Mathematics - Abstract
In certain statistical process control applications, performance of a product or process can be monitored effectively using a linear profile or a linear relationship between a response variable and one or more explanatory variables. In this article, we design a nonparametric bootstrap control chart for monitoring simple linear profiles based on T 2 statistic. We evaluate the performance of the proposed method in phase II. The average and standard deviation of the run length under different shifts in the intercept, slope, and standard deviation are considered as the performance measures. Simulation results show that the performance of the proposed bootstrap control chart improves as the size of the available data increases. more...
- Published
- 2012
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8. Estimating sporadic change point in the mean of polynomial profiles
- Author
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Reza Baradaran Kazemzadeh and Mona Ayoubi
- Subjects
Polynomial ,Statistics ,Phase (waves) ,Linear model ,Process (computing) ,Estimator ,Point (geometry) ,Control chart ,Smoothing ,Mathematics - Abstract
Identifying the actual time of a change brings a decrease in time range of searching for assignable causes leading to less cost. In this paper, the maximum likelihood approach is developed to estimate sporadic change point for the mean of a polynomial profile in Phase II which has not been performed yet in the literature. Estimation of the process parameters for the samples after the change point is carried out using filtering and smoothing estimation methods of dynamic linear models. The proposed procedures are applied after receiving an out-of-control signal from T2 control chart. The performance of the proposed change point estimators is also compared to the step and drift estimators' performance under sporadic change in the process mean. Simulation results confirm the effectiveness of the proposed methods in estimating sporadic change point. more...
- Published
- 2015
- Full Text
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9. Multivariate exponentially weighted moving sample covariance control chart for monitoring covariance matrix
- Author
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Mona Ayoubi, Seyed A. Vaghefi, E. Hassan Nayebi, and Amirhossein Amiri
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021103 operations research ,Covariance function ,Covariance matrix ,0211 other engineering and technologies ,02 engineering and technology ,Covariance ,Estimation of covariance matrices ,Matérn covariance function ,Scatter matrix ,Statistics ,0202 electrical engineering, electronic engineering, information engineering ,Law of total covariance ,Statistics::Methodology ,Rational quadratic covariance function ,020201 artificial intelligence & image processing ,Safety, Risk, Reliability and Quality ,Mathematics - Abstract
In this paper, a control chart is proposed to detect changes in the covariance matrix of a multivariate normal process, when sample size is one. The proposed chart statistic is constructed based on the exponentially weighted form of sample covariance matrix given by individual observation over time. Distance between the values of variance and covariance components in this multivariate exponentially weighted moving sample covariance matrix and, the in-control corresponding elements of process variance-covariance matrix provides a basis for process variability monitoring. The statistical performance of the proposed method is evaluated through the use of a Monte Carlo simulation. The results show the superiority of the proposed control chart performance especially in the case of incremental changes in covariance matrix. more...
- Published
- 2016
- Full Text
- View/download PDF
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