Reduction of the Effect of Estimation Error on In-control Performance for Risk-adjusted Bernoulli CUSUM Chart with Dynamic Probability Control Limits

2016 ◽  
Vol 33 (2) ◽  
pp. 381-386 ◽  
Author(s):  
Xiang Zhang ◽  
William H. Woodall
Keyword(s):  
Estimation Error ◽  
Cusum Chart ◽  
Control Performance ◽  
Control Limits

scholarly journals Cumulative Sum Chart Modeled under the Presence of Outliers

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 269 ◽  
Author(s):  
Nasir Abbas ◽  
Mu’azu Ramat Abujiya ◽  
Muhammad Riaz ◽  
Tahir Mahmood
Keyword(s):  
Phase I ◽  
Phase Ii ◽  
Estimation Error ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Cusum Chart ◽  
Cumulative Sum ◽  
Run Length ◽  
Design Structure ◽  
Control Limits

Cumulative sum control charts that are based on the estimated control limits are extensively used in practice. Such control limits are often characterized by a Phase I estimation error. The presence of these errors can cause a change in the location and/or width of control limits resulting in a deprived performance of the control chart. In this study, we introduce a non-parametric Tukey’s outlier detection model in the design structure of a two-sided cumulative sum (CUSUM) chart with estimated parameters for process monitoring. Using Monte Carlo simulations, we studied the estimation effect on the performance of the CUSUM chart in terms of the average run length and the standard deviation of the run length. We found the new design structure is more stable in the presence of outliers and requires fewer amounts of Phase I observations to stabilize the run-length performance. Finally, a numerical example and practical application of the proposed scheme are demonstrated using a dataset from healthcare surveillance where received signal strength of individuals’ movement is the variable of interest. The implementation of classical CUSUM shows that a shift detection in Phase II that received signal strength data is indeed masked/delayed if there are outliers in Phase I data. On the contrary, the proposed chart omits the Phase I outliers and gives a timely signal in Phase II.

scholarly journals Statistical process monitoring to improve quality assurance of inpatient care

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lena Hubig ◽  
Nicholas Lack ◽  
Ulrich Mansmann
Keyword(s):  
Quality Assurance ◽  
Inpatient Care ◽  
Performance Indicator ◽  
False Alarm Probability ◽  
Cusum Chart ◽  
Statistical Process ◽  
Process Characteristics ◽  
Cusum Charts ◽  
Statistical Process Monitoring ◽  
Control Limits

Abstract Background Statistical Process Monitoring (SPM) is not typically used in traditional quality assurance of inpatient care. While SPM allows a rapid detection of performance deficits, SPM results strongly depend on characteristics of the evaluated process. When using SPM to monitor inpatient care, in particular the hospital risk profile, hospital volume and properties of each monitored performance indicator (e.g. baseline failure probability) influence the results and must be taken into account to ensure a fair process evaluation. Here we study the use of CUSUM charts constructed for a predefined false alarm probability within a single process, i.e. a given hospital and performance indicator. We furthermore assess different monitoring schemes based on the resulting CUSUM chart and their dependence on the process characteristics. Methods We conduct simulation studies in order to investigate alarm characteristics of the Bernoulli log-likelihood CUSUM chart for crude and risk-adjusted performance indicators, and illustrate CUSUM charts on performance data from the external quality assurance of hospitals in Bavaria, Germany. Results Simulating CUSUM control limits for a false alarm probability allows to control the number of false alarms across different conditions and monitoring schemes. We gained better understanding of the effect of different factors on the alarm rates of CUSUM charts. We propose using simulations to assess the performance of implemented CUSUM charts. Conclusions The presented results and example demonstrate the application of CUSUM charts for fair performance evaluation of inpatient care. We propose the simulation of CUSUM control limits while taking into account hospital and process characteristics.

New Sliding Mode Controller for Robotic Manipulator Subjected to Disturbance and Uncertainty

2006 ◽  
Vol 220 (6) ◽  
pp. 807-815 ◽  
Author(s):  
J H Ham ◽  
S B Choi
Keyword(s):  
Position Control ◽  
Sliding Mode ◽  
Estimation Error ◽  
Sampling Period ◽  
Robotic Manipulator ◽  
Sliding Mode Controller ◽  
Control Performance ◽  
Parameter Uncertainties ◽  
Average Value ◽  
Predicted Values

This article presents a new sliding mode controller (SMC) for the position control of a robotic manipulator subjected to perturbations, such as parameter uncertainties and extraneous disturbances. The SMC is designed so that the sliding mode condition is satisfied and integrated with the perturbation estimator. The estimator is formulated by adopting a concept of the integrated average value of the imposed perturbation over a certain sampling period and realized using the Taylor series. In the formulation of the estimator, the relationship between control performance and sensor performance is established by adjusting the sampling ratio. Subsequently, in order to improve control performance, the actuating condition for the estimator is introduced: on-off switching condition (OSC). This condition is decided on the basis of the estimation error between actual and predicted values. By imposing the OSC, control accuracy can be enhanced when high frequency perturbations exist in the system. The benefits of the proposed methodology are demonstrated on a two-link planar manipulator. The position control performances of the manipulator are evaluated and compared between the proposed methodology and conventional control schemes.

scholarly journals The impact of varying patient populations on the in-control performance of the risk-adjusted CUSUM chart

2014 ◽  
Vol 27 (1) ◽  
pp. 31-36 ◽  
Author(s):  
W. Tian ◽  
H. Sun ◽  
X. Zhang ◽  
W. H. Woodall
Keyword(s):  
Cusum Chart ◽  
Control Performance ◽  
The Impact

The Effect of Estimation Error on Risk-Adjusted Survival Time CUSUM Chart Performance

2015 ◽  
Vol 32 (4) ◽  
pp. 1445-1452 ◽  
Author(s):  
Min Zhang ◽  
Yahui Xu ◽  
Zhen He ◽  
Xuejun Hou
Keyword(s):  
Survival Time ◽  
Estimation Error ◽  
Cusum Chart

scholarly journals To Compare Time-Weighted Graphs to Evaluate the Inclination of the Acetabular Component of Patients Who Had Total Hip Replacement Surgery

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Leman Tomak ◽  
Yuksel Bek ◽  
Yılmaz Tomak
Keyword(s):  
Total Hip Replacement ◽  
Acetabular Component ◽  
Hip Replacement ◽  
Control Charts ◽  
Moving Average ◽  
Cusum Chart ◽  
Total Hip ◽  
Replacement Surgery ◽  
Hip Replacement Surgery ◽  
Control Limits

Time-weighted graphs are used to detect small shifts in statistical process control. The aim of this study is to evaluate the inclination of the acetabular component with CUmulative SUM (CUSUM) chart, Moving Average (MA) chart, and Exponentially Weighted Moving Average (EWMA) chart. The data were obtained directly from thirty patients who had undergone total hip replacement surgery at Ondokuz Mayis University, Faculty of Medicine. The inclination of the acetabular component of these people, after total hip replacement, was evaluated. CUSUM chart, Moving Average chart, and Exponentially Weighted Moving Average were used to evaluate the quality control process of acetabular component inclination. MINITAB Statistical Software 15.0 was used to generate these control charts. The assessment done with time-weighted charts revealed that the acetabular inclination angles were settled within control limits and the process was under control. It was determined that the change within the control limits had a random pattern. As a result of this study it has been obtained that time-weighted quality control charts which are used mostly in the field of industry can also be used in the field of medicine. It has provided us with a faster visual decision.

scholarly journals Assessing the effect of estimation error on risk-adjusted CUSUM chart performance

2011 ◽  
Vol 24 (2) ◽  
pp. 176-181 ◽  
Author(s):  
M. A. Jones ◽  
S. H. Steiner
Keyword(s):  
Estimation Error ◽  
Cusum Chart

The Poisson INAR(1) CUSUM chart under overdispersion and estimation error

2011 ◽  
Vol 43 (11) ◽  
pp. 805-818 ◽  
Author(s):  
Christian H. Weiß ◽  
Murat Caner Testik
Keyword(s):  
Estimation Error ◽  
Cusum Chart

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Sign in / Sign up

Export Citation Format

Share Document