Multivariate Nonparametric Statistical Techniques for Simulation Model Validation

1997 ◽  
Author(s):  
Edward C. Larson ◽  
B. E. Parker ◽  
Poor Jr. ◽  
H. V.
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Statistical Techniques ◽  
Nonparametric Statistical

Development of a Simulation Model for Optimization of Business Process

2018 ◽  
pp. 26-42
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Business Process ◽  
Research Question ◽  
Future Research ◽  
The Novel ◽  
Model Based ◽  
Model Validity ◽  
Proposed Model ◽  
New Research

Optimization of business process assists in efficient organization of business process. For the success of optimization of business process, a simulation model based on gap processes for the analysis of buyers' burstiness in business process has been proposed. However, the model has to be validated. The aim of the research is to implement a validation approach to the simulation model based on gap processes for the optimization of business process underpinning elaboration of a new research question on the model validity. The meaning of the key concepts of “validation,” “model validation,” and “model validation approach” is studied. The results of the present research show that the application of real system measurements validates the simulation model for the optimization of business process. The novel contribution of the manuscript is revealed in the newly created research question on the proposed model validity. Directions of future research are proposed.

Transmission-Distribution Co-Simulation: Model Validation with Standalone Simulation

2018 ◽  
Author(s):  
Yaswanth Nag Velaga ◽  
Aoxia Chen ◽  
P.K. Sen ◽  
Gayathri Krishnamoorthy ◽  
Anamika Dubey
Keyword(s):  
Simulation Model ◽  
Model Validation

scholarly journals Validation of a Cardiovascular Disease Policy Microsimulation Model Using Both Survival and Receiver Operating Characteristic Curves

2017 ◽  
Vol 37 (7) ◽  
pp. 802-814 ◽  
Author(s):  
Ankur Pandya ◽  
Stephen Sy ◽  
Sylvia Cho ◽  
Sartaj Alam ◽  
Milton C. Weinstein ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Simulation Model ◽  
Receiver Operating Characteristic ◽  
Model Validation ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Mortality Data ◽  
Microsimulation Model ◽  
Specific Mortality ◽  
Cvd Mortality

Background. Despite some advances, cardiovascular disease (CVD) remains the leading cause of death and healthcare costs in the United States. We therefore developed a comprehensive CVD policy simulation model that identifies cost-effective approaches for reducing CVD burden. This paper aims to: 1) describe our model in detail; and 2) perform model validation analyses. Methods. The model simulates 1,000,000 adults (ages 35 to 80 years) using a variety of CVD-related epidemiological data, including previously calibrated Framingham-based risk scores for coronary heart disease and stroke. We validated our microsimulation model using recent National Health and Nutrition Examination Survey (NHANES) data, with baseline values collected in 1999-2000 and cause-specific mortality follow-up through 2011. Model-based (simulated) results were compared to observed all-cause and CVD-specific mortality data (from NHANES) for the same starting population using survival curves and, in a method not typically used for disease model validation, receiver operating characteristic (ROC) curves. Results. Observed 10-year all-cause mortality in NHANES v. the simulation model was 11.2% (95% CI, 10.3% to 12.2%) v. 10.9%; corresponding results for CVD mortality were 2.2% (1.8% to 2.7%) v. 2.6%. Areas under the ROC curves for model-predicted 10-year all-cause and CVD mortality risks were 0.83 (0.81 to 0.85) and 0.84 (0.81 to 0.88), respectively; corresponding results for 5-year risks were 0.80 (0.77 to 0.83) and 0.81 (0.75 to 0.87), respectively. Limitations. The model is limited by the uncertainties in the data used to estimate its input parameters. Additionally, our validation analyses did not include non-fatal CVD outcomes due to NHANES data limitations. Conclusions. The simulation model performed well in matching to observed nationally representative longitudinal mortality data. ROC curve analysis, which has been traditionally used for risk prediction models, can also be used to assess discrimination for disease simulation models.

scholarly journals Research on simulation model validation based on probability relational analysis

2021 ◽  
Vol 39 (5) ◽  
pp. 1158-1167
Author(s):  
Xiaolei NING ◽  
Xin ZHAO ◽  
Yingxia WU ◽  
Junmin ZHAO ◽  
Meibo LYU ◽  
...  
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Model Verification ◽  
Cumulative Distribution ◽  
Consistency Analysis ◽  
Simulation Data ◽  
Dynamic Data ◽  
Flight Data ◽  
Relational Analysis ◽  
Relational Degree

The most basic and direct method for simulation model validation is to compare the consistency of missile flight data and simulation data under the same input conditions. However, the existing dynamic data consistency analysis methods are mainly suitable for the case between 1-D missile flight data and 1-D simulation data, and do not conform to the consistency test of single sample flight data and multi-sample simulation data in equipment qualification/finalization test. To solve this problem, a simulation model validation method based on probabilistic relational analysis is proposed. The consistency of output data is measured from the two scales of probability relational coefficient and probability relational degree. The probability relational coefficient is determined by calculating the cumulative distribution probability value of real missile flight samples in the distribution function constructed by simulation data. The probability correlation degree is calculated by judging whether the probability relational coefficient satisfies the uniform distribution of[0 1]. The consistency analysis problem of a kind of dynamic data association is solved accordingly. The correlation theorem that the probability relational degree must satisfy and its property are proved. Meanwhile the operation steps of simulation model verification based on probability correlation analysis are given. This method can process all multi-dimensional simulation data at the same time, and integrate the random factors in the test process, so it can make full use of the test information under the condition of small sample flight test, and improve precision and the reliability of simulation model verification. The rationality and validity of this method are further verified by numerical tests and application examples.

Improvement and simulation model validation of nano-grating coupler based on SOI structure

2015 ◽  
Vol 27 (2) ◽  
pp. 24123
Author(s):  
任馨宇 Ren Xinyu ◽  
菅傲群 Jian Aoqun ◽  
段倩倩 Duan Qianqian ◽  
张文栋 Zhang Wendong ◽  
李朋伟 Li Pengwei ◽  
...  
Keyword(s):  
Simulation Model ◽  
Model Validation ◽  
Grating Coupler

scholarly journals A practical exercise in simulation model validation

1998 ◽  
Vol 4 (1) ◽  
pp. 100-117 ◽  
Author(s):  
Gong Mingrui ◽  
D. J. Murray-Smith
Keyword(s):  
Simulation Model ◽  
Model Validation
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