Uncertainty quantification and statistical model validation for an offshore jacket structure panel given limited test data and simulation model

2020 ◽  
Vol 61 (6) ◽  
pp. 2305-2318
Author(s):  
Min-Yeong Moon ◽  
Hyun-Seok Kim ◽  
Kangsu Lee ◽  
Byoungjae Park ◽  
K.K. Choi
Keyword(s):  
Statistical Model ◽  
Simulation Model ◽  
Uncertainty Quantification ◽  
Model Validation ◽  
Test Data ◽  
Statistical Model Validation

Prediction of rice productivity in Kandhamal district of Orissa using simulation modeling

2014 ◽  
Vol 05 (04) ◽  
pp. 1450021
Author(s):  
Banshidhar Panda ◽  
Milu Acharya ◽  
Dwitikrishna Panigrahi
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Model ◽  
Sample Size ◽  
Model Validation ◽  
Test Data ◽  
Simulation Modeling ◽  
Rice Yield ◽  
Correlation Test ◽  
Rice Productivity ◽  
Auto Correlation

Simulation technique has been employed to predict rice yield of Kandhamal plateaus in Orissa (India) using the data of previous years. Preliminary simulation model has been developed. The test for uniformity and independence has been conducted using Kolmogrov–smironov test and auto-correlation test, respectively. The result obtained has been subjected to testing of hypothesis by using two sided test. Data for five years (1995 to 2000) are used for model validation and the sample size is increased to 12 years i.e., from 1995 to 2007 for prediction up to 2012. Sensitivity analysis is conducted by changing the parameters within feasible limits to find out the effect on the model.

Brain tumor growth simulation: model validation through uncertainty quantification

2017 ◽  
Vol 8 (3) ◽  
pp. 655-662 ◽  
Author(s):  
N. Meghdadi ◽  
H. Niroomand-Oscuii ◽  
M. Soltani ◽  
F. Ghalichi ◽  
M. Pourgolmohammad
Keyword(s):  
Brain Tumor ◽  
Tumor Growth ◽  
Simulation Model ◽  
Uncertainty Quantification ◽  
Model Validation ◽  
Growth Simulation

On Statistical Model Validation

1996 ◽  
Vol 118 (2) ◽  
pp. 226-236 ◽  
Author(s):  
L. H. Lee ◽  
K. Poolla
Keyword(s):  
Statistical Model ◽  
Model Validation ◽  
Convex Sets ◽  
Randomized Algorithm ◽  
Parametric Uncertainty ◽  
Uncertainty Model ◽  
Data Record ◽  
Hit And Run ◽  
Statistical Model Validation ◽  
Volumes Of Convex Sets

In this paper we formulate a particular statistical model validation problem in which we wish to determine the probability that a certain hypothesized parametric uncertainty model is consistent with a given input-output data record. Using a Bayesian approach and ideas from the field of hypothesis testing, we show that in many cases of interest this problem reduces to computing relative weighted volumes of convex sets in RN (where N is the number of uncertain parameters). We also present and discuss a randomized algorithm based on gas kinetics, as well as the existing Hit-and-Run family of algorithms, for probable approximate computation of these volumes.

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.

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