MRMAide model validation process

2003 ◽  
Author(s):  
Maria Valinski ◽  
Robert M. McGraw
Keyword(s):  
Model Validation ◽  
Validation Process

The Eye of the Beholder: A Case Example of Changing Clients' Perspectives Through Involvement in the Model Validation Process

2015 ◽  
Vol 32 (4) ◽  
pp. 437-449 ◽  
Author(s):  
Lambertus P. J. van Nistelrooij ◽  
Etiënne A.J.A. Rouwette ◽  
Ilse M. Verstijnen ◽  
Jac A.M. Vennix
Keyword(s):  
Model Validation ◽  
Validation Process

scholarly journals Validação de Modelos Internos no Brasil: Análise de Metodologias de Backtesting de VaR

2006 ◽  
Vol 4 (1) ◽  
pp. 97
Author(s):  
Alan Cosme Rodrigues da Silva ◽  
Claudio Henrique Da Silveira Barbedo ◽  
Gustavo Silva Araújo ◽  
Myrian Beatriz Eiras das Neves
Keyword(s):  
Low Power ◽  
Model Validation ◽  
Internal Model ◽  
Real Data ◽  
Small Samples ◽  
Limited Data ◽  
Traffic Light ◽  
Validation Process ◽  
Data Set ◽  
Kuiper Test

The purpose of this paper is to analyze backtesting methodologies of VaR, focusing on aspects as suitability to volatile markets and limited data set. We verify, from regulatory standpoint, tests to complement the Basel traffic light results, using simulated and real data. The results indicate that tests based on failures proportion are not adequate for small samples even fro 1,000 observations. The Basel criterion is conservative and has low power, which does not invalidate its application, as the criterion is only one of the procedures adopted in internal model validation process. Thus, it is suggested using tests that capture the shape of returns distribution, as the Kuiper test, in addition to the Basel criterion.

scholarly journals Validation of computational models in biomechanics

2009 ◽  
Vol 224 (7) ◽  
pp. 801-812 ◽  
Author(s):  
H B Henninger ◽  
S P Reese ◽  
A E Anderson ◽  
J A Weiss
Keyword(s):  
Fluid Mechanics ◽  
Model Validation ◽  
Patient Outcomes ◽  
Basic Science ◽  
Computational Models ◽  
Peer Acceptance ◽  
Biological Systems ◽  
Verification And Validation ◽  
Validation Process ◽  
Computational Biomechanics

The topics of verification and validation have increasingly been discussed in the field of computational biomechanics, and many recent articles have applied these concepts in an attempt to build credibility for models of complex biological systems. Verification and validation are evolving techniques that, if used improperly, can lead to false conclusions about a system under study. In basic science, these erroneous conclusions may lead to failure of a subsequent hypothesis, but they can have more profound effects if the model is designed to predict patient outcomes. While several authors have reviewed verification and validation as they pertain to traditional solid and fluid mechanics, it is the intent of this paper to present them in the context of computational biomechanics. Specifically, the task of model validation will be discussed, with a focus on current techniques. It is hoped that this review will encourage investigators to engage and adopt the verification and validation process in an effort to increase peer acceptance of computational biomechanics models.

Model Validation

2009 ◽  
pp. 231-241
Author(s):  
Ben Kei Daniel
Keyword(s):  
Social Capital ◽  
Sensitivity Analysis ◽  
Model Validation ◽  
Real World ◽  
Bayesian Model ◽  
Computational Models ◽  
Lessons Learned ◽  
Practical Application ◽  
Validation Process ◽  
The World

Though computational models take a lot of effort to build, a model is generally not useful unless it can help people to understand the world being modelled, or the problem the model is intended to solve. A useful model allows people to make useful predictions about how the world will behave now and possibly tomorrow. Validation is the last step required in developing a useful Bayesian model. The goal of validation is to gain confidence in a model and to demonstrate and prove that a model produces reliable results that are closely related to the problems or issues in which the model is intended to address. The goal of the Chapter is to provide the reader with a basic understanding of the validation process and to share with them key lessons learned from the model of social capital presented in the book. While sensitivity analysis is intended to ensure that a Bayesian model is theoretically consistent with goals and assumptions of the modeller (how the modeller views the world) or the accuracy of sources of data used for building the model, the goal of validation is to demonstrate the practical application of the model in real world settings. This Chapter presents the main steps involved in the process of validating a Bayesian model. It illustrates this process by using examples drawn from the Bayesian model of social capital.

Dynamic Model Validation Metric Based on Wavelet Thresholded Signals

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Andrew D. Atkinson ◽  
Raymond R. Hill ◽  
Joseph J. Pignatiello ◽  
G. Geoffrey Vining ◽  
Edward D. White ◽  
...  
Keyword(s):  
Model Validation ◽  
Development Process ◽  
Experimental System ◽  
Wavelet Thresholding ◽  
Transient Phase ◽  
Validation Process ◽  
System Data ◽  
Wavelet Decompositions ◽  
Automobile Crash ◽  
Specific Concern

Model validation is a vital step in the simulation development process to ensure that a model is truly representative of the system that it is meant to model. One aspect of model validation that deserves special attention is when validation is required for the transient phase of a process. The transient phase may be characterized as the dynamic portion of a signal that exhibits nonstationary behavior. A specific concern associated with validating a model's transient phase is that the experimental system data are often contaminated with noise, due to the short duration and sharp variations in the data, thus hiding the underlying signal which models seek to replicate. This paper proposes a validation process that uses wavelet thresholding as an effective method for denoising the system and model data signals to properly validate the transient phase of a model. This paper utilizes wavelet thresholded signals to calculate a validation metric that incorporates shape, phase, and magnitude error. The paper compares this validation approach to an approach that uses wavelet decompositions to denoise the data signals. Finally, a simulation study and empirical data from an automobile crash study illustrates the advantages of our wavelet thresholding validation approach.

Supervised Learning in Game Data Science: Model Validation and Evaluation

2021 ◽  
pp. 219-238
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Model Validation ◽  
Learning Process ◽  
Model Evaluation ◽  
Data Science ◽  
The Other ◽  
Validation Process ◽  
Test Dataset ◽  
Unseen Data

This chapter focuses on two specific steps in the machine learning process, called model validation and model evaluation. Specifically, model validation is the step used to tune the hyperparameters of the model. Here, we often integrate a cross-validation process, which we discuss in detail in this chapter. Model evaluation, on the other hand, is the process of testing the performance of the model using unseen data, the test dataset. These processes are used to ensure that the model we developed through the algorithms discussed in Chapter 6 are reliable, given our data. The chapter will include labs to give you a practical introduction to these steps, given the modeling techniques discussed in the last chapter.

scholarly journals 3 MARIAS: FROM WOMEN FOR WOMEN

2021 ◽  
Author(s):  
Leticia Menegon ◽  
Adrian Cernev ◽  
José Bailan
Keyword(s):  
Model Validation ◽  
Business Model ◽  
Social Impact ◽  
Critical Discussion ◽  
Financial Sustainability ◽  
Business Incubator ◽  
Validation Process ◽  
Teaching Case ◽  
Proposed Model ◽  
Social Management

Objective of the case: Help students to evaluate the difficulties of the entrepreneurial process, observing the particularities that involve the conception of the business and its validation, the distance between the concept of the business and its effective operationalization and monetization. Methodology / approach: teaching case in Management, based on a real enterprise, started in a business incubator. Methodology / approach: Teaching case in Management, based on a real enterprise, started in a business incubator. Main results: The case favors reflections about the methodologies adopted in the business model validation process, as well as the construction of your MVP. Theoretical / methodological contributions: Develop in the student the ability to evaluate the business model validation process, from the interviews to the MVP. Relevance / originality: Encourage critical discussion about building a startup's MVP. The case also provides a debate on the gap between the conception of the business model and its operationalization, including with regard to the financial sustainability of the proposed model. Social / management contributions: helping students to face the difficulties of the business model validation process when building an MVP, as well as developing a financial support model for a social impact enterprise.

Reliability-Based Design Optimization Using Confidence-Based Model Validation for Insufficient Experimental Data

2016 ◽  
Author(s):  
Min-Yeong Moon ◽  
K. K. Choi ◽  
Hyunkyoo Cho ◽  
Nicholas Gaul ◽  
David Lamb ◽  
...  
Keyword(s):  
Experimental Data ◽  
Simulation Model ◽  
Design Optimization ◽  
Model Validation ◽  
Model Bias ◽  
Reliability Based Design Optimization ◽  
Validation Process ◽  
Target Problem ◽  
Reliability Based Design ◽  
The Cost

The conventional reliability-based design optimization (RBDO) methods assume that a simulation model is able to represent the real physics accurately. However, the simulation model could be biased. Accordingly, when the conventional RBDO design is manufactured, the product may not satisfy the target reliability. Therefore, model validation, which corrects model bias, should be integrated in the RBDO process by incorporating experimental data. The challenge is that only a limited number of experimental data is usually available due to the cost of actual product testing. Consequently, model validation for RBDO needs to account for the uncertainty induced by insufficient experimental data as well as variability inherently existing in the products. In this paper, a confidence-based model validation process that captures the uncertainty and corrects model bias at user-specified target conservativeness level is developed. Thus, RBDO can be performed using confidence-based model validation to obtain conservative RBDO design. It is found that RBDO with model bias correction becomes a moving-target problem because the feasible domain changes as the design moves. Consequently, the RBDO optimum may not be easily found due to the convergence problem. To resolve the issue, an efficient process is proposed by carrying out deterministic design optimization (DDO) and RBDO without validation, followed by RBDO with confidence-based model validation. Finally, we demonstrate that the proposed RBDO approach can achieve a conservative and practical optimum design given a limited number of experimental data.

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