Model Validation. The Manager shall cooperate with the Company and the FRBNY in the manner set forth below to validate the conceptual soundness and implementation of models used by the Manager in its performance of services under this Agreement if such model is used in such a way that an error related to the model’s formulation or implementation is likely to have a material adverse effect on the Company, including a significant financial loss, a significant error of analytical outputs including cash flows, discount rates, valuations, or statistics relating to those outputs (such as expected values, variances, percentiles, or stress estimates), or a violation of applicable law or (each, a “Material Model”). For purposes of this Section 8.5, as of the Effective Date, the Manager has identified as “Material Models” those models used in the performance of services that are based on BlackRock Solutions Aladdin interest rate modeling and yield curve construction techniques utilized for the generation of cash flows, projection of floating rate coupons, and discounting, in support of the regular reporting and analytics to be delivered pursuant to Section 9.1, as agreed upon with FRBNY, including the Manager’s Shifted Lognormal
Model Validation. All models provided to CEB for use in dynamic simulations shall be validated against site measurements. The Independent Engineer shall certify that the behaviour shown by the model under simulated conditions is representative of the behaviour of the Facility under equivalent conditions. For validation purposes, Facility Owner shall ensure that appropriate tests are performed and measurements are taken to assess the validity of the dynamic model. Facility Owner shall provide all available information showing how the predicted behaviour of the dynamic model to be verified with the actual observed behaviour of a prototype or similar PV modules/Inverter under laboratory conditions and / or actual observed behaviour of the real Facility as installed and connected to the CEB System. If the on-site measurements or other information provided indicate that the dynamic model is not valid in one or more respects, Facility Owner shall provide a revised model whose behaviour corresponds to the observed on-site behaviour as soon as reasonably practicable. The conditions validated should as far as possible be similar to those of interest, e.g. low short circuit level at Interconnection Boundary, large frequency and voltage excursions, primary resource variations.
Model Validation. Multiple regression analyses (Treuth et al., 1995, Xxxxxxx et al., 2002, Xxxx et al., 2007) and indices of adiposity were assessed (Xxxxxx et al., 2000) to address two questions relating to the estimation of total abdominal visceral fat using DXA adiposity and a range of anthropometric measures: 1) which of these predictive models for the “gold standard” CT measure of visceral fat, best fit our validation sample of 54 females; and 2) in relation to previous discussions (Xxxxxx et al., 2000), can visceral fat be reliably estimated based upon anthropometry alone. CT and DXA scans for the same individuals were date-matched to between 0.23 - 2.5 years of one another. The difference in scan date for the validation sample was included in all visceral fat regression models as a nuisance factor. A Xxxxx-Xxxxxx analysis was conducted to assess if the predicted VAT error term was constant or varied across the range of CT-measured VAT area.
Model Validation. We examined the validation approach for each of the 34 outcomes (clinical endpoints of the studies). Single random split was used 17 times (50.0%), with the data split into single train-test or train-validation-test parts. When the data are split into train-test parts the best model for training data is chosen based on model’s performance on test data, whereas when the data are split into train-validation-test sets the best model for training data is selected based on the performance of the model on validation data. Then the test data are used to internally validate the performance of the model on new patients. Resampling (cross-validation or nested cross-validation) was used 9 times (26.5%). External validation (testing the original prediction model in a set of new patients from a different year, location, country etc.) was used 4 times (11.8%). External validation involved the chronological split of data into training and test parts 3 times (temporal validation), and validation of a new dataset 1 time. Multiple random split was used 2 times (5.9%), with the data split into train-test or train-validation-test data multiple times. Validation was not performed for 2 datasets (5.9%). We recommend reporting the steps of the validation approach in detail, to avoid misconceptions. In case of complex procedures, a comprehensive representation of the validation procedures can be insightful. Researchers should aim at performing both internal and external validations, if possible, to maximize the reliability of the prediction models. Table 5.3 shows the performance measures used for model validation in the 24 studies. A popular measure in the survival field, the C-index, was employed in 8 studies (33.3%, as C-index or time-dependent C-index) and AUC in 5 studies (20.8%). Notably, during the screening process, several manuscripts were identified where AUC and C-statistic were used interchangeably. While there is a link between the dynamic time-dependent AUC and the C-index (the AUC can be interpreted as a concordance index employed to assess model discrimination) [55], the two are not identical and some caution is required. Apart from the C-index, there was no other established measure in the 24 studies (large variability). This issue is of paramount importance as validation (and development) of the SNNs depends on a suitable performance measure. Any candidate measure should take into account the censoring mechanism. By employing performance measures that are common...
Model Validation. A time domain validation of the Strasbourg University Head-Neck Model (SUFE-HN-Model) was proposed by Xxxxx et al. [49] under LS-DYNA and it has been carried out in comparison to the N.B.D.L tests [15] under front, oblique and lateral impacts. This time analysis permitted to validate the model in accordance with the classic validation procedures systematically chosen in the literature. Finally temporal validation was completed by simulating Xxx et al. [52] experience in order to evaluate the relative cervical motion under rear impact. Furthermore SUFE-HN is validated in the frequency domain. In past studies, Xxxxxxx et al. [42] and Xxxxx et al. [49] showed that a validation in the time domain is not sufficient to reproduce the dynamic behavior of the neck. In fact, a great amount of responses may exist in a given corridor. And these responses do not correspond to a same mechanical behavior. More recently, Xxxxxx et al. [48] produced an extend of the head/neck system characterization in the frontal and horizontal plane. Two kinds of experimental devices were therefore realized. The first one is the same than the one used by Xxxxxxx et al. [42] and the second one consists in a rotational solicitation of the thorax. The results obtained thanks to the FEM of the head/neck system are summarized in the Table 10. Table 10. Results of experimental test and simulation in terms of natural frequencies Mode Mode -Illustration Average Volunteer Natural frequency Head-Neck FEM Natural frequency Flexion-Extension 1.68±0.2 Hz 2.8 Hz Inclination 1.7±0.2 Hz 2.6 Hz Axial rotation 3.2±0.3 Hz 3.4 Hz S-Shape 8.8±0.5 Hz 11 Hz Lateral retraction 9.5±1.4 Hz 9.6 Hz
Model Validation. As in the calibration sample, missing data were minimal; a single case was missing one of the indicators of condom use and 17 cases were missing the economic index variable. Descriptive statistics for the validation sample are available by request. The measurement model fit statistics were very similar to those of the calibration sample (CFI = .94, RMSEA=.05). Due to a non- positive definite matrix, the indicators of the latent variable condom use were fixed to be equal, and the variance of condom use was fixed to one. This more restrictive and parsimonious model allowed for successful estimation with the validation sample and the equality was retained in subsequent models across both samples. In the structural model for the validation sample , the fit deteriorated slightly (CFI=.93, RMSEA=.05) but was considered good enough to use for multiple group testing.
Model Validation. As mentioned before, UrbClim was applied to three cities: Antwerp, London, and Bilbao. The specification of the domain and periods covered for each of the cities is provided in Table 1.
Model Validation. In the event that Fiserv deploys a model as part of delivery of its Service which Fiserv agrees requires validation consistent with OCC Bulletin 2011-12 by Fiserv with regard to Fiserv’s use of such model, Fiserv will validate such model in accordance with the guidance in OCC Bulletin 2011-12 as it applies to a service provider. Client shall remain responsible for its own model validation in accordance with OCC Bulletin 2011-12, as it applies to a financial institution. Fiserv shall provide to Client at no additional charge, a copy of its model validation report(s) which are provided by Fiserv generally to its client base at no additional charge, within a reasonable time after its completion. [CONFIDENTIAL TREATMENT REQUESTED].
Model Validation. Introduction and definition Policy makers need and use information stemming from simulations in order to develop more effective policies. As citizens, public administration and other stakeholders are affected by decisions based on these models, the reliability of applied models is crucial. Model validation can be defined as ”substantiation that a computerised model within its domain of applicability possesses a satisfactory range of accuracy consistent with the intended application of the model” (Xxxxxxxxxxx, 1979). Therefore, a policy model should be developed for a specific purpose (or context) and its validity determined with respect to that purpose (or context). If the purpose of such a model is to answer a variety of questions, the validity of the model needs to be determined with respect to each question. A model is considered valid for a set of experimental conditions if the model’s accuracy is within its acceptable range, which is the amount of accuracy required for the model’s intended purpose. The substantiation that a model is valid is generally considered to be a process and is usually part of the (total) policy model development process (Xxxxxxx, 2008). For this purpose, specific and integrated techniques and ICT tools are required to be developed for policy modelling. Model validation is composed of two main phases: Conceptual model validation, i.e. determining that theories and assumptions underlying the conceptual model are correct and that the model’s representation of the problem entity and the model’s structure, logic, and mathematical and causal relationships are “reasonable” for the intended purpose of the model. Computerised model verification ensures that computer programming and implementation of the conceptual model are correct, as well as states that the overall behaviour of the model is in line with the available historical data. Why it matters in governance Model Validation is connected both to modelling and simulation. According to the general need for policy assessment and evaluation, there are some specific issues stemming from the Model Validation, which are strongly related to governance: Reliability of models: policy makers use simulation results to develop effective policies that have an important impact on citizens, public administration and other stakeholders. Model validation is fundamental to guarantee that the output (simulation results) for policy makers is reliable. Acceleration of policy modelling process: policy ...
Model Validation. Mass transport and heat transfer assumptions were taken in order to simplify the model. Therefore, it is important to verify the validity of these hypothesis to avoid deviation from the reality. The real behaviour of the catalyst´s bed is affected by mass and heat transfer internal and external limitations. In this section, the validation of the taken assumptions and the validation of the whole modelling are developed. All the calculations referring to the mass and heat transfer internal and external constraints are shown in the Appendix at the end of the thesis. Internal limitations of the catalyst:
