Model Selection Sample Clauses
The Model Selection clause defines the process and criteria for choosing a specific model or methodology to be used in a project or agreement. Typically, this clause outlines who is responsible for selecting the model, the standards or benchmarks that must be met, and any approval procedures required before implementation. For example, it may specify that the client must approve the final model chosen by the service provider, or that the model must comply with certain industry standards. The core function of this clause is to ensure that all parties agree on the model to be used, thereby reducing misunderstandings and ensuring that the selected approach meets the project's objectives and requirements.
Model Selection. In order to compare our dynamic latent trait model with the benchmark model we use the deviance information criterion (DIC; according to ▇▇▇▇▇▇▇▇▇▇▇▇▇ et al., 2002). The DIC is a generalization of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for hierarchical models. In contrast to the AIC and BIC, DIC allows to compare Bayesian hierarchical models where the effective number of parame- ters is not clearly defined. Similar to the other information criteria a trade-off between model fit and model complexity is evaluated. The DIC contains one penalty term for the effective number of parameters used measuring model complexity and one term equal to the deviance of the likelihood measuring model fit. A lower DIC value indicates a better model fit. According to ▇▇▇▇▇▇▇▇▇▇▇▇▇ et al. (2002), if the difference in DIC is greater than 10, then the model with the larger DIC value has considerably less support than the model with the lower DIC value. For our models, the lower DIC value of our dynamic latent trait model (DIC = 9485.77) indicates that this model dominates in the terms of model fit as well as model complexity the obvious benchmark model (DIC = 12319.82). Rating errors. We begin our analysis of the estimation results with the rating errors. Our dynamic latent trait model captures estimates for the rating bias µj and the standard deviation σj of the rating error of the big three external rating agencies on the score scale. Table 3.8 shows the results for the estimated posterior distribution of the parameters for the three raters µj and σj, respectively. The posterior distributions of the parameters are characterized by the mean values (mean) and the standard deviations (SD) of the 18, 000 (4 × 4, 500) posterior draws. We infer from Table 3.8 that Fitch has the smallest absolute rating bias from µj σj mean SD mean SD Fitch 0.0155 0.0018 0.0752 0.0021 Moody’s 0.0887 0.0024 0.1013 0.0029 S&P 0.0732 0.0017 0.0641 0.0017 Table 3.8: Estimated rating bias µj and standard deviations σj for the rat- ing errors (on the score scale) of the big three external rating agencies Fitch, Moody’s and Standard&Poor’s. The posterior distributions of the parame- ters are characterized by the mean values (mean) and the standard deviations (SD) of the 18, 000 (4 × 4, 500) posterior draws. the consensus on the score scale with respect to the posterior mean (0.0155). Moody’s clearly seems to be too optimistic in its credit assessment yielding a p...
Model Selection. The Fifth-Generation NCAR / Penn State Mesoscale Model Version 3.7 (MM5; ▇▇▇▇▇ et al. 1994) and the NCAR Advanced Research Weather Research and Forecasting Model Version 3 (WRF; ▇▇▇▇▇▇▇▇▇ et al. 2008) were selected as the two meteorological models to be implemented in the upgraded PATH modelling system. Preprocessor programs of the MM5 modelling system including terrain, REGRID, LITTLE_R, and INTERPF were used to develop model inputs.
Model Selection. A logistic mixed effects model was used to investigate potential relationships between a binary outcome variable, i.e. the presence of a hitchhiker species with a group of explanatory variables such as manta ray gender. The model contained a random intercept to account for the correlation arising from individual mantas being repeatedly observed. To compare the goodness-of-fit, a GLMM model without random effects was tested. To ensure sufficient credibility to reliably estimate the parameters, categories of variables with cell counts below five were combined or removed such as injury type and breaching behaviour. The category ‘fresh mating wound’ from the pregnancy status variable was not included, since it was not possible to determine pregnancy status. The full model included the explanatory variables: manta ray gender, maturity status, pregnancy status, behavioural activity and sub-region (location of sighting). The Akaike information criterion (AIC) was used for the model selection procedure to determine the most important variables to include in the model. A lower AIC between two candidate models implies an improved fit to the data. The model was run separately for each of the hitchhiker species, and all of the variable combinations were tested (S2 Appendix, Tables 1-5). Next, the parameters (explanatory variables) with the lowest AIC were interpreted on the log odds scale (exp(parameter)) to obtain odds ratio values. The significance of each parameter was determined by whether the 95% confidence interval (CI) crossed one (non-significant). A narrow CI indicated that the estimate was known more precisely, in comparison to a wider CI which had a greater uncertainty. The analysis was performed using RStudio version 1.3.1056 [35].
Model Selection. No major multicollinearity problems were detected for this initial logistic regression model. Two-way interaction was considered for prior antibiotic use by race and AGE contact outside the household, however, a likelihood ratio test for the interaction terms showed no statistically significant interaction (X2df=1=0.018, p=0.89). Since the interaction terms were found not to be statistically significant they were eliminated from the model. Assessment of confounding using the 10% change in estimate approach revealed no meaningful confounding by race or contact with an AGE affected person outside the household as all model subsets had an adjusted odds ratio within 10% of the full model (gold standard) (Supplemental Table 1). Precision of the odds ratio estimates was also considered and there was no meaningful gain in precision comparing the full model (CI ratio= 3.2) to the model with only prior antibiotic use (CI ratio=3.0) (Supplemental Table 1). Since there was little loss in precision when controlling for these variables it was decided that the model containing race and contact with an AGE affected person outside the household would be considered as the final model for norovirus-associated AGE. The model was found to have good fit with a deviance statistic of 3.58 and a p-value of
Model Selection. A set of 13 models was fit to the data to examine the importance of year-specific apparent survival (S), reach transition probabilities (ψ, probability of a fish moving from Black Rocks to Westwater, and vice versa), and p’s (Table 1). The modeling strategy was a typical one where best estimates of p’s for increasingly complex models were estimated and followed by addition of other parameters (see ▇▇▇▇▇▇▇ et al. 2010 or more details). The top model in the set contained 45% of the AICc weight and had 70 estimable parameters including survival rates for each reach and year and as a function of TL and TL2, transition probabilities, and probabilities of capture for every year, reach, and state combination. The second-ranked model had 35% of total model weight and one fewer parameter (the TL2 term), with all else being the same. Because the signs of the survival terms in the top and second-ranked models were the same and those models contained the bulk of the total weight (80%), and presented essentially the same trends, only the top-ranked model was interpreted in this analysis. A model with year and reach specific survival rates (94 total parameters, model 11 in the set) received no weight and many survival parameters were not estimable. Annual abundance estimates for adult Humpback Chub (>200 mm TL) were calculated for 1998- 2012 using the ▇▇▇▇▇▇▇ estimator in the robust design model in Program MARK. The annual abundance estimates for Humpback Chub ranged from 1,139 (2008) to 6,747 (1998; Figure 2). Point estimates 95% confidence intervals (CI) for 1998–2000 were: 6,747 (4,001–11,636), 3,520 (2,513–4,979), and 2,266 (1,742–2,975), respectively. Point estimates for 2003–2005 were: 2,520 (1,814–3,554), 2,724 (2,034–3,689), and 2,000 (1,596–2,530), respectively. Point estimates for 2007–2008 were: 1,212 (972–1,532) and 1,139 (954–1,379), respectively. Point estimates for 2011–2012 were: 1,467 (1,175–1,861) and 1,315 (1,022–1,713), respectively (Figure 2). Significance of differences in estimates was tested based on over lapping confidence intervals (Schenker and Gentleman 2001). The last four years (2007, 2008, 2011, and 2012) were significantly (p<0.05) lower than the previous six years sampled (1998, 1999, 2000, 2003, 2004, and 2005) except for 2000, 2003, and 2005. Abundance estimates for juvenile Humpback Chub and first year adult Humpback Chub (200– 220 mm TL) were not attempted due to the low numbers of these size classes collected throughout all study ye...
Model Selection. The Delft3D suite of models will be utilized to provide a modelling platform for hydrodynamic and water quality modelling. A Delft3D model (“WHCW model”) covering marine waters of at least 7 km from the Project boundary has been developed in a previous preliminary study (1) (referred as the Feasibility Study hereafter) for developing the proposed CMPs. The WHCW model was developed based on the Update Model developed under the Update on Cumulative Water Quality and Hydrological
Model Selection. Table 4. Unadjusted and adjusted odds ratios of various characteristics with antenatal care adequacy Reproductive Age Women (15-49 years old) Receiving At Least One Prenatal Care Visit North and South Kivu (MICS 2010)
Model Selection. Using backwards elimination with a significance level of 0.10, the linear regression model for keratometric astigmatism at 1 year of age did not give any significant variables among surgical factors. Using the change variables, two surgical factors were given in the stepwise regression model: individual number of sutures (p = 0.061) and incision location (p = 0.071). The model is then: Δ K-Ast = 11.971 – 0.818 (Incision Loc.) – 0.830 (# Sutures). A mixed model with subject-specific intercepts containing all potential surgical factors of interest gave no significant results. The mixed model was given as: Ŷij = αi + bij tij + 0.213 X1 – 0.014 X2 + 0.061 X3 + 0.20 X4 – 0.14 X5 + 0.010 tij, where: Ŷij = Keratometric astigmatism in patient i at visit j; αi = Subject specific random effect on the intercept for subject i; bij = Subject specific random effect on the slope; X1 = Incision Type: 1 for Scleral Tunnel, 0 for Clear Corneal; X2 = Incision Location; X3 = Extended Keratome: 1 for Yes, 0 for No; X4 = Number of Sutures; X5 = Suture Type: 1 for Running, 0 for Interrupted; tij = Age of patient i at visit j, given in months. The lowest p-value of these variables was number of sutures (p = 0.190). The age variable in this model was also non-significant (p = 0.574). In the context of the model, all surgical factors are considered fixed effects, while age at surgery is treated as both a fixed and a random (subject-specific) effect.
Model Selection