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Model Selection
Published in Prabhanjan Narayanachar Tattar, H. J. Vaman, Survival Analysis, 2022
Prabhanjan Narayanachar Tattar, H. J. Vaman
As with the parametric model selection and the method of computing the BIC, we can augment the step function with the option of k = log() which will then lead to the model selection based on the BIC. Note that the software will still continue to display the criterion as AIC only. However, this is a simple inconvenience that we will have to live with.
Choosing among Competing Specifications
Published in Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle, Structural Equation Modeling for Health and Medicine, 2021
Douglas D. Gunzler, Adam T. Perzynski, Adam C. Carle
BIC values are dependent on sample size and model complexity. As a result, this can turn out to be a major limitation if we have too small of a sample, where BIC in formula (6.5) may automatically select the simplest model which may not be theoretically relevant.
Machine learning for radiation oncology
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
The Bayesian information criterion (BIC) is also a criterion for model selection among a finite set of models, and the model with the lowest BIC is preferred. The BIC is closely related to the AIC and is intended to resolve overfitting by introducing a penalty term for the number of parameters in the model. They have a different penalty for the number of parameters. Although the penalty of AIC is related to the number of estimated parameters, the penalty of BIC is related to its product with the log function of the sample size. It is assumed that a “true model” is in the set of candidates and that BIC will select the “true model” with the probability of 1, as n → ∞, but the probability of the selection via AIC is less than 1. [25,27,28] However, a simulation study demonstrates that the risk of selecting a very bad model is minimized with AIC under such an assumption. [27] If the “true model” is not in the candidate set, AIC is appropriate for finding the best approximating model when the approximation is done with regard to information loss. [25,27,28]
Factors influencing blood pressure variability in postmenopausal women: evidence from the China Health and Nutrition Survey
Published in Clinical and Experimental Hypertension, 2023
Zhonge Tao, Quanxin Qu, Jing Li, Xiaolin Li
We extracted all follow-up data on blood pressure from the database in 8 years (1993, 1997, 2000, 2004, 2006, 2009, 2011, and 2015). The mean processing method was used for the blood pressure measured three times in the same year. Then, we adopted the GBTM approach, which is an application of finite-mixture modeling that allows the identification of population subgroups (classes) characterized by statistically distinct trajectories for one or more outcomes of interest (21). The model was developed based on the blood pressure trajectories in the 8 years identified by the TRAJ procedure in SAS software. During the modeling process, Bayesian Information Criterion (BIC) was used to determine the optimal number of groups. BIC was a model selection benchmark and could balance model fit and complexity. The smaller BIC values indicated the better fit (21). For different group numbers and BIC of the model, we drew elbow diagrams to choose the group number according to the number corresponding to the inflection point. Then, the trajectory trend of the groups was separately visualized for the assignment of a descriptive label.
Built-environment risk assessment for pedestrians near bus-stops: a case study in Delhi
Published in International Journal of Injury Control and Safety Promotion, 2023
Deotima Mukherjee, K. Ramachandra Rao, Geetam Tiwari
Univariate analysis was conducted between the response and included predictors for both day and night datasets. Chi square tests were conducted to identify dependency between all the attributes before including them in the multivariate regression. Table 2 presents the combination of built-environment factors that have significant dependency and therefore not included in the multivariate models. This was followed by testing the null hypothesis for all the variables. For assessing the goodness-of-fit (GOF), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were performed (Kwayu et al., 2020). AIC is an estimation of GOF for any statistical model while BIC helps in selection of a model from multiple non-parametric models with different number of parameters. Of the two, BIC is a better performance measure as false positives would be misleading than a false negative. Both AIC and BIC are calculated using the log-likelihood of the model (Equations 5 and 6).
Bayesian approaches to variable selection in mixture models with application to disease clustering
Published in Journal of Applied Statistics, 2023
Many model selection approaches were also proposed within the Bayesian framework. For example, the Bayes factor (BF) [27] is a commonly used index that is based on integrated likelihood, and can be applied when there are more than two candidate models and can be used for comparing non-nested models. For comparing two models, 20]. The BIC is defined as N is the sample size and k + 1 classes to a model with k classes, and 26,42].