[1] When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealis...

In this paper, we discuss the challenge of determining the number of classes in a family of finite mixture models with the intent of improving the specification of latent class models for criminal trajectories. We argue that the traditional method of using either the Proc Traj or Mplus package to compute and maximize the Bayesian Information Criterion (BIC) is problematic: Proc Traj and Mplus d...

Peanut is one of the most important legume commodities in Indonesia. In its implementation, a lot research has been done related to this plant. However, studies conducted by growth models, it very rarely studied. Therefore, researchers are interested modeling peanuts. One models that can be used multilevel regression model for case repeated measurement data. Multilevel was chosen because consid...

background: although the cox proportional hazard regression is the most popular model for analyzing the prognostic factors on survival of cancer patients, under certain circumstances, parametric models estimate the parameter more efficiently than the cox model.  the aim of this study was to compare the cox regression model  with parametric models in patients with gastric cancer who registered a...

This study aims to determine the copula-based model for estimating aggregate losses in traffic accident insurance. research method is descriptive quantitative. Determination of best and accuracy determined based on Akaike Information Criterion (AIC), smallest Root Mean Square Error (RMSE), Vuong Test. The results study, there a dependency relationship or correlation between frequency claims sev...

1 Dr. Chen Xi worked at Eli Lilly and Company, and he is currently working at Pfizer, Inc. as a consultant specialized in clinical trial analysis and SAS programming support. His view does not reflect the business practice of any of his client firms. He can be reached through E-Mail at [email protected] or [email protected]. Information theoretic model selection criteria such as AIC, BIC and ICOM...

Linear mixed effects models are highly flexible in handling a broad range of data types and are therefore widely used in applications. A key part in the analysis of data is model selection, which often aims to choose a parsimonious model with other desirable properties from a possibly very large set of candidate statistical models. Over the last 5–10 years the literature on model selection in l...

The classical problem of model selection among parametric model sets is considered. The goal is to choose a model set which best represents observed data. The critical task is the choice of a criterion for model set comparison. Pioneer information theoretic based approaches to this problem are Akaike information criterion (AIC) and different forms of minimum description length (MDL). The prior ...

aim : the aim of this study was to compare alternatives methods for analysis of zero inflated count data and compare them with simple count models that are used by researchers frequently for such zero inflated data. background : analysis of viral load and risk factors could predict likelihood of achieving sustain virological response (svr). this information is useful to protect a person from ac...

Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce R package cAIC4 that allows computation Akaike information criterion (cAIC). Computation AIC needs to take into account uncertainty random effects variance therefore not straightforward. We fast stabl...