scholarly journals Interaction Effects on Prediction of Children Weight at School Entry Using Model Averaging

2018 ◽  
Vol 7 (4.30) ◽  
pp. 205
Author(s):  
Khuneswari Gopal Pillay ◽  
Sya Sya Syahira Muhammad Fitri Avtar ◽  
Mohd Asrul Affendi Abdullah
Keyword(s):  
Model Selection ◽  
Model Building ◽  
Mean Squared Error ◽  
Selection Criterion ◽  
Model Averaging ◽  
Interaction Effects ◽  
Small Sample ◽  
Information Criteria ◽  
Interaction Variables ◽  
Main Factor

Model selection introduce uncertainty to the model building process, therefore model averaging was introduced as an alternative to overcome the problem of underestimate of standards error in model selection. This research also focused on using selection criteria between Corrected Akaike's Information Criteria (AICC) and Bayesian Information Criteria (BIC) as weight for model averaging when involving interaction effects. Mean squared error of prediction (MSE(P)) was used in order to determine the best model for model averaging. Gateshead Millennium Study (GMS) data on children weight used to illustrate the comparison between AICC and BIC. The results showed that model selection criterion AICC performs better than BIC when there are small sample and large number of parameters included in the model. The presence of interaction variable in the model is not significant compared to the main factor variables due to the lower coefficient value of interaction variables. In conclusion, interaction variables give less information to the model as it coefficient value is lower than main factor.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

Investigation on the Improvement of Prediction by Bootstrap Model Averaging

2006 ◽  
Vol 45 (01) ◽  
pp. 44-50 ◽  
Author(s):  
N. H. Augustin ◽  
W. Sauerbrei ◽  
N. Holländer
Keyword(s):  
Model Selection ◽  
Mean Squared Error ◽  
Model Averaging ◽  
Predictive Performance ◽  
Information Criterion ◽  
Full Model ◽  
Backward Elimination ◽  
Study Results ◽  
Model Selection Uncertainty ◽  
Bootstrap Model

Summary Objectives: We illustrate a recently proposed two-step bootstrap model averaging (bootstrap MA) approach to cope with model selection uncertainty. The predictive performance is investigated in an example and in a simulation study. Results are compared to those derived from other model selection methods. Methods: In the framework of the linear regression model we use the two-step bootstrap MA, which consists of a screening step to eliminate covariates thought to have no influence on the response, and a model-averaging step. We also apply the full model, variable selection using backward elimination based on Akaike’s Information Criterion (AIC), the Bayes Information Criterion (BIC) and the bagging approach. The predictive performance is measured by the mean squared error (MSE) and the coverage of confidence intervals for the true response. Results: We obtained similar results for all approaches in the example. In the simulation the MSE was reduced by all approaches in comparison to the full model. The smallest values are obtained for bootstrap MA. Only the bootstrap MA and the full model correctly estimated the nominal coverage. The backward elimination procedures led to substantial underestimation and bagging to an overestimation of the true coverage. The screening step of bootstrap MA eliminates most of the unimportant factors. Conclusion: The new bootstrap MA approach shows promising results for predictive performance. It increases practical usefulness by eliminating unimportant factors in the screening step.

scholarly journals Normalized Information Criteria and Model Selection in the Presence of Missing Data

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2474
Author(s):  
Nitzan Cohen ◽  
Yakir Berchenko
Keyword(s):  
Missing Data ◽  
Model Selection ◽  
Missing Values ◽  
Model Averaging ◽  
Information Criterion ◽  
Current Theory ◽  
Information Criteria ◽  
Alternative Methods ◽  
Sample Sizes ◽  
Statistical Efficiency

Information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC) are commonly used for model selection. However, the current theory does not support unconventional data, so naive use of these criteria is not suitable for data with missing values. Imputation, at the core of most alternative methods, is both distorted as well as computationally demanding. We propose a new approach that enables the use of classic well-known information criteria for model selection when there are missing data. We adapt the current theory of information criteria through normalization, accounting for the different sample sizes used for each candidate model (focusing on AIC and BIC). Interestingly, when the sample sizes are different, our theoretical analysis finds that AICj/nj is the proper correction for AICj that we need to optimize (where nj is the sample size available to the jth model) while −(BICj−BICi)/(nj−ni) is the correction of BIC. Furthermore, we find that the computational complexity of normalized information criteria methods is exponentially better than that of imputation methods. In a series of simulation studies, we find that normalized-AIC and normalized-BIC outperform previous methods (i.e., normalized-AIC is more efficient, and normalized BIC includes only important variables, although it tends to exclude some of them in cases of large correlation). We propose three additional methods aimed at increasing the statistical efficiency of normalized-AIC: post-selection imputation, Akaike sub-model averaging, and minimum-variance averaging. The latter succeeds in increasing efficiency further.

Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

2015 ◽  
Vol 27 (9) ◽  
pp. 1857-1871 ◽  
Author(s):  
Chee-Ming Ting ◽  
Abd-Krim Seghouane ◽  
Muhammad Usman Khalid ◽  
Sh-Hussain Salleh
Keyword(s):  
Model Selection ◽  
Resting State ◽  
Brain Networks ◽  
Small Sample ◽  
Information Criteria ◽  
Fmri Data ◽  
Data Sets ◽  
Data Set ◽  
Model Order ◽  
Vector Autoregressive

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types—a resting state, an event-related design, and a block design data set—with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback’s IC (KIC) based on Kullback’s symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.

scholarly journals Model-Building of Multiple Binary Logit using Model Averaging

2018 ◽  
Vol 7 (4.30) ◽  
pp. 224
Author(s):  
Siti Aisyah Mohd Padzil ◽  
Khuneswari Gopal Pillay ◽  
Rohayu Mohd Salleh
Keyword(s):  
Research Study ◽  
Model Building ◽  
Model Averaging ◽  
Statistical Modelling ◽  
Absolute Error ◽  
Information Criteria ◽  
Upper Gastrointestinal Bleed ◽  
Binary Logit ◽  
High Possibility ◽  
Upper Gastrointestinal

Many researchers had been carried out on the study of statistical modelling, making it easier for new researchers in many sectors (social sciences, economics, medical, and etc.) to obtain knowledge in order to ease their research study. Nevertheless, there is still no agreed guidelines in obtaining the best model for multiple binary logit (MBL) using model averaging (MA). This research will demonstrate the proper guidelines to obtain best MBL model by using MA. Upper Gastrointestinal Bleed (UGIB) data were studied to illustrate the process of model-building using the proposed guidelines. This study will pinpoint the factors with high possibility leading to mortality of UGIB patients using obtained best model. Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were used to compute the weights in model averaging method. The performance of the models was computed by using Root mean square error (RMSE) and mean absolute error (MAE). Model obtained by using BIC weights showed a better performance since the RMSE and MAE values are lower compared to model obtained using AICc weights. The factors that affects the survivability of UGIB patients are shock score, comorbidity and rebleed. In conclusion, model-building of multiple binary logit using model averaging showed a better performance when using BIC.

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