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Review Article

Model Selection in Regression: Application to Tumours in Childhood

Volume 1 - Issue 1

Annah Managa*

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    • Department of Statistics, University of South Africa, South Africa

    *Corresponding author: Annah Managa, Department of Statistics, University of South Africa, South Africa, Email: managma@unisa.ac.za

Received: August 23, 2018;   Published: September 10, 2018

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Summary

We give a chronological review of the major model selection methods that have been proposed from circa 1960. These model selection procedures include Residual mean square error (MSE), coefficient of multiple determination (R2 ), adjusted coefficient of multiple determination (Adj R2 ), Estimate of Error Variance (S2 ), Stepwise methods, Mallow’s Cp , Akaike information criterion (AIC), Schwarz criterion (BIC). Some of these methods are applied to a problem of developing a model for predicting tumors in childhood using log-linear models. The theoretical review will discuss the problem of model selection in a general setting. The application will be applied to log-linear models in particular.

Keywords: MSE; R2 ; Adj R2 ; (S2 ); Stepwise methods; Cp ; AIC; BIC

Abstract| Introduction| Literature Review| Frequentist Model Selection Criteria| Methods | Results| Discussion| Conclusion And Recommendations| References|

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