Bootstrap-based model selection in subset polynomial regression

Bootstrap-based model selection in subset polynomial regression

The subset polynomial regression model is wider than the polynomial regression model. This study proposes an estimate of the parameters of the subset polynomial regression model with unknown error and distribution. The Bootstrap method is used to estimate the parameters of the subset polynomial regr...

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Veröffentlicht in:International journal of advances in intelligent informatics 2018-07, Vol.4 (2), p.87-94
Hauptverfasser: Suparman, Suparman, Rusiman, Mohd Saifullah
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Bootstrap algorithm
Computer simulation
Confidence intervals
Economic models
Mammography
Model selection
Neural networks
Parameter estimation
Polynomials
Regression
Regression models
Set theory
Statistical analysis
Statistical methods
Subset polynomial
Test procedures
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Beschreibung
Zusammenfassung:The subset polynomial regression model is wider than the polynomial regression model. This study proposes an estimate of the parameters of the subset polynomial regression model with unknown error and distribution. The Bootstrap method is used to estimate the parameters of the subset polynomial regression model. Simulated data is used to test the performance of the Bootstrap method. The test results show that the bootstrap method can estimate well the parameters of the subset polynomial regression model.
ISSN:2442-6571
2442-6571
DOI:10.26555/ijain.v4i2.173