Influential data cases when the criterion is used for variable selection in multiple linear regression

Steel, S. J.; Uys, D. W.

doi:10.1016/j.csda.2005.02.003

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…MLR analysis is a widely used and well documented statistical procedure [20]. MLR attempts to model the relationship between two or more explanatory variables and a response variable by fitting a linear equation to the observed data.…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Predicting the Performance Measures of a 2-Dimensional Message Passing Multiprocessor Architecture by Using Machine Learning Methods

Akay¹,

Acı²,

Abut³

2015

NNW

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2-dimensional Simultaneous Optical Multiprocessor Exchange Bus (2D SOME-Bus) is a reliable, robust implementation of petaflops-performance computer architecture. In this paper, we develop models to predict the performance measures (i.e. average channel utilization, average channel waiting time, average network latency, average processor utilization and average input waiting time) of a message passing architecture interconnected by the 2D SOME-Bus by using Multilayer Feed-forward Artificial Neural Network (MFANN), Support Vector Regression (SVR) and Multiple Linear Regression (MLR). OPNET Modeler is used to simulate the message passing 2D SOME-Bus multiprocessor architecture and to create the training and testing datasets. Using 10-fold cross validation, the performance of the prediction models have been evaluated using several performance metrics. The results show that the SVR model using the radial basis function kernel (SVR-RBF) yields the lowest prediction error among all models.

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Section: Multiple Linear Regressionmentioning

confidence: 99%