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An Optimal Selection of Regression Variables
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1981
Year
Mathematical ProgrammingControl MethodControl StrategyControl VariablesEngineeringHigh-dimensional MethodParameter EstimationModel-based Control TechniqueProcess ControlFeature SelectionRegression AnalysisStatistical InferenceSample SizeEstimation TheoryStatisticsRegression Variables
An asymptotically optimal selection of regression variables is proposed. The key assumption is that the number of control variables is infinite or increases with the sample size. It is also shown that Mallows's Cp', Akaike's FPE and aic methods are all asymptotically equivalent to this method.