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#Com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection Classes and Interfaces - 10 results found.
Name | Description | Type | Package | Framework |
BackwardElimination | Constructs a GLM model for a set of observations using the backward elimination method. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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BackwardElimination .Step | | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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EliminationByAIC | In each step, a factor is dropped if the resulting model has the least AIC, until no factor removal can result in a model with AIC lower than the current AIC. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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EliminationByZValue | In each step, the factor with the least z-value is dropped, until all z-values are greater than the critical value (given by the significance level). | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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ForwardSelection | Constructs a GLM model for a set of observations using the forward selection method. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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ForwardSelection .Step | | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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GLMModelSelection | Given a set of observations {y, X}, we would like to construct a GLM to explain the data. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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GLMModelSelection .ModelNotFound | Throw a ModelNotFound exception when fail to construct a model toSee Also:Serialized Form | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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SelectionByAIC | In each step, a factor is added if the resulting model has the highest AIC, until no factor addition can result in a model with AIC higher than the current AIC. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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SelectionByZValue | In each step, the most significant factor is added, until all remaining factors are insignificant. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |