2016
DOI: 10.1016/j.enbuild.2016.05.065
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A comparison of approaches to stepwise regression on variables sensitivities in building simulation and analysis

Abstract: Highlights: The robustness of stepwise regression is irrespective of selection approach.  The linear regression model constructed by AIC has a high risk of overfitting, especially when the sample size is small.  A mixture of discretized-continuous and categorical variables can be used for global SA.  For stepwise regression, increasing sample size can identify more sensitive variables, but the importance of highly sensitive variables remains the same.  The importance of variables for design objectives and… Show more

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Cited by 66 publications
(41 citation statements)
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“…However, the climate indices may be not independent of each other, it is necessary to identify significant indices for the precipitation from a great number of climate indices for alleviating the multicollinearity between these climate indices. The stepwise variable selection is a systematic method to select the significant predictors to build an optimal regression model by adding or subtracting a predictor from a large set of potential explanatory variables based on their statistical significance in a regression (Wang et al ., 2016a; Kim et al ., 2018). When any two explanatory variables are highly correlative, only the one with higher explanation skill will be selected into final model (Chen et al ., 2019).…”
Section: Introductionmentioning
confidence: 99%
“…However, the climate indices may be not independent of each other, it is necessary to identify significant indices for the precipitation from a great number of climate indices for alleviating the multicollinearity between these climate indices. The stepwise variable selection is a systematic method to select the significant predictors to build an optimal regression model by adding or subtracting a predictor from a large set of potential explanatory variables based on their statistical significance in a regression (Wang et al ., 2016a; Kim et al ., 2018). When any two explanatory variables are highly correlative, only the one with higher explanation skill will be selected into final model (Chen et al ., 2019).…”
Section: Introductionmentioning
confidence: 99%
“…Test the significance of the regression equation for new variables. If it is significant, it will introduce new variables, and if it is not significant, it will not introduce them, until there are no more significant variables to introduce, and no insignificant variables need to be eliminated …”
Section: Methodsmentioning
confidence: 99%
“…Linear correlation is applied to examine relationships between air temperature and any a potential influencing (predictor) variable. Stepwise multiple linear regression is applied to determine the relative importance of different predictor variables in explaining the dependent variable (Clow, ; Draper & Smith, ), where the bidirectional elimination combining forward selection and backward elimination is adopted to determine the final regression model (Wang et al, ). The statistical significance of the regression model is assessed by an F test.…”
Section: Methodsmentioning
confidence: 99%