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Table 2 Linear effect estimates for the LISA study: quantile boosting

From: Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting

  Cross-sectional analysis Longitudinal analysis
Variable τ= 0.025 τ= 0.975 τ= 0.025 τ= 0.975
Intercept 14.208 14.867 14.627 12.723
cAge -- -- f(·) f(·)
cBMI2 f(·) f(·) f(·) f(·)
cBMI0 0.008    
mBMI 0.028 0.034 0.029 0.132
mDiffBMI 0.026    
cSex = male   0.068   
cArea = urban -0.029 -0.075   -0.043
cBreast = yes     
mSmoke = yes -0.228 0.296   0.158
mEdu = 1 (low)   0.162   0.162
mEdu = 2   0.406   0.176
mEdu = 3   0.130   -0.107
mEdu = 4 (high)   0.070   -0.092
  1. Resulting effect estimates for the borders of 95% PIs with the quantile boosting approach. Only effects of selected variables are displayed. Non-linear effect estimates are presented as Figure S1 and Figure S3 in Additional file 1.