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Table 1 Results simulation study

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

95% PIs

p= 10

p= 500

 

mboost

quantregForest

mboost

quantregForest

Linear setup

    

π ^ | x 1

0.9454

0.9948

0.9361

0.9997

π ^ | x 2

0.9489

0.9689

0.9425

0.9889

π ^ | x 3

0.9466

0.9561

0.9418

0.9609

π ^ | x 4

0.9437

0.9307

0.9400

0.9471

π ^ | x 5

0.9405

0.9310

0.9373

0.9534

Non-linear setup

    

π ^ | x 1

0.9486

0.9721

0.9662

0.9832

π ^ | x 2

0.9494

0.9925

0.9623

0.9961

π ^ | x 3

0.9490

0.9940

0.9521

0.9954

π ^ | x 4

0.9460

0.9785

0.9407

0.9792

π ^ | x 5

0.9314

0.8743

0.9171

0.8942

  1. Mean conditional coverage resulting from 95% PIs for both setups and both scenarios. In every row, the value of the better performing algorithm (with the mean conditional coverage closer to the expected coverage of 95%) for each setup is printed in bold.