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Jackknife testing : An experimental approach to refine model calibration and validation Von Quintus, Harold L et al

By: Publication details: Washington DC National Cooperative Highway Research Program, 2003; Research results digest 283, Description: 12 sSubject(s): Online resources: Bibl.nr: VTI P7702:283Location: Abstract: This digest summarizes findings from research with the objective of developing a detailed, statistically sound, and practical experimental plan to refine the calibration and validation of the performance models incorporated in the pavement design guide produced in NCHRP Project 1-37A with laboratory-measured hot mix asphalt (HMA) material properties. Jackknifing is an analytical procedure for refining and confirming the calibration coefficients of mechanistic-empirical distress prediction equations and models such as those used in the 2002 Design Guide. Jackknifing provides more reliable assessments of model prediction accuracy than the alternative use of either traditional split-sample validation or calibration goodness-of-fit statistics because jackknifing's goodness-of-fit statistics are based on predictions rather than the data used for fitting the model parameters
Item type: Reports, conferences, monographs
Holdings: VTI P7702:283

This digest summarizes findings from research with the objective of developing a detailed, statistically sound, and practical experimental plan to refine the calibration and validation of the performance models incorporated in the pavement design guide produced in NCHRP Project 1-37A with laboratory-measured hot mix asphalt (HMA) material properties. Jackknifing is an analytical procedure for refining and confirming the calibration coefficients of mechanistic-empirical distress prediction equations and models such as those used in the 2002 Design Guide. Jackknifing provides more reliable assessments of model prediction accuracy than the alternative use of either traditional split-sample validation or calibration goodness-of-fit statistics because jackknifing's goodness-of-fit statistics are based on predictions rather than the data used for fitting the model parameters