Open AccessEstimasi Model Regresi Kuantil Spline Kuadratik pada Data Trombosit dan Hematokrit Pasien DBD
Author(s) -
Bunga Aprilia,
Anna Islamiyati,
Anisa Anisa,
Nirwan Ilyas
Publication year - 2020
Publication title -
estimasi
Language(s) - English
Resource type - Journals
eISSN - 2721-3803
pISSN - 2721-379X
DOI - 10.20956/ejsa.v1i2.9264
Subject(s) - quantile regression , mathematics , spline (mechanical) , statistics , smoothing spline , nonparametric regression , estimator , quantile , nonparametric statistics , regression analysis , spline interpolation , structural engineering , engineering , bilinear interpolation
Nonparametric quantile regression is used to estimate the regression function when assumptions about the shape of the regression curve are unknown. It is only assumed to be subtle by involving quantile values. One estimator in nonparametric regression is spline. The segmented properties of the spline provide more flexibility than ordinary polynomials. Therefore, the nature of the spline makes it possible to adapt more effectively to the local characteristics of a function or data. This study proposes to get the results of the estimation platelet count model to the hematocrit value of DHF. The optimal model obtained from the estimation of quadratic spline quantile regression is at quantile 0.5 with one knot and the GCV value is 41.5. The results of the estimation show that there is a decrease in platelet counts as the percentage of hematocrit increase.