In traditional parametric regression models, the functional form of the model is specified before the model is fit to data, and the object is to estimate the parameters of the model. In nonparametric regression, in contrast, the object is to estimate the regression function directly without specifying its form explicitly. In this appendix to Fox and Weisberg (2011), we describe how to fit sever...

Background: Neutrophil percentage to albumin ratio (NPAR) has been shown be correlated with the prognosis of various diseases. This study aimed explore effect NPAR on patients in coronary care units (CCU). Method: All data this were extracted from Medical Information Mart for Intensive Care III (MIMIC-III, version1.4) database. divided into four groups according their quartiles. The primary out...

We consider nonparametric regression models with multivariate covariates and estimate the regression curve by an undersmoothed local polynomial smoother. The resulting residual-based empirical distribution function is shown to differ from the errorbased empirical distribution function by the density times the average of the errors, up to a uniformly negligible remainder term. This result implie...

Introduction: Rectal toxicity is a dose limiting issue in prostate cancer radiotherapy. Prediction of these effects may be used to tailor the therapy. The purpose of this work was to develop predictive radiomic models based on clinical, dosimetric and radiomic features extracted from rectal wall magnetic resonance image (MRI).   Materials and Methods: This st...

In linear regression, we need to avoid adding too much richness to the model. Therefore we need feature selection, or regularization to make our fitting curve smoother. Qualitatively, the original linear regression model is an optimization problem of the form min w m i=1 (w · x i − y i) 2 And the corresponding regularized version of the same problem is min w m i=1 (w · x i − y i) 2 + λw 2 2 , w...