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siqr's Introduction

siqr

Single-index quantile regression models are important tools in semiparametric regression to provide a comprehensive view of the conditional distributions of a response variable. This methods is especially useful when the data is heterogeneous or heavy tailed.

We provides functions that allow users to fit Single-Index Quantile Regression model via an efficient iterative local linear approach. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) at here for details.

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siqr's Issues

Question regarding weights calculation

During step 2, the kernel weights are calculated as follows.

ynew <- as.vector(sapply(y,function(x){x-a}))
for (i in 1:n){
  for (j in 1:n){
    xnew[(i-1)*n+j,]<-b[j]*(X[i,]-X[j,]);
  }
}
xgh<-rep(0,n^2); 
for(jj in 1:d){
  xgh<-xgh+xnew[,jj]*beta.old[jj]/h; 
}
wts<-stats::dnorm(xgh);

but I think the correct way should be

xdiff <- matrix(0,nrow=n^2,ncol=d)
for (i in 1:n){
  for (j in 1:n){
    xdiff[(i-1)*n+j,]<- (X[i,]-X[j,]);
  }
}
xgh<-rep(0,n^2); 
for(jj in 1:d){
  xgh<-xgh+xdiff[,jj]*beta.old[jj]/h; 
}

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