# CV bandwidth selector for density Computes Cross Validation bandwidth selector for the Parzen--Rosenblatt density estimator... ```r denCVBwSelC(x, kernel = gaussK, weig = rep(1, length(x)), interval = .lokestOptInt) ``` ## Arguments - `x`: vector with data points. - `kernel`: Kernel used to perform the estimation, see `Kernels`. - `weig`: Vector of weights for observations. - `interval`: A range of values where to look for the bandwidth parameter. ## Details The selector is implemented using its definition. ## Returns A numeric value with the bandwidth. ## References Fan, J. and Gijbels, I. **Local polynomial modelling and its applications\/**. Chapman & Hall, London (1996). Wand, M.~P. and Jones, M.~C. **Kernel smoothing\/**. Chapman and Hall Ltd., London (1995). ## Author(s) Jorge Luis Ojeda Cabrera. ## See Also `bw.nrd0`, `dpik`. ## Examples ```r stdy <- function(size=100,rVar=rnorm,dVar=dnorm,kernel=gaussK,x=NULL) { if( is.null(x) ) x <- rVar(size) Tc <- system.time( dbwc <- denCVBwSelC(x,kernel) )[3] ucvT <- system.time( ucvBw <- bw.ucv(x,lower=0.00001,upper=2.0) )[3] nrdT <- system.time( nrdBw <- bw.nrd(x) )[3] { xeval <- seq( min(x)+dbwc , max(x)-dbwc ,length=50) hist(x,probability=TRUE) lines(xeval,trueDen <- dVar(xeval),col="red") lines(density(x),col="cyan") xevalDenc <- PRDenEstC(x,xeval,dbwc,kernel) dencMSE <- mean( (trueDen-xevalDenc)^2 ) xevalucvDen <- PRDenEstC(x,xeval,ucvBw,kernel) ucvMSE <- mean( (trueDen-xevalucvDen)^2 ) xevalDenNrd <- PRDenEstC(x,xeval,nrdBw,kernel) nrdMSE <- mean( (trueDen-xevalDenNrd)^2 ) lines(xevalDenc,col="green") lines(xevalucvDen,col="blue") lines(xevalDenNrd,col="grey") } return( cbind( bwVal=c(evalC=dbwc,ucvBw=ucvBw,nrdBw=nrdBw), mse=c(dencMSE,ucvMSE,nrdMSE), time=c(Tc,ucvT,nrdT) ) ) } stdy(100,kernel=gaussK) stdy(100,rVar=rexp,dVar=dexp,kernel=gaussK) ## check stdy with other kernel, distributions ```

denCVBwSelC function