`rlcp`

Likelihood Cross Valation (LCV) is commonly used to select the bandwidth in kernel density estimations. It is, however, known to be sensitive to fat-tailed distributions and/or outliers. Wu (2019) proposed a Robust Likelihood Cross Validation (RLCV) method that is resistant against fat-tailedness and/or extreme observations.

This method replaces the log-likelihood function in the cross validation objective function with a linear approximation for densities smaller than a certain threshold. It can be interpreted as a combination of LCV (for non-extreme density values, which account for the bulk of the sample) and least squares cross validation (for a few small extreme density values, if they exist in the sample). An automatic thresholding rule, depending on the sample size, number of dimension and sample covariance, has been proposed. This makes this bandwidth-selector free of tuning parameters.

The R package `rlcv`

implements this method for uni- and
multi-variate kernel density estimation. Below we illustrate the RLCV
bandwidth selector with a few examples. We start with a univariate
log-normal distribution. This distribution has an extended right tail.
The commands for univarite LCV and RLCV are respectively
`lcv`

and `rlcv`

. As is discussed in Wu (2019),
LCV tends to oversmooth in the presence of long-tails. The comparison of
these two bandwidths is consistent with the theoretical projection.

```
library(rlcv)
set.seed(12345)
=rlnorm(300)
x=lcv(x.obs=x)
fit1=rlcv(x.obs=x)
fit2c(fit1$h,fit2$h)
#> [1] 0.3807475 0.1546201
```

Let’s examine the goodness-of-fit numerically and visually. LCV misses the peak badly, while RLCV appears to be a bit rugged. At the same time, RLCV clearly has a smaller mse.

```
=seq(0,10,length=100)
x.new=dlnorm(x.new)
f0=kde(x.obs=x,x.new=x.new,h=fit1$h)
f1=kde(x.obs=x,x.new=x.new,h=fit2$h)
f2# compare mean squared error
mean((f0-f1)^2)
#> [1] 0.003178948
mean((f0-f2)^2)
#> [1] 0.001165473
# density plots
matplot(x.new,cbind(f0,f1,f2),col=c("black","green","red"),type='l',xlab='x',ylab='')
legend('right',legend = c('true','LCV','RLCV'), col = c('black','green','red'), lty = 1:3, lwd = 1 , xpd = T )
```

Let’s move on to multivariate densities. The corresponding commands
are `lcv_d`

and `rlcv_d`

. Consider first the
old-faithful geyser data. It turns out these two methods yield almost
identical bandwidths. Not surprisingly, their contour plots are nearly
identical. Note that the joint distribution of the geyser data doesn’t
have fat tails or outliers. This experiment confirms that LCV and RLCV
produce similar results in the absence of fat-tails or outliers. This is
desirable as LCV is known to be efficient (but not robust).

```
=datasets::faithful
x=cbind(x[,1],x[,2])
x=lcv_d(x.obs=x)
fit1=rlcv_d(x.obs=x)
fit2$h
fit1#> [1] 0.2891278 0.1665945
$h
fit2#> [1] 0.2891273 0.1665954
=seq(min(x[,1])*.8,max(x[,1])*1.2,length=50)
x1=seq(min(x[,2])*.8,max(x[,2])*1.2,length=50)
x2=rep(x1,each=50)
x11=rep(x2,50)
x22=kde_d(x.new=cbind(x11,x22),x.obs=x,h=fit1$h)
f1=kde_d(x.new=cbind(x11,x22),x.obs=x,h=fit2$h)
f2filled.contour(x1,x2,matrix(f1,50,50))
```

`filled.contour(x1,x2,matrix(f2,50,50))`

Lastly we look into a fat-tailed multivariate distribution. We
construct a bivariate distribution by combining two univariate
*t* distributions with five degrees of freedom via a *t*
copula with five degrees of freedom. Again with a fat-tailed underlying
distribution, LCV bandwidths tend to be larger. Although their contours
look similar in shape, examination of the mse suggests that RLCV fits
the true density better. The comparsion of their summary statistics also
reveals that LCV oversmoothes, yielding a large gap between the true
mode and fitted mode.

```
library(copula)
set.seed(12345)
=tCopula(.5,df=5)
ncop=500
n=rCopula(n,ncop)
u=qt(u[,1],5)
x1=qt(u[,2],5)
x2=cbind(x1,x2)
x=lcv_d(x.obs=x)
fit1=rlcv_d(x.obs=x)
fit2$h
fit1#> [1] 0.5186154 0.4273611
$h
fit2#> [1] 0.3637373 0.3632867
# evaluation data
=x2=seq(-5,5,length=50)
x1=rep(x1,each=50)
x11=rep(x2,50)
x22=kde_d(x.new=cbind(x11,x22),x.obs=x,,h=fit1$h)
f1=kde_d(x.new=cbind(x11,x22),x.obs=x,,h=fit2$h)
f2=dCopula(cbind(pt(x11,5),pt(x22,5)),ncop)*dt(x11,5)*dt(x22,5)
f0# Mean squared errors
mean((f0-f1)^2)
#> [1] 3.889088e-05
mean((f0-f2)^2)
#> [1] 1.959031e-05
# check summary statistics
summary(cbind(f0,f1,f2))
#> f0 f1 f2
#> Min. :4.330e-06 Min. :0.0000000 Min. :0.000e+00
#> 1st Qu.:2.019e-04 1st Qu.:0.0001575 1st Qu.:3.842e-05
#> Median :7.309e-04 Median :0.0012354 Median :1.071e-03
#> Mean :9.540e-03 Mean :0.0095453 Mean :9.551e-03
#> 3rd Qu.:4.385e-03 3rd Qu.:0.0068628 3rd Qu.:6.122e-03
#> Max. :1.820e-01 Max. :0.1243200 Max. :1.434e-01
# plot the fitted densities
filled.contour(x1,x2,matrix(f1,50,50))
```

`filled.contour(x1,x2,matrix(f2,50,50))`

**Reference**

Wu, Ximing (2019), “Robust Likelihood Cross Validation for Kernel
Density Estimation,” *Journal of Business and Economic
Statistics*, 37(4): 761-770.