# cckde: Continuous convolution density estimator In cctools: Tools for the Continuous Convolution Trick in Nonparametric Estimation

## Description

The continuous convolution kernel density estimator is defined as the classical kernel density estimator based on continuously convoluted data (see `cont_conv()`). `cckde()` fits the estimator (including bandwidth selection), `dcckde()` and `predict.cckde()` can be used to evaluate the estimator.

## Usage

 ```1 2 3 4 5 6``` ```cckde(x, bw = NULL, mult = 1, theta = 0, nu = 5, ...) dcckde(x, object) ## S3 method for class 'cckde' predict(object, newdata, ...) ```

## Arguments

 `x` a matrix or data frame containing the data (or evaluation points). `bw` vector of bandwidth parameter; if `NULL`, the bandwidths are selected automatically by likelihood cross validation. `mult` bandwidth multiplier; either a positive number or a vector of such. Each bandwidth parameter is multiplied with the corresponding multiplier. `theta` scale parameter of the USB distribution (see, `dusb()`). `nu` smoothness parameter of the USB distribution (see, `dusb()`). The estimator uses the Epanechnikov kernel for smoothing and the USB distribution for continuous convolution (default parameters correspond to the uniform distribution on [-0.5, 0.5]. `...` unused. `object` `cckde` object. `newdata` matrix or data frame containing evaluation points.

## Details

If a variable should be treated as ordered discrete, declare it as `ordered()`, factors are expanded into discrete dummy codings.

## References

Nagler, T. (2017). A generic approach to nonparametric function estimation with mixed data. arXiv:1704.07457

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```# dummy data with discrete variables dat <- data.frame( F1 = factor(rbinom(10, 4, 0.1), 0:4), Z1 = ordered(rbinom(10, 5, 0.5), 0:5), Z2 = ordered(rpois(10, 1), 0:10), X1 = rnorm(10), X2 = rexp(10) ) fit <- cckde(dat) # fit estimator dcckde(dat, fit) # evaluate density predict(fit, dat) # equivalent ```

cctools documentation built on May 2, 2019, 8:51 a.m.