# Kernel.asymmetric: Asymmetric Smoothing Kernel In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

 Kernel.asymmetric R Documentation

## Asymmetric Smoothing Kernel

### Description

Represent Asymmetric Smoothing Kernels: normal, cosine, triweight, quartic and uniform.

 AKer.norm=ifelse(u>=0,2*dnorm(u),0) AKer.cos=ifelse(u>=0,pi/2*(cos(pi*u/2)),0) AKer.epa=ifelse(u>=0 & u<=1,3/2*(1-u^2),0) AKer.tri=ifelse(u>=0 & u<=1,35/16*(1-u^2)^3,0) AKer.quar=ifelse(u>=0 & u<=1,15/8*(1-u^2)^2,0) AKer.unif=ifelse(u>=0 & u<=1,1,0)

### Usage

```Kernel.asymmetric(u, type.Ker = "AKer.norm")
```

### Arguments

 `u` Data. `type.Ker` Type of asymmetric metric kernel, by default asymmetric normal kernel.

### Details

Type of Asymmetric kernel:

 Asymmetric Normal Kernel: `AKer.norm` Asymmetric Cosine Kernel: `AKer.cos` Asymmetric Epanechnikov Kernel: `AKer.epa` Asymmetric Triweight Kernel: `AKer.tri` Asymmetric Quartic Kernel: `AKer.quar` Asymmetric Uniform Kernel: `AKer.unif`

### Value

Returns asymmetric kernel.

### Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

### References

Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.

Hardle, W. Applied Nonparametric Regression. Cambridge University Press, 1994.

### Examples

```y=qnorm(seq(.1,.9,len=100))
a<-Kernel.asymmetric(u=y)
b<-Kernel.asymmetric(type.Ker="AKer.tri",u=y)
c=AKer.cos(y)
```

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.