NxKREG - Kernel Regression

Returns the (Nadaraya–Watson) kernel regression.

Syntax

X

is the x-component of the input data table (a one dimensional array of cells (e.g. rows or columns)).

Y

is the y-component (i.e. function) of the input data table (a one dimensional array of cells (e.g. rows or columns)).

P

is the polynomial order (0 = constant, 1= linear, 2=Quadratic, 3=Cubic, etc.), etc.). If missing, P = 0.

Kernel

is the weighting kernel function used with KNN-Regression method : 0(or missing)=Uniform, 1=Triangular, 2=Epanechnikov, 3=Quartic, 4=Triweight, 5=Tricube, 6=Gaussian, 7=Cosine, 8=Logistic, 9= Sigmoid, 10= Silverman.

Value	Kernel
0	Uniform Kernel (default)
1	Triangular Kernel
2	Epanechnikov Kernel
3	Quartic Kernel
4	Triweight Kernel
5	Tricube Kernel
6	Gaussian Kernel
7	Cosine Kernel
8	Logistic Kernel
9	Sigmoid Kernel
10	Silverman Kernel

H

is the smoothing parameter (bandwidth) of the kernel density estimator. If missing, a default value of one(1) is assumed.

Optimize

is a flag (True/False) for searching and using optimal integer value K (i.e. number of data-points). If missing or omitted, optimize is assumed False.

target

is the desired x-value(s) to interpolate for (a single value or a one dimensional array of cells (e.g. rows or columns)).

Return

is a number that determines the type of return value: 0=Forecast (default), 1=errors, 2=Smoothing parameter (bandwidth), 3=RMSE (CV). If missing or omitted, NxREG returns forecast/regression value(s).

Return	Description
0	Forecast/Regression value(s) (default)
1	Forecast/Regression error(s)
2	Kernel Smoothing parameter (badwidth)
3	RMSE (cross-validation)

Remarks