CoDa_BayesNW: Compositional data analytic approach and nonparametric...

In ftsa: Functional Time Series Analysis

Compositional data analytic approach and nonparametric function-on-function regression for forecasting density

Description

Log-ratio transformation from constrained space to unconstrained space, where a standard nonparametric function-on-function regression can be applied.

Usage

CoDa_BayesNW(data, normalization, m = 5001, 
	band_choice = c("Silverman", "DPI"), 
	kernel = c("gaussian", "epanechnikov"))

Arguments

data	Densities or raw data matrix of dimension N by p, where N denotes sample size and p denotes dimensionality
normalization	If a standardization should be performed?
m	Grid points within the data range
band_choice	Selection of optimal bandwidth
kernel	Type of kernel function

Details

1) Compute the geometric mean function 2) Apply the centered log-ratio transformation 3) Apply a nonparametric function-on-function regression to the transformed data 4) Transform forecasts back to the compositional data 5) Add back the geometric means, to obtain the forecasts of the density function

Value

Out-of-sample density forecasts

Author(s)

Han Lin Shang

References

Egozcue, J. J., Diaz-Barrero, J. L. and Pawlowsky-Glahn, V. (2006) ‘Hilbert space of probability density functions based on Aitchison geometry’, Acta Mathematica Sinica, 22, 1175-1182.

Ferraty, F. and Shang, H. L. (2021) ‘Nonparametric density-on-density regression’, working paper.

See Also

CoDa_FPCA

Examples

## Not run: 
CoDa_BayesNW(data = DJI_return, normalization = "TRUE", 
		band_choice = "DPI", kernel = "epanechnikov")

## End(Not run)	

ftsa documentation built on Sept. 11, 2023, 5:09 p.m.