dbacf-package {dbacf} | R Documentation |
Autocovariance function estimation via difference-based methods
Description
Difference-based (auto)covariance/correlation estimation in change point regression with stationary errors.
Provides bias-reducing methods for (auto)covariance-correlation
estimation in change point regression with stationary m
-dependent errors
without having to pre-estimate the underlying signal of the observations.
In the same spirit, provides a robust estimator of the autorregressive coefficient
in change point regression with stationary, AR(1)
errors.
It also includes a general projection-based method for covariance matrix estimation.
Autocovariance Estimation
dbacf
returns and plots by default (auto)covariance/correlation
estimates without pre-estimating the underlying not necessarily smooth
signal of observations with stationary m
-dependent errors. The corresponding
plot method plot.dbacf
allows for adjusting graphical
parameters to users' liking. This method is based on plot.acf
.
dbacf_AR1
returns (auto)covariance/correlation estimates while
circumventing the difficult estimation of the underlying change point regression
function from observations with stationary AR(1)
errors.
Covariance Matrix Estimation
Given a matrix estimate, not necessarily positive definite, of
the covariance matrix of a stationary process,
nearPDToeplitz
returns the nearest, in the Frobenius norm,
covariance matrix to the initial estimate. See projectToeplitz
for the projection of a given symmetric matrix onto the space of Toeplitz matrices.
See also symBandedToeplitz
for creating a (stationary process'
large covariance) matrix by specifying its dimension and values of its
autocovariance function.
Author(s)
Tecuapetla-Gómez, I. itecuap@conabio.gob.mx
References
Grigoriadis, K.M., Frazho, A., Skelton, R. (1994). Application of alternating convex projection methods for computation of positive Toeplitz matrices, IEEE Transactions on signal processing 42(7), 1873–1875.
N. Higham (2002). Computing the nearest correlation matrix - a problem from finance, Journal of Numerical Analysis 22, 329–343.
Tecuapetla-Gómez, I and Munk, A. (2017). Autocovariance
estimation in regression with a discontinuous signal and m
-dependent errors: A
difference-based approach. Scandinavian Journal of Statistics, 44(2), 346–368.
Levine, M. and Tecuapetla-Gómez, I. (2023). Autocovariance
function estimation via difference schemes for a semiparametric change point model
with m
-dependent errors. Submitted.