arma_est_bip_s: arma_est_bip_s The function arma_est_bip_mm(x,p,q) comuptes...

In Mufabo/Rrobustsp: Robust Signal Processing

arma_est_bip_s The function arma_est_bip_mm(x,p,q) comuptes BIP S-estimates of the ARMA model parameters. It also computes an outlier cleaned signal using BIP-ARMA(p,q) predictions

Description

arma_est_bip_s The function arma_est_bip_mm(x,p,q) comuptes BIP S-estimates of the ARMA model parameters. It also computes an outlier cleaned signal using BIP-ARMA(p,q) predictions

Usage

arma_est_bip_s(x, p, q, tolX = 0.01)

Arguments

x:	data (observations/measurements/signal)
p:	autoregressive order
q:	moving-average order
tolX:	numeric. Threshold passed to pracma::lsqnonlin. Default = 1e-2

Value

result: named list with following fields

ar_coeffs	numeric vector of length p. BIP-AR(p) S estimates
ma_coeffs	numeric vector of length q. BIP-AR(q) S estimates
inno_scale	numeric, BIP s-estimate of the innovations scale
ar_coeffs_init	numeric vector of length p. Robust starting point for estimation
ma_coeffs_init	numeric vector of length q. Robust starting point for estimation

Note

file is in dependentData_armaEstBipS.R

References

"Robust Statistics for Signal Processing" Zoubir, A.M. and Koivunen, V. and Ollila, E. and Muma, M. Cambridge University Press, 2018.

"Bounded Influence Propagation τ-Estimation: A New Robust Method for ARMA Model Estimation." Muma, M. and Zoubir, A.M. IEEE Transactions on Signal Processing, 65(7), 1712-1727, 2017.

Examples

library(signal)
library(pracma)
N <- 500
a <- rnorm(N)
p <- 1
q <- 0
x <- signal::filter(1, c(1, -0.8), a)

arma_est_bip_s(x, p, q)

Mufabo/Rrobustsp documentation built on June 11, 2022, 10:41 p.m.