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Table 1 MSE performance of the proposed SMC methods for ARMA models (known a and b) with fGn, known and unknown \(\sigma _{u}^{2}\)

From: Sequential Monte Carlo for inference of latent ARMA time-series with innovations correlated in time

PF type State estimation error (MSE)
  Known \(\sigma _{u}^{2}\) Unknown \(\sigma _{u}^{2}\)
AR(1), H=0.5 1.1081 1.1945
AR(1), H=0.7 1.3946 1.4397
AR(1), H=0.9 1.1195 1.1970
MA(1), H=0.5 1.0223 1.0686
MA(1), H=0.7 1.0585 1.1136
MA(1), H=0.9 0.87374 0.94053
ARMA(1,1), H=0.5 1.5947 1.6197
ARMA(1,1), H=0.7 1.7852 1.8516
ARMA(1,1), H=0.9 1.7214 1.7362