TY - JOUR
T1 - Robust parameter estimation for stationary processes by an exotic disparity from prediction problem
AU - Liu, Yan
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2017/10
Y1 - 2017/10
N2 - A new class of disparities from the point of view of prediction problem is proposed for minimum contrast estimation of spectral densities of stationary processes. We investigate asymptotic properties of the minimum contrast estimators based on the new disparities for stationary processes with both finite and infinite variance innovations. The relative efficiency and the robustness against randomly missing observations are shown in our numerical simulations.
AB - A new class of disparities from the point of view of prediction problem is proposed for minimum contrast estimation of spectral densities of stationary processes. We investigate asymptotic properties of the minimum contrast estimators based on the new disparities for stationary processes with both finite and infinite variance innovations. The relative efficiency and the robustness against randomly missing observations are shown in our numerical simulations.
KW - Asymptotic efficiency
KW - Minimum contrast estimation
KW - Prediction problem
KW - Spectral density
KW - Stationary process
UR - http://www.scopus.com/inward/record.url?scp=85020394505&partnerID=8YFLogxK
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U2 - 10.1016/j.spl.2017.05.005
DO - 10.1016/j.spl.2017.05.005
M3 - Article
AN - SCOPUS:85020394505
VL - 129
SP - 120
EP - 130
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
SN - 0167-7152
ER -