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Rank‐based estimation for autoregressive moving average time series models
Author(s) -
Andrews Beth
Publication year - 2008
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2007.00545.x
Subject(s) - mathematics , estimator , asymptotic distribution , autoregressive model , strong consistency , rank (graph theory) , consistency (knowledge bases) , series (stratigraphy) , autoregressive–moving average model , residual , star model , asymptotic analysis , statistics , m estimator , autoregressive integrated moving average , time series , combinatorics , algorithm , paleontology , geometry , biology
. We establish asymptotic normality and consistency for rank‐based estimators of autoregressive‐moving average model parameters. The estimators are obtained by minimizing a rank‐based residual dispersion function similar to the one given by L.A. Jaeckel [Ann. Math. Stat. Vol. 43 (1972) 1449–1458]. These estimators can have the same asymptotic efficiency as maximum likelihood estimators and are robust. The quality of the asymptotic approximations for finite samples is studied via simulation.