کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1147309 957574 2006 22 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Maximum likelihood estimation for all-pass time series models
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز عددی
پیش نمایش صفحه اول مقاله
Maximum likelihood estimation for all-pass time series models
چکیده انگلیسی

An autoregressive-moving average model in which all roots of the autoregressive polynomial are reciprocals of roots of the moving average polynomial and vice versa is called an all-pass time series model. All-pass models generate uncorrelated (white noise) time series, but these series are not independent in the non-Gaussian case. An approximate likelihood for a causal all-pass model is given and used to establish asymptotic normality for maximum likelihood estimators under general conditions. Behavior of the estimators for finite samples is studied via simulation. A two-step procedure using all-pass models to identify and estimate noninvertible autoregressive-moving average models is developed and used in the deconvolution of a simulated water gun seismogram.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 97, Issue 7, August 2006, Pages 1638-1659