کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8919485 | 1642891 | 2018 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A data-cleaning augmented Kalman filter for robust estimation of state space models
ترجمه فارسی عنوان
یک تمیز کردن داده، فیلتر کلمن را برای برآورد مدل های فضای حالت، افزوده می کند
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
چکیده انگلیسی
A robust augmented Kalman filter (AKF) is presented for the general state space model featuring non-stationary and regression effects. The robust filter shrinks the observations towards their one-step-ahead prediction based on the past, by bounding the effect of the information carried by a new observation according to an influence function. When maximum likelihood estimation is carried out on the replacement data, an M-type estimator is obtained. The performance of the robust AKF is investigated in two applications using as a modeling framework the basic structural time series model-a popular unobserved components model in the analysis of seasonal time series. First, a Monte Carlo experiment is conducted in order to evaluate the comparative accuracy of the proposed method for estimating the variance parameters. Second, the method is applied in a forecasting context to a large set of European trade statistics series.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Econometrics and Statistics - Volume 5, January 2018, Pages 107-123
Journal: Econometrics and Statistics - Volume 5, January 2018, Pages 107-123
نویسندگان
Martyna Marczak, Tommaso Proietti, Stefano Grassi,