Details
| Original language | German |
|---|---|
| Title of host publication | International Work-Conference on Time Series Analysis |
| Subtitle of host publication | ITISE 2017: Time Series Analysis and Forecasting, Contributions to Statistics |
| Editors | I. Rojas , H. Pomares , O. Valenzuela |
| Pages | 323-337 |
| Number of pages | 14 |
| ISBN (electronic) | 978-3-319-96944-2 |
| Publication status | Published - 4 Oct 2018 |
Abstract
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International Work-Conference on Time Series Analysis: ITISE 2017: Time Series Analysis and Forecasting, Contributions to Statistics. ed. / I. Rojas ; H. Pomares ; O. Valenzuela . 2018. p. 323-337.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Further Results on a Modified EM Algorithm for Parameter Estimation in Linear Models with Time-Dependent Autoregressive and t-Distributed Errors
AU - Kargoll, Boris
AU - Alkhatib, Hamza
AU - Omidalizarandi, Mohammad
AU - Schuh, Wolf-Dieter
PY - 2018/10/4
Y1 - 2018/10/4
N2 - In this contribution, we consider an expectation conditional maximization either (ECME) algorithm for the purpose of estimating the parameters of a linear observation model with time-dependent autoregressive (AR) errors. The degree of freedom (d.o.f.) of the underlying family of scaled t-distributions, which is used to account for outliers and heavy-tailedness of the white noise components, is adapted to the data, resulting in a self-tuning robust estimator. The time variability of the AR coefficients is described by a second linear model. We improve the estimation of the d.o.f. in a previous version of the ECME algorithm, which involves a zero search, by using an interval Newton method. We model the transient oscillations of a shaker table measured by a high-accuracy accelerometer, and we analyze various criteria for selecting a simultaneously parsimonious and realistic time-variability model.
AB - In this contribution, we consider an expectation conditional maximization either (ECME) algorithm for the purpose of estimating the parameters of a linear observation model with time-dependent autoregressive (AR) errors. The degree of freedom (d.o.f.) of the underlying family of scaled t-distributions, which is used to account for outliers and heavy-tailedness of the white noise components, is adapted to the data, resulting in a self-tuning robust estimator. The time variability of the AR coefficients is described by a second linear model. We improve the estimation of the d.o.f. in a previous version of the ECME algorithm, which involves a zero search, by using an interval Newton method. We model the transient oscillations of a shaker table measured by a high-accuracy accelerometer, and we analyze various criteria for selecting a simultaneously parsimonious and realistic time-variability model.
U2 - 10.1007/978-3-319-96944-2_22
DO - 10.1007/978-3-319-96944-2_22
M3 - Aufsatz in Konferenzband
SN - 978-3-319-96943-5
SP - 323
EP - 337
BT - International Work-Conference on Time Series Analysis
A2 - Rojas , I.
A2 - Pomares , H.
A2 - Valenzuela , O.
ER -