Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresie logistică robustă× | Analiză robustă a seriilor de timp× | |
|---|---|---|
| Domeniu | Statistică | Statistică |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2001 | 2019 |
| Autorul original≠ | Cantoni & Ronchetti (2001); Bondell (2008) | Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition |
| Tip≠ | Robust generalized linear model (binary outcome) | Robust time series model (AR / MA / ARIMA) |
| Sursa seminală≠ | Cantoni, E. & Ronchetti, E. (2001). Robust Inference for Generalized Linear Models. Journal of the American Statistical Association, 96(455), 1022-1030. DOI ↗ | Maronna, R. A., Martin, R. D., Yohai, V. J., & Salibián-Barrera, M. (2019). Robust Statistics: Theory and Methods (with R) (2nd ed.). Wiley. ISBN: 978-1119214687 |
| Denumiri alternative | robust binary regression, weighted logistic regression, Mallows-type logistic regression, Robust Lojistik Regresyon | robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi |
| Înrudite | 5 | 5 |
| Rezumat≠ | Robust Logistic Regression is a variant of logistic regression that is resistant to outliers and leverage points, fitting a binary or categorical outcome with Mallows-type weighted estimation. The robust framework for generalized linear models was developed by Cantoni and Ronchetti (2001), with a weighting approach later refined by Bondell (2008). | Robust Time Series Analysis fits autoregressive, moving-average, and ARIMA models to series that contain outliers or structural breaks, using M-estimation or MM-estimation instead of ordinary least squares so that a few anomalous observations do not distort the fit. It follows the robust statistics tradition consolidated in Maronna, Martin, Yohai and Salibián-Barrera (2019). |
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