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	सुदृढ़ समय श्रृंखला विश्लेषण	Robust Linear Mixed-Effects Model
क्षेत्र	सांख्यिकी	सांख्यिकी
परिवार	Regression model	Regression model
उद्भव वर्ष	2019	2016
प्रवर्तक	Maronna, Martin, Yohai & Salibián-Barrera (textbook treatment); robust estimation tradition	Richardson & Welsh (robust REML); Koller (robustlmm implementation)
प्रकार	Robust time series model (AR / MA / ARIMA)	Robust linear mixed-effects model
मौलिक स्रोत	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	Koller, M. (2016). robustlmm: An R Package for Robust Estimation of Linear Mixed-Effects Models. Journal of Statistical Software, 75(6), 1-24.
उपनाम	robust ARIMA, robust autoregressive model, outlier-resistant time series, Robust Zaman Serisi Analizi	robust mixed-effects model, robust linear mixed model, robust LMM, Robust Karma Etkiler Modeli
संबंधित	5	5
सारांश	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).	The robust mixed model is a linear mixed-effects model for panel and repeated-measures data that tolerates outliers and heavy-tailed errors. It replaces the usual likelihood with bounded-influence estimating equations, building on the robust restricted maximum likelihood of Richardson and Welsh (1995) and the robustlmm implementation of Koller (2016).