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| 강건 계층적 선형 모형× | Robust Regression× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 2004 | 1964 |
| 창시자≠ | Maas & Hox (2004); Goldstein et al. (2018) | Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974) |
| 유형≠ | Robust multilevel regression | Regression with outlier resistance |
| 원전≠ | Maas, C. J. M., & Hox, J. J. (2004). Robustness issues in multilevel regression analysis. Statistica Neerlandica, 58(2), 127–137. DOI ↗ | Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ |
| 별칭 | robust HLM, robust multilevel model, robust mixed-effects linear model, robust nested regression | M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation |
| 관련≠ | 5 | 6 |
| 요약≠ | Robust Hierarchical Linear Model (Robust HLM) extends standard HLM by replacing or protecting its standard errors against violations of distributional assumptions — chiefly non-normal residuals, heteroscedasticity, and influential clusters. It retains the nested, two-level (or higher) structure while producing more trustworthy inference under real-world data conditions. | Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed. |
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