Robust model selection in regression via weighted likelihood methodology

By Claudio Agostinelli. 2002

51 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3ad6415e93e043f024042a87bba42540d0a61b6a

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In Statistics and Probability

1. Gaorong Li
2. Lixing Zhu
3. Samuel Müller
4. Heng Peng
5. Helen Lindsay
6. Xiaobei Zhou
7. Alan H. Welsh
8. Mark D. Robinson

Citations in the corpus

• Outlier Robust Model Selection in Linear Regression (2005, methodology): “, Hampel 1983; Ronchetti 1985, 1997; Ronchetti and Staudte 1994; Sommer and Staudte 1995; Sommer and Huggins 1996; Ronchetti, Field, and Blanchard 1997; Qian and Künsch 1998; Agostinelli 2002; Miller 2002; Wisnowski, Simpson, Montgomery, and Runger 2003). These approaches are based either on robust ”
• Robustly detecting differential expression in RNA sequencing data using observation weights (2013, background): “Of particular importance, after adding observation weights, the asymptotic theory suggests that likelihood ratio statistics of model parameters still converge to approximate chi-squared distributions under the null hypothesis [1].”
• NONCONCAVE PENALIZED M-ESTIMATION WITH A DIVERGING NUMBER OF PARAMETERS (2011, methodology): “Agostinelli (2002) used weighted likelihood to increase the robustness of model selection.”