Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test)

Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test) Article

Hayes, Timothy, Baraldi, Amanda N, Coxe, Stefany. (2024). Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test) . BEHAVIOR RESEARCH METHODS, 56(8), 8608-8639. 10.3758/s13428-024-02494-1

cited authors

Hayes, Timothy; Baraldi, Amanda N; Coxe, Stefany

sustainable development goals

SDG 03: Good Health and Well-being

authors

Hayes, Timothy

publication date

December 1, 2024

published in

BEHAVIOR RESEARCH METHODS Journal

keywords

Auxiliary variables
INFERENCE
Missing at random
Missing data
Psychology
Psychology, Experimental
Psychology, Mathematical
R PACKAGE
Random forest
SELECTION
Social Sciences

FIU Discovery

Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little's MCAR test) Article

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sustainable development goals

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