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Partial F ‐tests with multiply imputed data in the linear regression framework via coefficient of determination
Author(s) -
Chaurasia Ashok,
Harel Ofer
Publication year - 2014
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6334
Subject(s) - linear regression , imputation (statistics) , regression , simple linear regression , computer science , statistics , regression analysis , scalar (mathematics) , missing data , mathematics , data mining , geometry
Tests for regression coefficients such as global, local, and partial F ‐tests are common in applied research. In the framework of multiple imputation, there are several papers addressing tests for regression coefficients. However, for simultaneous hypothesis testing, the existing methods are computationally intensive because they involve calculation with vectors and (inversion of) matrices. In this paper, we propose a simple method based on the scalar entity, coefficient of determination, to perform (global, local, and partial) F ‐tests with multiply imputed data. The proposed method is evaluated using simulated data and applied to suicide prevention data. Copyright © 2014 John Wiley & Sons, Ltd.