Journal of the Royal Statistical Society: Series C (Applied Statistics)

Subsample ignorable likelihood for regression analysis with missing data

Journal Article

Summary.  Two common approaches to regression with missing covariates are complete‐case analysis and ignorable likelihood methods. We review these approaches and propose a hybrid class, called subsample ignorable likelihood methods, which applies an ignorable likelihood method to the subsample of observations that are complete on one set of variables, but possibly incomplete on others. Conditions on the missing data mechanism are presented under which subsample ignorable likelihood gives consistent estimates, but both complete‐case analysis and ignorable likelihood methods are inconsistent. We motivate and apply the method proposed to data from the National Health and Nutrition Examination Survey, and we illustrate properties of the methods by simulation. Extensions to non‐likelihood analyses are also mentioned.

Related Topics

Related Publications

Related Content

Site Footer

Address:

This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.