Robust location estimators in regression models with covariates and responses missing at random

Ana M. Bianco, Graciela Boente, Wenceslao González-Manteiga and Ana Pérez-González

Journal of Nonparametric Statistics, 2020, vol. 32, issue 4, 915-939

Abstract: This paper deals with robust marginal estimation under a general regression model when missing data occur in the response and also in some covariates. The target is a marginal location parameter given through an M-functional. To obtain robust Fisher-consistent estimators, properly defined marginal distribution function estimators are considered. These estimators avoid the bias due to missing values assuming a missing at random condition. Three methods are considered to estimate the marginal distribution which allows to obtain the M-location of interest: the well-known inverse probability weighting, a convolution-based method that makes use of the regression model and an augmented inverse probability weighting procedure that prevents against misspecification. Different aspects of their asymptotic behaviour are derived under regularity conditions. The robust studied estimators and their classical relatives are compared through numerical experiments under different missing data models, including clean and contaminated samples. The methodology is illustrated through a real data set.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2020.1834108 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:32:y:2020:i:4:p:915-939

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2020.1834108

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().