Nonparametric Estimation of Regression Parameters from Censored Data with Two Discrete Covariates

Mohammad H. Rahbar
Additional contact information
Mohammad H. Rahbar: Mohammad Rahbar — University of Wisconsin-La Crosse, Math Department

A chapter in Lifetime Data: Models in Reliability and Survival Analysis, 1996, pp 257-262 from Springer

Abstract: Abstract A nonparametric method of estimation is presented in a multiple regression model where there are two discrete independent variables (covariates) and the dependent variable (response) is randomly right censored. The censoring distribution may depend on the covariates. The efficiency of our non-iterative estimators is compared with the other non-iterative estimators in the literature using simulations. The asymptotic normality of the estimators of the regression parameters is established. In addition, the distributions of estimators of the asymptotic variances are obtained.

Date: 1996
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:sprchp:978-1-4757-5654-8_34

Ordering information: This item can be ordered from
http://www.springer.com/9781475756548

DOI: 10.1007/978-1-4757-5654-8_34

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().