Estimation in a Gaussian linear regression model under type II censoring

Kishan G. Mehrotra, Gouri K. Bhattacharyya

Research output: Contribution to journalArticlepeer-review

Abstract

In the setting of a Gaussian linear regression model, estimation of the regression parameters is considered when a fixed number of design points are replicated in the experiment, and the response data at each design point are type II censored. A judicious approximation of the likelihood equation leads to closed form solutions which are analogous to the maximum likelihood estimators in the uncensored case. The exact and asymptotic properties of these modified maximum likelihood estimators are investigated, and efficiency comparisons are made with the maximum likelihood estimators.

Original languageEnglish (US)
Pages (from-to)241-250
Number of pages10
JournalJournal of Statistical Planning and Inference
Volume17
Issue numberC
DOIs
StatePublished - 1987

Keywords

  • Asymptotic normality
  • Censored samples
  • Modified maximum likelihood
  • Regression analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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