A proper selection among multiple Buckley–James estimates

Qiqing Yu ()
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Qiqing Yu: Binghamton University, SUNY

Metrika: International Journal for Theoretical and Applied Statistics, 2024, vol. 87, issue 6, No 4, 713-728

Abstract: Abstract Consider the semiparametric linear regression estimation problem with right-censored data. Under right censoring, the Buckley–James estimator (BJE) is the standard extension of the least squares estimator. Moreover, an iterative algorithm for the BJE has been implemented in R package called rms. We show that it often does not yield a solution, even if a consistent BJE exists. Yu and Wong (J Stat Comput Simul 72:451–460, 2002) proposed another algorithm to find all possible BJEs. The latter algorithm is modified in this paper so that it indeed finds all BJEs when the underlying regression parameter vector is identifiable. We show that some of these BJE’s can be inconsistent. Thus it is important to decide how to select a proper BJE such that it is consistent if the parameter is identifiable. We suggest either choose one close to the modified semi-parametric maximum likelihood estimator (Yu and Wong in Technometrics 47:34–42, 2005) or a finite boundary point if there are infinitely many BJEs.

Keywords: Linear regression; Multiple solutions; Semi-parametric model; Consistency; Primary 62 J05; Secondary 62 G05 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00184-023-00933-1

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