The Annals of Statistics

Large Sample Theory of Estimation in Biased Sampling Regression Models. I

Peter J. Bickel and J. Ritov

Full-text: Open access

Abstract

Biased sampling regression models were introduced by Jewell, generalizing the truncated regression model studied by Bhattacharya, Chernoff and Yang. If the independent variable takes on only a finite number of values (as does the stratum variable), we show: 1. That if the slope of the underlying regression model is assumed known, then the nonparametric maximum likelihood estimates of the distribution of the independent and dependent variables (a) can be calculated from ordinary $M$ estimates; (b) are asymptotically efficient. 2. How to construct $M$ estimates of the slope which are always $\sqrt n$ consistent, asymptotically Gaussian and are efficient locally, for example, if the error distribution is Gaussian. We support our asymptotics with a small simulation.

Article information

Source
Ann. Statist., Volume 19, Number 2 (1991), 797-816.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176348121

Digital Object Identifier
doi:10.1214/aos/1176348121

Mathematical Reviews number (MathSciNet)
MR1105845

Zentralblatt MATH identifier
0742.62036

JSTOR
links.jstor.org

Subjects
Primary: 62G05: Estimation
Secondary: 62G20: Asymptotic properties

Keywords
Regression biased sampling nonparametric maximum likelihood $M$ estimates

Citation

Bickel, Peter J.; Ritov, J. Large Sample Theory of Estimation in Biased Sampling Regression Models. I. Ann. Statist. 19 (1991), no. 2, 797--816. doi:10.1214/aos/1176348121. https://projecteuclid.org/euclid.aos/1176348121


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