The Annals of Statistics

Concavity and Estimation

Shelby J. Haberman

Full-text: Open access

Abstract

Simplified conditions are given for consistency and asymptotic normality of $M$-estimates derived by maximization of averages of independent identically distributed random concave functions. Applications are made to maximum likelihood estimation.

Article information

Source
Ann. Statist., Volume 17, Number 4 (1989), 1631-1661.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176347385

Digital Object Identifier
doi:10.1214/aos/1176347385

Mathematical Reviews number (MathSciNet)
MR1026303

Zentralblatt MATH identifier
0699.62027

JSTOR
links.jstor.org

Subjects
Primary: 62E20: Asymptotic distribution theory
Secondary: 62F10: Point estimation

Keywords
Asymptotic normality $M$-estimation maximum likelihood strong consistency

Citation

Haberman, Shelby J. Concavity and Estimation. Ann. Statist. 17 (1989), no. 4, 1631--1661. doi:10.1214/aos/1176347385. https://projecteuclid.org/euclid.aos/1176347385


Export citation