Efficiency of a linear combination of the median and the sample mean: The double truncated normal distribution

Gabriela Damilano; Pedro Puig

doi:10.1111/j.1467-9469.2004.00411.x

Efficiency of a linear combination of the median and the sample mean: The double truncated normal distribution

Gabriela Damilano, Pedro Puig

Department of Mathematics

Research output: Contribution to journal › Article › Research › peer-review

5 Citations (Scopus)

Abstract

We characterize all symmetric location models for which a linear combination of the median and the sample mean is an asymptotically efficient estimator of the location parameter. The resulting model can be understood as a symmetrized or double truncated normal distribution. A simple algorithm to estimate the parameters is given and an application is presented.

Original language	English
Pages (from-to)	629-637
Journal	Scandinavian Journal of Statistics
Volume	31
Issue number	4
DOIs	https://doi.org/10.1111/j.1467-9469.2004.00411.x
Publication status	Published - 1 Jan 2004

Keywords

Asymptotically efficient estimator
Location and scale models
Pseudolikelihood
Symmetric location models
Truncated normal distribution

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10.1111/j.1467-9469.2004.00411.x

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title = "Efficiency of a linear combination of the median and the sample mean: The double truncated normal distribution",

abstract = "We characterize all symmetric location models for which a linear combination of the median and the sample mean is an asymptotically efficient estimator of the location parameter. The resulting model can be understood as a symmetrized or double truncated normal distribution. A simple algorithm to estimate the parameters is given and an application is presented.",

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KW - Asymptotically efficient estimator

KW - Location and scale models

KW - Pseudolikelihood

KW - Symmetric location models

KW - Truncated normal distribution

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DO - 10.1111/j.1467-9469.2004.00411.x

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Efficiency of a linear combination of the median and the sample mean: The double truncated normal distribution

Abstract

Keywords

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