Maximum likelihood estimation for density as an infinite-dimensional Gaussian shift

S. G. Bobkov

doi:10.1007/BF01362787

Maximum likelihood estimation for density as an infinite-dimensional Gaussian shift

S. G. Bobkov

School of Mathematics

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

Abstract

A new approach is suggested for the nonparametric estimation of the unknown distribution density under the assumption of the bounded variation of the true density. As an estimator there occurs a statistic based on the application of the maximum likelihood method to the estimation of an infinite-dimensional shifi of a Gaussian process with a known correlation function. The quality of the obtained estimate is investigated.

Original language	English (US)
Pages (from-to)	1825-1826
Number of pages	2
Journal	Journal of Soviet Mathematics
Volume	61
Issue number	1
DOIs	https://doi.org/10.1007/BF01362787
State	Published - Aug 1 1992

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title = "Maximum likelihood estimation for density as an infinite-dimensional Gaussian shift",

abstract = "A new approach is suggested for the nonparametric estimation of the unknown distribution density under the assumption of the bounded variation of the true density. As an estimator there occurs a statistic based on the application of the maximum likelihood method to the estimation of an infinite-dimensional shifi of a Gaussian process with a known correlation function. The quality of the obtained estimate is investigated.",

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AB - A new approach is suggested for the nonparametric estimation of the unknown distribution density under the assumption of the bounded variation of the true density. As an estimator there occurs a statistic based on the application of the maximum likelihood method to the estimation of an infinite-dimensional shifi of a Gaussian process with a known correlation function. The quality of the obtained estimate is investigated.

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Maximum likelihood estimation for density as an infinite-dimensional Gaussian shift

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