Linear estimators for Gaussian random variables in Hilbert spaces

We study a statistical model for infinite dimensional Gaussian random variables with unknown parameters. For this model we derive linear estimators for the mean and the variance of the Gaussian distribution. Furthermore, we construct confidence intervals and perform hypothesis testing. A linear regression problem in infinite dimensions and some perspectives to statistical and machine learning are presented as well.

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Journal
Theory of Probability and Mathematical Statistics
Published
2025-05-09
DOI
https://doi.org/10.1090/tpms/1230
Primary Topic
Statistical Methods and Inference
Type
article
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article

Linear estimators for Gaussian random variables in Hilbert spaces

Stefan Tappe
Theory of Probability and Mathematical Statistics
Statistical Methods and Inference
article

Linear estimators for Gaussian random variables in Hilbert spaces

Stefan Tappe
article en

Abstract

We study a statistical model for infinite dimensional Gaussian random variables with unknown parameters. For this model we derive linear estimators for the mean and the variance of the Gaussian distribution. Furthermore, we construct confidence intervals and perform hypothesis testing. A linear regression problem in infinite dimensions and some perspectives to statistical and machine learning are presented as well.

Theory of Probability and Mathematical StatisticsVol. 112(0)
University of Freiburg (DE)
Openalex Percentile: Top 90%
Statistical Methods and Inference
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Linear estimators for Gaussian random variables in Hilbert spaces — Stefan Tappe · Theory of Probability and Mathematical Statistics (2025) | TGRS Research Map | TGRS