@inproceedings{9c24e6eee70c4babaebabc07c9ef269a,
title = "Invariant embedding technique and its applications for improvement or optimization of statistical decisions",
abstract = "In the present paper, for improvement or optimization of statistical decisions under parametric uncertainty, a new technique of invariant embedding of sample statistics in a performance index is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, an invariant embedding technique is independent of the choice of priors. It allows one to eliminate unknown parameters from the problem and to find the best invariant decision rule, which has smaller risk than any of the well-known decision rules. To illustrate the proposed technique, application examples are given.",
keywords = "decision rule, improvement, invariant embedding technique, optimization, Parametric uncertainty",
author = "Nicholas Nechval and Maris Purgailis and Gundars Berzins and Kaspars Cikste and Juris Krasts and Konstantin Nechval",
year = "2010",
doi = "10.1007/978-3-642-13568-2\_22",
language = "English",
isbn = "3642135676",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "306--320",
booktitle = "Analytical and Stochastic Modeling Techniques and Applications - 17th International Conference, ASMTA 2010, Proceedings",
note = "17th International Conference on Analytical and Stochastic Modeling Techniques and Applications, ASMTA 2010 ; Conference date: 14-06-2010 Through 16-06-2010",
}