NDLI: Minimum Proper Loss Estimators for <?Pub

Content Provider	IEEE Xplore Digital Library
Author	Holland, M.J. Ikeda, K.
Copyright Year	1991
Abstract	In this paper, we propose a methodology for systematically deriving estimators minimizing proper loss functions defined on parametric statistical models, by restricting ourselves to losses taking a functional form which allows for straightforward proofs of key properties. Proving propriety is tantamount to deriving a pairwise divergence quantity between probability measures, admitting a natural interpretation of minimum proper loss estimators as divergence minimizers. We show that proper losses of varying complexity can be readily constructed given propriety of losses taking a rudimentary form, and that for many important models, verifying desired properties in the rudimentary case is immediate. As a special case, we derive computationally tractable estimators requiring only the first and second moments, verify strict propriety properties, and empirically confirm their utility though parameter estimation tasks using both controlled simulations and real-world meteorological network data sets. Comparisons against numerous standard estimators show that the proposed estimators are at least competitive with, and often markedly superior to all standard references, uniformly across tasks, data sets, and model classes, suggesting a strong alternative to standard benchmarks in a wide variety of estimation problems.
Sponsorship	IEEE Signal Processing Society
Starting Page	704
Ending Page	713
Page Count	10
File Size	2376407
File Format	PDF
ISSN	1053587X
Volume Number	64
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Parametric statistics Standards Q measurement Loss measurement Parameter estimation Systematics statistical signal processing Density estimation parameter estimation proper loss function
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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