Divergence measures for statistical data processing—An annotated bibliography
Divergence measures for statistical data processing—An annotated bibliography -
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Density-ratio matching under the bregman divergence: a unified framework of density-ratio estimation
Estimation of the ratio of probability densities has attracted a great deal of attention since it
can be used for addressing various statistical paradigms. A naive approach to density-ratio …
can be used for addressing various statistical paradigms. A naive approach to density-ratio …
Robust point set registration using gaussian mixture models
In this paper, we present a unified framework for the rigid and nonrigid point set registration
problem in the presence of significant amounts of noise and outliers. The key idea of this …
problem in the presence of significant amounts of noise and outliers. The key idea of this …
Families of alpha-beta-and gamma-divergences: Flexible and robust measures of similarities
In this paper, we extend and overview wide families of Alpha-, Beta-and Gamma-
divergences and discuss their fundamental properties. In literature usually only one single …
divergences and discuss their fundamental properties. In literature usually only one single …
[ספר][B] Statistical inference: the minimum distance approach
This book gives a comprehensive account of density-based minimum distance methods and
their use in statistical inference. It covers statistical distances, density-based minimum …
their use in statistical inference. It covers statistical distances, density-based minimum …
[ספר][B] Confidence, likelihood, probability
T Schweder, NL Hjort - 2016 - books.google.com
This lively book lays out a methodology of confidence distributions and puts them through
their paces. Among other merits they lead to optimal combinations of confidence from …
their paces. Among other merits they lead to optimal combinations of confidence from …
The focused information criterion
A variety of model selection criteria have been developed, of general and specific types.
Most of these aim at selecting a single model with good overall properties, for example …
Most of these aim at selecting a single model with good overall properties, for example …
[HTML][HTML] Robust parameter estimation with a small bias against heavy contamination
In this paper we consider robust parameter estimation based on a certain cross entropy and
divergence. The robust estimate is defined as the minimizer of the empirically estimated …
divergence. The robust estimate is defined as the minimizer of the empirically estimated …
[HTML][HTML] Generalized alpha-beta divergences and their application to robust nonnegative matrix factorization
We propose a class of multiplicative algorithms for Nonnegative Matrix Factorization (NMF)
which are robust with respect to noise and outliers. To achieve this, we formulate a new …
which are robust with respect to noise and outliers. To achieve this, we formulate a new …
Choosing a robustness tuning parameter
A novel method is proposed for choosing the tuning parameter associated with a family of
robust estimators. It consists of minimising estimated mean squared error, an approach that …
robust estimators. It consists of minimising estimated mean squared error, an approach that …