1,159 research outputs found
New results on mixture and exponential models by Orlicz spaces
New results and improvements in the study of nonparametric exponential and
mixture models are proposed. In particular, different equivalent
characterizations of maximal exponential models, in terms of open exponential
arcs and Orlicz spaces, are given. Our theoretical results are supported by
several examples and counterexamples and provide an answer to some open
questions in the literature.Comment: Published at http://dx.doi.org/10.3150/15-BEJ698 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Cramer-Rao Lower Bound and Information Geometry
This article focuses on an important piece of work of the world renowned
Indian statistician, Calyampudi Radhakrishna Rao. In 1945, C. R. Rao (25 years
old then) published a pathbreaking paper, which had a profound impact on
subsequent statistical research.Comment: To appear in Connected at Infinity II: On the work of Indian
mathematicians (R. Bhatia and C.S. Rajan, Eds.), special volume of Texts and
Readings In Mathematics (TRIM), Hindustan Book Agency, 201
Revisiting Chernoff Information with Likelihood Ratio Exponential Families
The Chernoff information between two probability measures is a statistical
divergence measuring their deviation defined as their maximally skewed
Bhattacharyya distance. Although the Chernoff information was originally
introduced for bounding the Bayes error in statistical hypothesis testing, the
divergence found many other applications due to its empirical robustness
property found in applications ranging from information fusion to quantum
information. From the viewpoint of information theory, the Chernoff information
can also be interpreted as a minmax symmetrization of the Kullback--Leibler
divergence. In this paper, we first revisit the Chernoff information between
two densities of a measurable Lebesgue space by considering the exponential
families induced by their geometric mixtures: The so-called likelihood ratio
exponential families. Second, we show how to (i) solve exactly the Chernoff
information between any two univariate Gaussian distributions or get a
closed-form formula using symbolic computing, (ii) report a closed-form formula
of the Chernoff information of centered Gaussians with scaled covariance
matrices and (iii) use a fast numerical scheme to approximate the Chernoff
information between any two multivariate Gaussian distributions.Comment: 41 page
Recent advances in directional statistics
Mainstream statistical methodology is generally applicable to data observed
in Euclidean space. There are, however, numerous contexts of considerable
scientific interest in which the natural supports for the data under
consideration are Riemannian manifolds like the unit circle, torus, sphere and
their extensions. Typically, such data can be represented using one or more
directions, and directional statistics is the branch of statistics that deals
with their analysis. In this paper we provide a review of the many recent
developments in the field since the publication of Mardia and Jupp (1999),
still the most comprehensive text on directional statistics. Many of those
developments have been stimulated by interesting applications in fields as
diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics,
image analysis, text mining, environmetrics, and machine learning. We begin by
considering developments for the exploratory analysis of directional data
before progressing to distributional models, general approaches to inference,
hypothesis testing, regression, nonparametric curve estimation, methods for
dimension reduction, classification and clustering, and the modelling of time
series, spatial and spatio-temporal data. An overview of currently available
software for analysing directional data is also provided, and potential future
developments discussed.Comment: 61 page
Construction of networks by associating with submanifolds of almost Hermitian manifolds
The idea that data lies on a non-linear space has brought up the concept of
manifold learning as a part of machine learning
A numerical approximation method for the Fisher-Rao distance between multivariate normal distributions
We present a simple method to approximate Rao's distance between multivariate
normal distributions based on discretizing curves joining normal distributions
and approximating Rao's distances between successive nearby normal
distributions on the curves by the square root of Jeffreys divergence, the
symmetrized Kullback-Leibler divergence. We consider experimentally the linear
interpolation curves in the ordinary, natural and expectation parameterizations
of the normal distributions, and compare these curves with a curve derived from
the Calvo and Oller's isometric embedding of the Fisher-Rao -variate normal
manifold into the cone of symmetric positive-definite
matrices [Journal of multivariate analysis 35.2 (1990): 223-242]. We report on
our experiments and assess the quality of our approximation technique by
comparing the numerical approximations with both lower and upper bounds.
Finally, we present several information-geometric properties of the Calvo and
Oller's isometric embedding.Comment: 46 pages, 19 figures, 3 table
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