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María del Carmen Pardo

dblp:68/6482 · also M. C. Pardo 0001 · DBLP profile ↗
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7ranked-venue papers
6as first author
0since 2021 · last 2003
0000-0002-4623-6321ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 5 · 4 first-authorTheory of computation · 2 · 2 first-authorArtificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Information theory · 81% Computational geometry · 19%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › information measures › divergence measures
kullback-leibler divergence		 0.012003
On asymptotic properties of information-theoretic divergences · IEEE Trans. Inf. Theory 2003
Information theory › probability theory
discrete probability distribution		 0.011997
About distances of discrete distributions satisfying the data processing theorem of information theory · IEEE Trans. Inf. Theory 1997
Computational geometry
distance measures		 0.011997
About distances of discrete distributions satisfying the data processing theorem of information theory · IEEE Trans. Inf. Theory 1997
Information theory › hypothesis testing
goodness-of-fit testing		 0.012003
On asymptotic properties of information-theoretic divergences · IEEE Trans. Inf. Theory 2003
Information theory › information measures
data processing inequality		 0.011997
About distances of discrete distributions satisfying the data processing theorem of information theory · IEEE Trans. Inf. Theory 1997
YearPublicationVenuePosition
2003 On asymptotic properties of information-theoretic divergences
abstract
Mutual asymptotic equivalence is established within three classes of information-theoretic divergences of discrete probability distributions, namely, f-divergences of Csiszar, f-divergences of Bregman, and f-divergences of Burbea-Rao. These equivalences are used to find asymptotic distributions of the corresponding divergence statistics for testing the goodness of fit when the hypothetic distribution is uniform. All results are based on standard expansion techniques and on a new relation between the Bregman and Burbea-Rao divergences formulated in Lemma 2 of the paper.
María del Carmen Pardo, Igor Vajda
 IEEE Trans. Inf. Theory1
1999 Nested Models for Categorical Data
María del Carmen Pardo
 Inf. Sci.1
1998 Goodness-of-Fit Tests for Stationary Distributions of Markov Chains Based on Rao's Divergence
María del Carmen Pardo
 Inf. Sci.1
1997 About distances of discrete distributions satisfying the data processing theorem of information theory
abstract
The distances of discrete probability distributions are considered. Necessary and sufficient conditions for validity of the data processing theorem of information theory are established. These conditions are applied to the Burbea-Rao (1982) divergences and Bregman (1967) distances.
María del Carmen Pardo, Igor Vajda
 IEEE Trans. Inf. Theory1
1996 A Comparison of a New Family of Goodness-of-fit Statistics
María del Carmen Pardo, Julio Angel Pardo
 Inf. Sci.1
1994 Informational Energy Test for Equality of Variances
Julio Angel Pardo, María del Carmen Pardo, M. L. Vicente, María Dolores Esteban
 IPMU2
1994 On Testing Indenpendence in Multidimensional Contingency Tables with Stratified Random Sampling
María del Carmen Pardo
 Inf. Sci.1