Distribution-Dependent Distance of First Two Moments

X. Rong Li

Distribution-Dependent Distance of First Two Moments

X. Rong Li

University of New Orleans

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Closeness measures between distributions, between vectors, and between matrices abound. Many practical problems, however, call for measures of closeness between the first two moments of two unspecified distributions given only a sample of one of them or of a third distribution without other information. We present several metrics for such problems that demand special, distribution-dependent solutions, and show their good qualities. We demonstrate their rich applications in various areas, such as estimation performance analysis, efficiency of distributed fusion, metrized Kullback-Leibler divergence, decision performance evaluation, credibility of estimators, filter initialization, and empirical distribution function problems.

Original language	English
Title of host publication	FUSION 2019 - 22nd International Conference on Information Fusion
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9780996452786
State	Published - Jul 2019
Externally published	Yes
Event	22nd International Conference on Information Fusion, FUSION 2019 - Ottawa, Canada Duration: 2 Jul 2019 → 5 Jul 2019

Publication series

Name	FUSION 2019 - 22nd International Conference on Information Fusion

Conference

Conference	22nd International Conference on Information Fusion, FUSION 2019
Country/Territory	Canada
City	Ottawa
Period	2/07/19 → 5/07/19

Keywords

covariance
distance
distribution
estimation
mean
measure
metric

Cite this

@inproceedings{ae106ff487d140c29f1a54f4b1ef847f,

title = "Distribution-Dependent Distance of First Two Moments",

abstract = "Closeness measures between distributions, between vectors, and between matrices abound. Many practical problems, however, call for measures of closeness between the first two moments of two unspecified distributions given only a sample of one of them or of a third distribution without other information. We present several metrics for such problems that demand special, distribution-dependent solutions, and show their good qualities. We demonstrate their rich applications in various areas, such as estimation performance analysis, efficiency of distributed fusion, metrized Kullback-Leibler divergence, decision performance evaluation, credibility of estimators, filter initialization, and empirical distribution function problems.",

keywords = "covariance, distance, distribution, estimation, mean, measure, metric",

author = "\{Rong Li\}, X.",

note = "Publisher Copyright: {\textcopyright} 2019 ISIF-International Society of Information Fusion.; 22nd International Conference on Information Fusion, FUSION 2019 ; Conference date: 02-07-2019 Through 05-07-2019",

year = "2019",

month = jul,

language = "英语",

series = "FUSION 2019 - 22nd International Conference on Information Fusion",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "FUSION 2019 - 22nd International Conference on Information Fusion",

}

Distribution-Dependent Distance of First Two Moments. / Rong Li, X.
FUSION 2019 - 22nd International Conference on Information Fusion. Institute of Electrical and Electronics Engineers Inc., 2019. 9011168 (FUSION 2019 - 22nd International Conference on Information Fusion).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Distribution-Dependent Distance of First Two Moments

AU - Rong Li, X.

PY - 2019/7

Y1 - 2019/7

N2 - Closeness measures between distributions, between vectors, and between matrices abound. Many practical problems, however, call for measures of closeness between the first two moments of two unspecified distributions given only a sample of one of them or of a third distribution without other information. We present several metrics for such problems that demand special, distribution-dependent solutions, and show their good qualities. We demonstrate their rich applications in various areas, such as estimation performance analysis, efficiency of distributed fusion, metrized Kullback-Leibler divergence, decision performance evaluation, credibility of estimators, filter initialization, and empirical distribution function problems.

AB - Closeness measures between distributions, between vectors, and between matrices abound. Many practical problems, however, call for measures of closeness between the first two moments of two unspecified distributions given only a sample of one of them or of a third distribution without other information. We present several metrics for such problems that demand special, distribution-dependent solutions, and show their good qualities. We demonstrate their rich applications in various areas, such as estimation performance analysis, efficiency of distributed fusion, metrized Kullback-Leibler divergence, decision performance evaluation, credibility of estimators, filter initialization, and empirical distribution function problems.

KW - covariance

KW - distance

KW - distribution

KW - estimation

KW - mean

KW - measure

KW - metric

UR - https://www.scopus.com/pages/publications/85081789346

M3 - 会议稿件

AN - SCOPUS:85081789346

T3 - FUSION 2019 - 22nd International Conference on Information Fusion

BT - FUSION 2019 - 22nd International Conference on Information Fusion

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd International Conference on Information Fusion, FUSION 2019

Y2 - 2 July 2019 through 5 July 2019

ER -

Distribution-Dependent Distance of First Two Moments

Abstract

Publication series

Conference

Keywords

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