Statistical Data Fusion

Nonfiction, Science & Nature, Mathematics, Probability, Statistics
Cover of the book Statistical Data Fusion by Benjamin Kedem, Victor De Oliveira, Michael Sverchkov, World Scientific Publishing Company
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Author: Benjamin Kedem, Victor De Oliveira, Michael Sverchkov ISBN: 9789813200203
Publisher: World Scientific Publishing Company Publication: January 24, 2017
Imprint: WSPC Language: English
Author: Benjamin Kedem, Victor De Oliveira, Michael Sverchkov
ISBN: 9789813200203
Publisher: World Scientific Publishing Company
Publication: January 24, 2017
Imprint: WSPC
Language: English

See Press Release

This book comes up with estimates or decisions based on multiple data sources as opposed to more narrowly defined estimates or decisions based on single data sources. And as the world is awash with data obtained from numerous and varied processes, there is a need for appropriate statistical methods which in general produce improved inference by multiple data sources.

The book contains numerous examples useful to practitioners from genomics. Topics range from sensors (radars), to small area estimation of body mass, to the estimation of small tail probabilities, to predictive distributions in time series analysis.

Contents:

  • Introduction
  • Weighted Systems of Distributions
  • Multivariate Extension
  • Some Asymptotic Results
  • Out of Sample Fusion
  • Bayesian Weighted Systems
  • Small Area Estimation

Readership: Graduate students, researchers, practitioners of statistics, engineers, scientists.
Key Features:

  • A novel idea of "repeated out of sample fusion" applied in the estimation of very small tail probabilities using only moderately large samples. The idea is to fuse real and artificial data to obtain improved estimates of small tail probabilities
  • Improved kernel density estimates, univariate as well as multivariate, as compared with traditional kernel density estimates
  • A new Bayesian extension of the density ratio model
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

See Press Release

This book comes up with estimates or decisions based on multiple data sources as opposed to more narrowly defined estimates or decisions based on single data sources. And as the world is awash with data obtained from numerous and varied processes, there is a need for appropriate statistical methods which in general produce improved inference by multiple data sources.

The book contains numerous examples useful to practitioners from genomics. Topics range from sensors (radars), to small area estimation of body mass, to the estimation of small tail probabilities, to predictive distributions in time series analysis.

Contents:

Readership: Graduate students, researchers, practitioners of statistics, engineers, scientists.
Key Features:

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