Statistical Data Fusion

Statistical Data Fusion

Benjamin Kedem, Victor De Oliveira;Michael Sverchkov


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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.
Semiparametric, Density Ratio, Bayesian, Small Area, Tail Probability, Out of Sample Fusion0