Statistical Inference From High Dimensional Data(1st Edition)

Authors:

Carlos Fernandez Lozano

Type:Hardcover/ PaperBack / Loose Leaf
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Book details

ISBN: 3036509445, 978-3036509440

Book publisher: Mdpi AG

Book Price $0 : - Real-world Problems Can Be High-dimensional, Complex, And Noisy - More Data Does Not Imply More Information - Different Approaches Deal With The So-called Curse Of Dimensionality To Reduce Irrelevant Information - A Process With Multidimensional Information Is Not Necessarily Easy To Interpret Nor Process - In Some Real-world Applications, The Number Of Elements Of A Class Is Clearly Lower Than The Other. The Models Tend To Assume That The Importance Of The Analysis Belongs To The Majority Class And This Is Not Usually The Truth - The Analysis Of Complex Diseases Such As Cancer Are Focused On More-than-one Dimensional Omic Data - The Increasing Amount Of Data Thanks To The Reduction Of Cost Of The High-throughput Experiments Opens Up A New Era For Integrative Data-driven Approaches - Entropy-based Approaches Are Of Interest To Reduce The Dimensionality Of High-dimensional Data