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Combinatorial Inference in Geometric Data Analysis
Paperback
Series: Chapman & Hall/CRC Computer Science & Data Analysis
CalculusGeneral MathematicsProbability & Statistics
ISBN13: 9781032093734
Publisher: CRC Press
Published: Jun 30 2021
Pages: 268
Weight: 0.84
Height: 0.57 Width: 6.14 Depth: 9.21
Language: English
Geometric Data Analysis designates the approach of Multivariate Statistics that conceptualizes the set of observations as a Euclidean cloud of points. Combinatorial Inference in Geometric Data Analysis gives an overview of multidimensional statistical inference methods applicable to clouds of points that make no assumption on the process of generating data or distributions, and that are not based on random modelling but on permutation procedures recasting in a combinatorial framework.
It focuses particularly on the comparison of a group of observations to a reference population (combinatorial test) or to a reference value of a location parameter (geometric test), and on problems of homogeneity, that is the comparison of several groups for two basic designs. These methods involve the use of combinatorial procedures to build a reference set in which we place the data. The chosen test statistics lead to original extensions, such as the geometric interpretation of the observed level, and the construction of a compatibility region.
Features:
- Defines precisely the object under study in the context of multidimensional procedures, that is clouds of points
- Presents combinatorial tests and related computations with R and Coheris SPAD software
- Includes four original case studies to illustrate application of the tests
- Includes necessary mathematical background to ensure it is self-contained
This book is suitable for researchers and students of multivariate statistics, as well as applied researchers of various scientific disciplines. It could be used for a specialized course taught at either master or PhD level.
1 different editions
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Combinatorial Inference in Geometric Data Analysis
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Bienaise, Solène
Combinatorial Inference in Geometric Data Analysis
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