|
Showing 1 - 2 of
2 matches in All Departments
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.
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.
|
You may like...
Quanta
Gilberto Gil
CD
R146
Discovery Miles 1 460
H2O
Francisco Aguabella
CD
R226
Discovery Miles 2 260
Jazz Cuba 2
Valdes, Lopez, …
CD
R228
Discovery Miles 2 280
Sea
Jorge Drexler
CD
R331
Discovery Miles 3 310
Urubu
Antonio Carlos Jobim
CD
R467
Discovery Miles 4 670
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.