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This book presents the latest findings on statistical inference in
multivariate, multilinear and mixed linear models, providing a
holistic presentation of the subject. It contains pioneering and
carefully selected review contributions by experts in the field and
guides the reader through topics related to estimation and testing
of multivariate and mixed linear model parameters. Starting with
the theory of multivariate distributions, covering identification
and testing of covariance structures and means under various
multivariate models, it goes on to discuss estimation in mixed
linear models and their transformations. The results presented
originate from the work of the research group Multivariate and
Mixed Linear Models and their meetings held at the Mathematical
Research and Conference Center in Bedlewo, Poland, over the last 10
years. Featuring an extensive bibliography of related publications,
the book is intended for PhD students and researchers in modern
statistical science who are interested in multivariate and mixed
linear models.
This volume features selected contributions on a variety of topics
related to linear statistical inference. The peer-reviewed papers
from the International Conference on Trends and Perspectives in
Linear Statistical Inference (LinStat 2016) held in Istanbul,
Turkey, 22-25 August 2016, cover topics in both theoretical and
applied statistics, such as linear models, high-dimensional
statistics, computational statistics, the design of experiments,
and multivariate analysis. The book is intended for statisticians,
Ph.D. students, and professionals who are interested in statistical
inference.
This volume features selected contributions on a variety of topics
related to linear statistical inference. The peer-reviewed papers
from the International Conference on Trends and Perspectives in
Linear Statistical Inference (LinStat 2016) held in Istanbul,
Turkey, 22-25 August 2016, cover topics in both theoretical and
applied statistics, such as linear models, high-dimensional
statistics, computational statistics, the design of experiments,
and multivariate analysis. The book is intended for statisticians,
Ph.D. students, and professionals who are interested in statistical
inference.
This book expands on the classical statistical multivariate
analysis theory by focusing on bilinear regression models, a class
of models comprising the classical growth curve model and its
extensions. In order to analyze the bilinear regression models in
an interpretable way, concepts from linear models are extended and
applied to tensor spaces. Further, the book considers
decompositions of tensor products into natural subspaces, and
addresses maximum likelihood estimation, residual analysis,
influential observation analysis and testing hypotheses, where
properties of estimators such as moments, asymptotic distributions
or approximations of distributions are also studied. Throughout the
text, examples and several analyzed data sets illustrate the
different approaches, and fresh insights into classical
multivariate analysis are provided. This monograph is of interest
to researchers and Ph.D. students in mathematical statistics,
signal processing and other fields where statistical multivariate
analysis is utilized. It can also be used as a text for second
graduate-level courses on multivariate analysis.
This book presents the latest findings on statistical inference in
multivariate, multilinear and mixed linear models, providing a
holistic presentation of the subject. It contains pioneering and
carefully selected review contributions by experts in the field and
guides the reader through topics related to estimation and testing
of multivariate and mixed linear model parameters. Starting with
the theory of multivariate distributions, covering identification
and testing of covariance structures and means under various
multivariate models, it goes on to discuss estimation in mixed
linear models and their transformations. The results presented
originate from the work of the research group Multivariate and
Mixed Linear Models and their meetings held at the Mathematical
Research and Conference Center in Bedlewo, Poland, over the last 10
years. Featuring an extensive bibliography of related publications,
the book is intended for PhD students and researchers in modern
statistical science who are interested in multivariate and mixed
linear models.
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