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Author: Ole E Barndorff-Nielsen
Publisher: Springer Science & Business Media
ISBN: 1461236827
Category : Mathematics
Languages : en
Pages : 154
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Book Description
Author: Ole E Barndorff-Nielsen
Publisher: Springer Science & Business Media
ISBN: 1461236827
Category : Mathematics
Languages : en
Pages : 154
Get Book Here
Book Description
Author: Ole E Barndorff-Nielsen
Publisher:
ISBN: 9781461236832
Category :
Languages : en
Pages : 160
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Book Description
Author: Robert A. Wijsman
Publisher: IMS
ISBN: 9780940600195
Category : Mathematics
Languages : en
Pages : 264
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Book Description
Author: Ole E. Barndorff-Nielsen
Publisher:
ISBN:
Category : Decomposition (Mathematics)
Languages : en
Pages : 66
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Book Description
Author: Piero Barone
Publisher: Springer Science & Business Media
ISBN: 1461229200
Category : Mathematics
Languages : en
Pages : 266
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Book Description
This volume comprises a collection of papers by world- renowned experts on image analysis. The papers range from survey articles to research papers, and from theoretical topics such as simulated annealing through to applied image reconstruction. It covers applications as diverse as biomedicine, astronomy, and geophysics. As a result, any researcher working on image analysis will find this book provides an up-to-date overview of the field and in addition, the extensive bibliographies will make this a useful reference.
Author: Kelly H. Zou
Publisher: CRC Press
ISBN: 1439812233
Category : Mathematics
Languages : en
Pages : 243
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Book Description
Statistical evaluation of diagnostic performance in general and Receiver Operating Characteristic (ROC) analysis in particular are important for assessing the performance of medical tests and statistical classifiers, as well as for evaluating predictive models or algorithms. This book presents innovative approaches in ROC analysis, which are releva
Author: Valerii V. Fedorov
Publisher: Springer Science & Business Media
ISBN: 9780387982151
Category : Mathematics
Languages : en
Pages : 136
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Book Description
Here, the authors explain the basic ideas so as to generate interest in modern problems of experimental design. The topics discussed include designs for inference based on nonlinear models, designs for models with random parameters and stochastic processes, designs for model discrimination and incorrectly specified (contaminated) models, as well as examples of designs in functional spaces. Since the authors avoid technical details, the book assumes only a moderate background in calculus, matrix algebra, and statistics. However, at many places, hints are given as to how readers may enhance and adopt the basic ideas for advanced problems or applications. This allows the book to be used for courses at different levels, as well as serving as a useful reference for graduate students and researchers in statistics and engineering.
Author: David Pollard
Publisher: Cambridge University Press
ISBN: 9780521002899
Category : Mathematics
Languages : en
Pages : 372
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Book Description
This book grew from a one-semester course offered for many years to a mixed audience of graduate and undergraduate students who have not had the luxury of taking a course in measure theory. The core of the book covers the basic topics of independence, conditioning, martingales, convergence in distribution, and Fourier transforms. In addition there are numerous sections treating topics traditionally thought of as more advanced, such as coupling and the KMT strong approximation, option pricing via the equivalent martingale measure, and the isoperimetric inequality for Gaussian processes. The book is not just a presentation of mathematical theory, but is also a discussion of why that theory takes its current form. It will be a secure starting point for anyone who needs to invoke rigorous probabilistic arguments and understand what they mean.
Author: Rainer Schwabe
Publisher: Springer Science & Business Media
ISBN: 1461240387
Category : Mathematics
Languages : en
Pages : 132
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Book Description
In real applications most experimental situations are influenced by a large number of different factors. In these settings the design of an experiment leads to challenging optimization problems, even if the underlying relationship can be described by a linear model. Based on recent research, this book introduces the theory of optimum designs for complex models and develops general methods of reduction to marginal problems for large classes of models with relevant interaction structures.
Author: Maia Berkane
Publisher: Springer Science & Business Media
ISBN: 146121842X
Category : Mathematics
Languages : en
Pages : 285
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Book Description
This volume gathers refereed papers presented at the 1994 UCLA conference on "La tent Variable Modeling and Application to Causality. " The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data.
