Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences.
A thorough and comprehensive guide to the theoretical, practical, and methodological approaches used in survey experiments across disciplines such as political science, health sciences, sociology, economics, psychology, and marketing This book explores and explains the broad range of experimental designs embedded in surveys that use both probability and non-probability samples.
This volume presents a unified mathematical framework for the transmission channels for damaging shocks that can lead to instability in financial systems.
This book not only presents essential material to understand fuzzy metric fixed point theory, but also enables the readers to appreciate the recent advancements made in this direction.
Economic evaluation has become an essential component of clinical trial design to show that new treatments and technologies offer value to payers in various healthcare systems.
Providing a self-contained resource for upper undergraduate courses in combinatorics, this text emphasizes computation, problem solving, and proof technique.
Written by Nick Bingham, Chairman and Professor of Statistics at Birkbeck College, and Rudiger Kiesel, an "e;up-and-coming"e; academic, Risk Neutrality will benefit the Springer Finance Series in many ways.
Contrary to common intuition that all digits should occur randomly with equal chances in real data, empirical examinations consistently show that not all digits are created equal, but rather that low digits such as {1, 2, 3} occur much more frequently than high digits such as {7, 8, 9} in almost all data types, such as those relating to geology, chemistry, astronomy, physics, and engineering, as well as in accounting, financial, econometrics, and demographics data sets.
This book provides a rigorous introduction to the principles of econometrics and gives students and practitioners the tools they need to effectively and accurately analyze real data.
Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others).
Structural Equation Modeling provides a conceptual and mathematical understanding of structural equation modelling, helping readers across disciplines understand how to test or validate theoretical models, and build relationships between observed variables.
This textbook offers a comprehensive analysis of medical decision-making under uncertainty by combining test information theory with expected utility theory.
Now in its sixth edition, this textbook presents the tools and concepts used in multivariate data analysis in a style accessible for non-mathematicians and practitioners.
This book is for people who work in the tech industry-computer and data scientists, software developers and engineers, designers, and people in business, marketing or management roles.
This volume originates from the INDAM Symposium on Trends on Applications of Mathematics to Mechanics (STAMM), which was held at the INDAM headquarters in Rome on 5-9 September 2016.
Statistical Estimation of Epidemiological Risk provides coverage of the most important epidemiological indices, and includes recent developments in the field.
The goal of this Lecture Note is to prove a new type of limit theorems for normalized sums of strongly dependent random variables that play an important role in probability theory or in statistical physics.
Developed by Jean-Paul Benzerci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe.
Whilst the greatest effort has been made to ensure the quality of this text, due to the historical nature of this content, in some rare cases there may be minor issues with legibility.
Discrete probability theory and the theory of algorithms have become close partners over the last ten years, though the roots of this partnership go back much longer.
The vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs.
This IMA Volume in Mathematics and its Applications DIRECTIONS IN ROBUST STATISTICS AND DIAGNOSTICS is based on the proceedings of the first four weeks of the six week IMA 1989 summer program "e;Robustness, Diagnostics, Computing and Graphics in Statistics"e;.
Praise for the first edition: Principles of Uncertainty is a profound and mesmerising book on the foundations and principles of subjectivist or behaviouristic Bayesian analysis.
It was none other than Henri Poincare who at the turn of the last century, recognised that initial-value sensitivity is a fundamental source of random- ness.
Quickly and Easily Write Dynamic DocumentsSuitable for both beginners and advanced users, Dynamic Documents with R and knitr, Second Edition makes writing statistical reports easier by integrating computing directly with reporting.
This book covers topics of Informational Geometry, a field which deals with the differential geometric study of the manifold probability density functions.
