Probability and statistics

The quantitative modeling of complex systems of interacting risks is a fairly recent development in the financial and insurance industries.

In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics.

This self-contained book presents the main techniques of quantitative portfolio management and associated statistical methods in a very didactic and structured way, in a minimum number of pages.

As a result, the inference of phylogenies often seems divorced from any connection to other methods of analysis of scienti?

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis.

Any method of fitting equations to data may be called regression.

After Karl Joreskog's first presentation in 1970, Structural Equation Modelling or SEM has become a main statistical tool in many fields of science.

Nanohertz Gravitational Wave Astronomy explores the exciting hunt for low frequency gravitational waves by using the extraordinary timing precision of pulsars.

This book brings together carefully selected, peer-reviewed works on mathematical biology presented at the BIOMAT International Symposium on Mathematical and Computational Biology, which was held at the Institute of Numerical Mathematics, Russian Academy of Sciences, in October 2017, in Moscow.

The aim of this monograph is quite modest: It attempts to be a systematic exposition of all that appeared in the literature and was known to us by the end of the 20th century about the Laplace distribution and its numerous generalizations and extensions.

Robust and nonparametric statistical methods have their foundation in fields ranging from agricultural science to astronomy, from biomedical sciences to the public health disciplines, and, more recently, in genomics, bioinformatics, and financial statistics.

This book gives a brief survey of the theory of multidimensional (multivariate), weakly stationary time series, with emphasis on dimension reduction and prediction.

Probability and Statistics for Data Science: Math + R + Data covers "e;math stat"e;-distributions, expected value, estimation etc.

Give Your Students the Proper Groundwork for Future Studies in OptimizationA First Course in Optimization is designed for a one-semester course in optimization taken by advanced undergraduate and beginning graduate students in the mathematical sciences and engineering.

Big Data Systems encompass massive challenges related to data diversity, storage mechanisms, and requirements of massive computational power.

The purpose of this monograph is to provide a theory of Markov processes that are invariant under the actions of Lie groups, focusing on ways to represent such processes in the spirit of the classical Levy-Khinchin representation.

Probability and Conditional Expectations bridges the gap between books on probability theory and statistics by providing the probabilistic concepts estimated and tested in analysis of variance, regression analysis, factor analysis, structural equation modeling, hierarchical linear models and analysis of qualitative data.

Approach your problems from the right end It isn't that they can't see the solution.

This book was written to provide guidance for those who need to apply statistical methods for practical use.

The modern theory of Sequential Analysis came into existence simultaneously in the United States and Great Britain in response to demands for more efficient sampling inspection procedures during World War II.

Potential theory and certain aspects of probability theory are intimately related, perhaps most obviously in that the transition function determining a Markov process can be used to define the Green function of a potential theory.

The dissemination of the MIXED procedure in SAS has provided a whole class of statistical models for routine use.

Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and ModelingSince the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data.

Biostatistics, Second Edition, is a user-friendly guide on biostatistics, which focuses on the proper use and interpretation of statistical methods.

Statistical agencies, research organizations, companies, and other data stewards that seek to share data with the public face a challenging dilemma.

This volume provides an overview of two of the most important examples of interacting particle systems, the contact process, and the voter model, as well as their many variants introduced in the past 50 years.

This book explores various state-of-the-art aspects behind the statistical analysis of neuroimaging data.

This 2004 book presents a fascinating collection of problems related to the Cauchy-Schwarz inequality and coaches readers through solutions.

This book is designed to provide a comprehensive introduction to R programming for data analysis, manipulation and presentation.

Statistical inference carries great significance in model building from both the theoretical and the applications points of view.

Take a look at the complete Table of Contents for Statistics and Probability with Applications.

This book is the first one devoted to high-dimensional (or large-scale) diffusion stochastic processes (DSPs) with nonlinear coefficients.

This research monograph on circular data analysis covers some recent advances in the field, besides providing a brief introduction to, and a review of, existing methods and models.

The three parts of this book on survey methodology combine an introduction to basic sampling theory, engaging presentation of topics that reflect current research trends, and informed discussion of the problems commonly encountered in survey practice.

This book provides the mathematical definition of white noise and gives its significance.

A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression Smoothing and Regression: Approaches, Computation, and Application bridges the many gaps that exist among competing univariate and multivariate smoothing techniques.

Big Data Analytics in Oncology with R serves the analytical approaches for big data analysis.

This book is a positive analysis of voting ''paradoxes'' and argues that negative ''impossibility'' results are not justified.

This book deals with the impact of uncertainty in input data on the outputs of mathematical models.

In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species.

This concise set of course-based notes provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP).

This books reviews and brings readers up to date with the latest research knowledge on road traffic safety.

This book depicts a wide range of situations in which there exist finite form representations for the Meijer G and the Fox H functions.

This edited volume presents current research in biostatistics with emphasis on biopharmaceutical applications.

Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies.

Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications.