General

Praise for the First Edition"The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data. In addition, it is clearly written in plain...

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality...

Introduction to Time Series Using Stata, Revised Edition provides a step-by-step guide to essential time-series techniques-from the incredibly simple to the quite complex- and, at the same time, demonstrates how these techniques can be applied in the...

An Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. As with previous editions, readers will learn to work effectively in Stata to perform data management,...

Implement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with...

Prepare to view the world through a new lens - SPEED - a groundbreaking exploration that challenges your perceptions of life's driving force, from the internationally bestselling author'There is perhaps no other academic who paints pictures with numbers...

Statistical Testing Strategies in the Health Sciences provides a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced...

This book provides an introduction to probabilistic methods in finance, based on stochastic models in discrete time. It is aimed primarily at graduate students in mathematics but may also benefit mathematicians in academia and the financial industry. ...

The jackknife and the bootstrap are nonparametric methods for assessing the errors in a statistical estimation problem. They provide several advantages over the traditional parametric approach: the methods are easy to describe and they apply to...

This updated classic text will aid readers in understanding much of the current literature on order statistics: a flourishing field of study that is essential for any practising statistician and a vital part of the training for students in statistics...

Statisticians know that the clean data sets that appear in textbook problems have little to do with real-life industry data. To better prepare their students for all types of statistical careers, academic statisticians now strive to use data sets from...

This book develops systematically and rigorously, yet in an expository and lively manner, the evolution of general random processes and their large time properties such as transience, recurrence, and convergence to steady states. The emphasis is on the...

Linear Stochastic Systems, originally published in 1988, is today as comprehensive a reference to the theory of linear discrete-time-parameter systems as ever. Its most outstanding feature is the unified presentation, including both input-output and...

The Sharpest Cut is written in honor of Manfred Padberg, who has made fundamental contributions to both the theoretical and computational sides of integer programming and combinatorial optimization. This outstanding collection presents recent results in...

Optimization is of critical importance in engineering. Engineers constantly strive for the best possible solutions, the most economical use of limited resources, and the greatest efficiency. As system complexity increases, these goals mandate the use of...

Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. It discusses neural networks in a statistical context, an...

The Memoirs of the AMS is devoted to the publication of new research in all areas of pure and applied mathematics. The Memoirs is designed particularly to publish long papers of groups of cognate papers in book form, and is under the supervision of the...

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This manual provides a practical guide on how graduate students can work in statistical and data science consulting and offers valuable resources for anyone interested in creating, expanding, or maintaining statistical or data science consulting programs...

Starting with the fundamentals of classical smooth optimization and building on established convex programming techniques, this research monograph presents a foundation and methodology for modern nonconvex nondifferentiable optimization. It provides...

Test-driven data analysis is the synthesis of ideas from test-driven development of software to data-intensive work including data science, data analysis, and data engineering. It is a methodology for improving the quality of data and of analytical...

Monty the Null Hippopotamus: Exploring Statistical Foundations Through Simulation offers a revolutionary approach to introductory statistics for students who grew up in the computing age. Breaking away from the traditional formula-memorization approach...

The Memoirs of the AMS is devoted to the publication of new research in all areas of pure and applied mathematics. The Memoirs is designed particularly to publish long papers of groups of cognate papers in book form, and is under the supervision of the...

This book provides an introduction to double generalized linear models (DGLMs) under frequentist and Bayesian frameworks. These models include the class of generalized linear models and compose a unified class of models, where appropriate functions of...

Phylogenetics is a topical and growing area of research. Phylogenies (phylogenetic trees and networks) allow biologists to study and graph evolutionary relationships between different species. These are also used to investigate other evolutionary...

From the Ising model to large language models, Gibbs Measures in Machine Learning offers a complete journey through one of the most powerful concepts connecting statistical physics and modern AI.Starting with the mathematical foundations - measure...

ELEMENTARY SURVEY SAMPLING is an engaging and practical introduction to survey sampling. The book begins with real-world scenarios and uses easy-to-understand formulas, methodologies, and concepts to show how sample surveys are used in a wide variety of...

Theory, experiments, computation, and data are considered as the four pillars of science and engineering. Experimental Design for Data Science and Engineering describes efficient statistical methods for making the experiments cheaper and computations...

This thoroughly revised and updated new edition reflects the progress that has been made in dose-exposure-response (DER) modeling. As the title suggests, the new edition covers more topics on dose and dose adjustment. A large part of the book has been...

Hidden Semi-Markov Models (HSMMs) have been extensively used for diverse applications where the objective is to analyze time series whose dynamics can be explained by a hidden process. A Comprehensive Guide to HSMM offers an accessible introduction to...

The presented ideas create and shape the vision of a "Manifesto of Higher Education Governance".Fractals are typically understood as self-similar structures, but this concept is expanded to include "self-different fractals". These fractals align with a...

This book offers a comprehensive exploration of popular continuous distribution families, including Exponentiated, Beta, Kumaraswamy, T-X, and Transmuted. It details methods for developing new distribution families, their properties, and inference...

Predicting the future is a difficult task but, as with the weather, it is possible with good models. But how does one predict the far future before the near future is known? Time parallel time integration, also known as PinT (Parallel-in-Time) methods,...

"This little book is a brilliant introduction to an important boundary field between the theory of probability and differential equations." -E. B. Dynkin, Mathematical ReviewsThis well-written book has been used for many years to learn about stochastic...

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. It focuses on agreement among the various players in...

Statistics in Environmental Monitoring and Assessment details how statistics plays an essential role in every step of an environmental monitoring and assessment program, from the sampling design to the data management and data analysis methods that are...

Applied Statistics with Python, Volume II focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support...

The primary purpose of this book is to introduce the reader to elementary statistical methods as related to tests of differences, such as one-sample tests, tests of two independent samples, tests of two matched samples, and one-way analysis of variance...

Implementing R for Statistics provides comprehensive coverage of basic statistical concepts using this important open-source programming language tool, from installing R and RStudio, to exploring its basic structure and uses, to extending some core...

Mathematical Foundations of Artificial Intelligence: Basics of Manifold Theory is the first volume in a two-part series. Together, they establish a unifying mathematical framework based on smooth manifold theory and Riemannian geometry essential tools...