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Biostatistics with R
Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis. This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.
Nonparametric Bayesian Inference in Biostatistics
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.
Deep Learning in Science
Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.
Algorithmic Advances in Riemannian Geometry and Applications
This book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compil...
The Sociology of Education
The Sociology of Education: A Systematic Analysis is a comprehensive and cross-cultural look at the sociology of education. This textbook gives a sociological analysis of education by incorporating a diverse set of theoretical approaches. The authors include practical applications and current educational issues to discuss the structure and processes that make education systems work as well as the role sociologists play in both understanding and bring about change. In addition to up-to-date examples and research, the eighth edition presents three chapters on inequality in educational access and experiences, where class, race and ethnicity, and gender are presented as separate (though intersecting) vectors of educational inequality. Each chapter combines qualitative and quantitative approaches and relevant theory; classics and emerging research; and micro- and macro-level perspectives.
Computational Methods for Risk Management in Economics and Finance
At present, computational methods have received considerable attention in economics and finance as an alternative to conventional analytical and numerical paradigms. This Special Issue brings together both theoretical and application-oriented contributions, with a focus on the use of computational techniques in finance and economics. Examined topics span on issues at the center of the literature debate, with an eye not only on technical and theoretical aspects but also very practical cases.
The Sociology of Education
The ninth edition of The Sociology of Education examines the field in rare breadth by incorporating a diverse range of theoretical approaches and a distinct sociological lens in its overview of education and schooling. Education is changing rapidly, just as the social forces outside of schools are, and to present the material in a meaningful way, the authors of this book provide a unifying framework—an open systems approach—to illustrate how the issues and structures we find in education are all interconnected. Separate chapters are devoted to how schools help shape who has access to educational opportunities and who does not; issues of race, class and gender; the organization of schools...
AMSTAT News
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Handbook of Discrete-Valued Time Series
Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed ca
