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Handbook of Data Visualization
Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.
Statistical Learning and Data Science
Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data wor
Introduction to Probability Models
Introduction to Probability Models, Tenth Edition, provides an introduction to elementary probability theory and stochastic processes. There are two approaches to the study of probability theory. One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text. The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic p...
Encyclopedia of Measurement and Statistics
Publisher Description
Model-Free Prediction and Regression
The Model-Free Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality. Prediction has been traditionally approached via a model-based paradigm, i...
MACHINE LEARNING IN BIOINFORMATICS
Machine learning (ML) has revolutionized the field of bioinformatics, offering innovative tools and methodologies to tackle complex biological problems. In bioinformatics, data is often vast, diverse, and multidimensional, ranging from genomic sequences to protein structures, gene expressions, and clinical datasets. Machine learning techniques have proven essential in analyzing and extracting meaningful patterns from these enormous datasets. The use of ML in bioinformatics spans a broad spectrum of applications, from predicting protein structures and functions to identifying genetic variants associated with diseases. By leveraging supervised, unsupervised, and reinforcement learning algorith...
Control and Optimization Methods for Electric Smart Grids
Control and Optimization Methods for Electric Smart Grids brings together leading experts in power, control and communication systems, and consolidates some of the most promising recent research in smart grid modeling, control and optimization in hopes of laying the foundation for future advances in this critical field of study. The contents comprise eighteen essays addressing wide varieties of control-theoretic problems for tomorrow’s power grid. Topics covered include control architectures for power system networks with large-scale penetration of renewable energy and plug-in vehicles, optimal demand response, new modeling methods for electricity markets, cyber-security,data analysis and wide-area control using synchronized phasor measurements.
Aeschylus: Suppliants
Aeschylus' 'Suppliants' dramatises the myth of the fifty daughters of Danaos, who flee Egypt and come to Argos as suppliants, trying to escape forced marriage to their Egyptian cousins. It was long considered to be the earliest surviving tragedy. Even after the mid-20th century, when new evidence established a later date for the play, critics tended to condemn it for its alleged 'archaic' features. As a result it has long been underestimated, although a careful examination reveals it to be one of the most exciting tragedies. This companion employs a variety of critical approaches to set the play in its literary, dramatic, social and historical contexts, and also offers a thorough examination of the performance of the tragedy, investigating topics such as stage, action, music, song and dance.
Frontiers In Statistics
During the last two decades, many areas of statistical inference have experienced phenomenal growth. This book presents a timely analysis and overview of some of these new developments and a contemporary outlook on the various frontiers of statistics.Eminent leaders in the field have contributed 16 review articles and 6 research articles covering areas including semi-parametric models, data analytical nonparametric methods, statistical learning, network tomography, longitudinal data analysis, financial econometrics, time series, bootstrap and other re-sampling methodologies, statistical computing, generalized nonlinear regression and mixed effects models, martingale transform tests for model diagnostics, robust multivariate analysis, single index models and wavelets.This volume is dedicated to Prof. Peter J Bickel in honor of his 65th birthday. The first article of this volume summarizes some of Prof. Bickel's distinguished contributions.
Tree-Based Methods for Statistical Learning in R
Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus, both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level, which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example, user...
