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Machine Learning and Knowledge Discovery in Databases
The three volume proceedings LNAI 11906 – 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019. The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy...
Handbook of Survival Analysis
Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians
Handbook of Bayesian, Fiducial, and Frequentist Inference
The emergence of data science, in recent decades, has magnified the need for efficient methodology for analyzing data and highlighted the importance of statistical inference. Despite the tremendous progress that has been made, statistical science is still a young discipline and continues to have several different and competing paths in its approaches and its foundations. While the emergence of competing approaches is a natural progression of any scientific discipline, differences in the foundations of statistical inference can sometimes lead to different interpretations and conclusions from the same dataset. The increased interest in the foundations of statistical inference has led to many p...
ANOVA and Mixed Models
ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs. Based on knowledge from an introductory course on probability and statistics, the theoretical foundations of the most important models are introduced. The focus is on an intuitive understanding of the theory, common pitfalls in practice, and the application of the methods in R. From data visualization and model fitting, up to the interpretation of the corresponding output, the whole workflow is presented using R. The book does not only cover standard ANOVA models, but also models for more advanced designs...
Handbook of Matching and Weighting Adjustments for Causal Inference
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
Cause Effect Pairs in Machine Learning
This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the st...
Handbook of Generalized Pairwise Comparisons
In today's healthcare landscape, there is a pressing need for quantitative methodologies that include the patients' perspective in any treatment decision. Handbook of Generalized Pairwise Comparisons: Methods for Patient-Centric Analysis provides a comprehensive overview of an innovative and powerful statistical methodology that generalizes the traditional Wilcoxon-Mann-Whitney test by extending it to any number of outcomes of any type and including thresholds of clinical relevance into a single, multidimensional evaluation. The book covers the statistical foundations of generalized pairwise comparisons (GPC), applications in various disease areas, implications for regulatory approvals and benefit-risk analyses, and considerations for patient-centricity in clinical research. With contributions from leading experts in the field, this book stands as an essential resource for a more holistic and patient-centric assessment of treatment effects.
Handbook of Bayesian Variable Selection
Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed phenomenon. Variable selection is especially important when the number of potential predictors is substantially larger than the sample size and sparsity can reasonably be assumed. The Handbook of Bayesian Variable Selection provides a comprehensive review of theoretical, methodological and computational aspects of Bayesian methods for variable selection. The topics cov...
A Companion to Experimental Philosophy
This is a comprehensive collection of essays that explores cutting-edge work in experimental philosophy, a radical new movement that applies quantitative and empirical methods to traditional topics of philosophical inquiry. Situates the discipline within Western philosophy and then surveys the work of experimental philosophers by sub-discipline Contains insights for a diverse range of fields, including linguistics, cognitive science, anthropology, economics, and psychology, as well as almost every area of professional philosophy today Edited by two rising scholars who take a broad and inclusive approach to the field Offers a complete introduction for non-specialists and students to the central approaches, findings, challenges, and controversies in experimental philosophy
Experimental Algorithms
This book constitutes the refereed proceedings of the 8th International Symposium on Experimental and Efficient Algorithms, SEA 2009, held in Dortmund, Germany, in June 2009. The 23 revised full papers were carefully reviewed and selected from 64 submissions and present current research on experimental evaluation and engineering of algorithms, as well as in various aspects of computational optimization and its applications. Contributions are supported by experimental evaluation, methodological issues in the design and interpretation of experiments, the use of (meta-) heuristics, or application-driven case studies that deepen the understanding of a problem's complexity.
