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Meta-analysis of Binary Data Using Profile Likelihood
Providing reliable information on an intervention effect, meta-analysis is a powerful statistical tool for analyzing and combining results from individual studies. Meta-Analysis of Binary Data Using Profile Likelihood focuses on the analysis and modeling of a meta-analysis with individually pooled data (MAIPD). It presents a unifying approac
Introduction to Probability, Statistics and R
Zusammenfassung: A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis. This book serves as a gateway to learning statistical methods from scratch, assuming a solid background in high school mathematics. Readers gradually progress from basic concepts to advanced statistical modelling, with examples from actuarial, biological, ecological, engineering, environmental, medicine, and social sciences highlighting the real-world relevance of the subject. An accompanying R package enables seamless practice and immediate application, making it ideal for beginners. The book comprises 19 chap...
Statistical Modelling
This volume presents the published proceedings of the lOth International Workshop on Statistical Modelling, to be held in Innsbruck, Austria from 10 to 14 July, 1995. This workshop marks an important anniversary. The inaugural workshop in this series also took place in Innsbruck in 1986, and brought together a small but enthusiastic group of thirty European statisticians interested in statistical modelling. The workshop arose out of two G LIM conferences in the U. K. in London (1982) and Lancaster (1985), and from a num ber of short courses organised by Murray Aitkin and held at Lancaster in the early 1980s, which attracted many European statisticians interested in Generalised Linear Modelli...
Spatial Cluster Modelling
Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this b
Capture-Recapture Methods for the Social and Medical Sciences
Capture-recapture methods have been used in biology and ecology for more than 100 years. However, it is only recently that these methods have become popular in the social and medical sciences to estimate the size of elusive populations such as illegal immigrants, illicit drug users, or people with a drinking problem. Capture-Recapture Methods for the Social and Medical Sciences brings together important developments which allow the application of these methods. It has contributions from more than 40 researchers, and is divided into eight parts, including topics such as ratio regression models, capture-recapture meta-analysis, extensions of single and multiple source models, latent variable models and Bayesian approaches. The book is suitable for everyone who is interested in applying capture-recapture methods in the social and medical sciences. Furthermore, it is also of interest to those working with capture-recapture methods in biology and ecology, as there are some important developments covered in the book that also apply to these classical application areas.
Classification, Data Analysis, and Data Highways
This volume presents 43 articles dealing with models and methods of data analysis and classification, statistics and stochastics, information systems and WWW- and Internet-related topics as well as many applications. These articles are selected from more than 100 papers presented at the 21st Annual Conference of the Gesellschaft für Klassifikation. Based on the submitted and revised papers six sections have been arranged: - Classification and Data Analysis - Mathematical and Statistical Methods - World Wide Web and the Internet - Speech and Pattern Recognition - Marketing.
Distributed Machine Learning and Gradient Optimization
This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol. Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appeal to a broad audience in the field of machine learning, artificial intelligence, big data and database management.
Bibliographie Internationale de Sociologie 1980
Each "Bibliography" lists and annotates the most important works published during the year. They are arranged by topic and indexed by author, subject, and geographic location.
Semi-Infinite Programming and Applications
Semi-infinite programming is a natural extension of linear pro gramming that allows finitely many variables to appear in infinitely many constraints. As the papers in this collection will reconfirm, the theoretical and practical manifestations and applications of this prob lem formulation are abundant and significant. This volume presents 20 carefully selected papers that were pre sented at the International Symposium on Semi-Infinite Programming and Applications, The University of Texas at Austin, September 8-10, 1981. A total of 70 papers were presented by distinguished participants from 15 countries. This was only the second international meeting on this topic, the first taking place in B...
