Topics in modelling of clustered data

Topics in Modelling of Clustered Data

📂 Library 📅 2002 🏛 Chapman & Hall/CRC 🌐 English

Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encoun

Topics in Modelling of Clustered Data

📁 Topics in Modelling of Clustered Data

✍ Aerts M., Molenberghs G., Ryan L.M. 📂 Library 📅 2002 🌐 English

Many methods for analyzing clustered data exists, all with advantages and limitation in particular applications. Compiled from the contributions of some of the world's leading researchers, this essential reference describes the main tools and techniques for modelling clustered data medical, biologic

Emerging Topics in Modeling Interval-Cen

📁 Emerging Topics in Modeling Interval-Censored Survival Data

✍ Jianguo Sun, Ding-Geng Chen 📂 Library 📅 2022 🏛 Springer 🌐 English

<p><span>This book primarily aims to discuss emerging topics in statistical methods and to booster research, education, and training to advance statistical modeling on interval-censored survival data. Commonly collected from public health and biomedical research, among other sources, interval-censor

Relational Data Clustering: Models, Algo

📁 Relational Data Clustering: Models, Algorithms, and Applications

✍ Bo Long, Zhongfei Zhang, Philip S. Yu 📂 Library 📅 2010 🏛 Chapman & Hall 🌐 English

A culmination of the authors’ years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Charles Bouveyron; Gilles Celeux; T. Brendan Murphy; Adrian E. Raftery 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observat

Model-Based Clustering and Classificatio

📁 Model-Based Clustering and Classification for Data Science: With Applications in R

✍ Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery 📂 Library 📅 2019 🏛 Cambridge University Press 🌐 English

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observat