Ebook: Advances in Data Analysis: Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks
- Genre: Mathematics // Mathematicsematical Statistics
- Tags: Statistical Theory and Methods, Probability Theory and Stochastic Processes, Statistics for Life Sciences Medicine Health Sciences, Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences, Applications of Mat
- Series: Statistics for Industry and Technology
- Year: 2010
- Publisher: Birkhäuser Basel
- Edition: 1
- Language: English
- pdf
An outgrowth of the 12th International Conference on Applied Stochastic Models and Data Analysis, this book is a collection of invited chapters presenting recent developments in the field of data analysis, with applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. Emphasized throughout the volume are new methods with the potential for solving real-world problems in various areas.
The book is divided into eight major sections:
* Data Mining and Text Mining
* Information Theory and Statistical Applications
* Asymptotic Behaviour of Stochastic Processes and Random Fields
* Bioinformatics and Markov Chains
* Life Table Data, Survival Analysis, and Risk in Household Insurance
* Neural Networks and Self-Organizing Maps
* Parametric and Nonparametric Statistics
* Statistical Theory and Methods
Advances in Data Analysis is a useful reference for graduate students, researchers, and practitioners in statistics, mathematics, engineering, economics, social science, bioengineering, and bioscience.
An outgrowth of the 12th International Conference on Applied Stochastic Models and Data Analysis, this book is a collection of invited chapters presenting recent developments in the field of data analysis, with applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. Emphasized throughout the volume are new methods with the potential for solving real-world problems in various areas.
The book is divided into eight major sections:
* Data Mining and Text Mining
* Information Theory and Statistical Applications
* Asymptotic Behaviour of Stochastic Processes and Random Fields
* Bioinformatics and Markov Chains
* Life Table Data, Survival Analysis, and Risk in Household Insurance
* Neural Networks and Self-Organizing Maps
* Parametric and Nonparametric Statistics
* Statistical Theory and Methods
Advances in Data Analysis is a useful reference for graduate students, researchers, and practitioners in statistics, mathematics, engineering, economics, social science, bioengineering, and bioscience.