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Advanced Statistical Methods In Process Monitoring Finance And Environmental Science 2024th Edition Sven Knoth

  • SKU: BELL-63693306
Advanced Statistical Methods In Process Monitoring Finance And Environmental Science 2024th Edition Sven Knoth
$ 31.00 $ 45.00 (-31%)

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Advanced Statistical Methods In Process Monitoring Finance And Environmental Science 2024th Edition Sven Knoth instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 29.26 MB
Pages: 503
Author: Sven Knoth, Yarema Okhrin, Philipp Otto
ISBN: 9783031691102, 3031691105
Language: English
Year: 2024
Edition: 2024

Product desciption

Advanced Statistical Methods In Process Monitoring Finance And Environmental Science 2024th Edition Sven Knoth by Sven Knoth, Yarema Okhrin, Philipp Otto 9783031691102, 3031691105 instant download after payment.

This book presents a unique collection of contributions on modern methods and applications in three key areas of statistics, celebrating the significant work of Wolfgang Schmid in this field. It is structured thematically into parts focusing on statistical process monitoring, financial statistics, and spatial statistics with environmetrics, each featuring chapters from leading experts.

The opening articles on statistical process monitoring present novel methodologies for the detection of anomalies and control charting techniques, which are crucial for maintaining quality in manufacturing processes. Detailed discussions are included on integrating multivariate statistical methods and real-time monitoring to enhance process reliability and efficiency.

The part on financial statistics explores rigorous approaches in financial econometrics, with an emphasis on dynamic modelling of market volatility and risk assessment. Contributions cover advanced asset allocation strategies, leveraging high-dimensional data analysis, and the application of machine learning techniques.

Spatial statistics and environmetrics are addressed through innovative research on the statistical analysis of environmental data. This includes the use of geostatistical models and hybrid models that combine traditional statistical techniques with machine learning to improve the prediction of environmental phenomena. Key topics here involve the modelling of extremes and airborne pollutants, the prediction of earthquakes using a smartphone-based sensor network, and reviews of selected topics essential in modern spatial statistics.

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