Statistics in Industry and Government

Erscheinungsjahr 2025
ISBN: 978-0-443-31422-3
Verlag: Elsevier Inc

Buch, Englisch, 410 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 717 g

Statistics in Industry and Government covers industrial quality control and high-class quality maintenance in products. The book aims to cover as many applications that use statistics as an underlying tool in bringing the best quality products and industrial designs. Chapters in this new release include Analysis of Official Time Series with Ecce Signum, an R Package for Multivariate Signal Extraction and Forecasting, The Maturity Structure of Public Debt: A Granular Approach Using Indian Data, Harnessing the power of spherical intersection: A less arbitrary unsupervised learning method applied to pattern recognition within financial data, and much more.

Other chapters in this release include The Use of Causal Inference with Structural Models in Industry, MSME Statistics in India, The Importance of Accurate, Timely, Credible Crime Data to Inform Crime and Justice Policy, Combining Information from Multiple Sources in Official Statistics, Active Learning of Computer Experiment with both Quantitative and Qualitative Inputs, On the use of machine learning methods for missing data problems, Optimal Experimental Planning for Experiments Based on Coherent Systems with Industrial Applications, and more.

Autoren/Hrsg.

Weitere Mitwirkende

Khattree, Ravindra

So, Hon Yiu

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

1. Analysis of Official Time Series with Ecce Signum, an R Package for Multivariate Signal Extraction and Forecasting
Tucker Sprague McElroy and James Livsey
2. The Maturity Structure of Public Debt: A Granular Approach Using Indian Data
Chetan Ghate, Piyali Das and Subhadeep Halder
3. Harnessing the power of spherical intersection: A less arbitrary unsupervised learning method applied to pattern recognition within financial data
Michel Ferreira Cardia Haddad
4. The Use of Causal Inference with Structural Models in Industry
Takashi Isozaki
5. MSME Statistics in India
Poonam Munjal, Palash Baruah and sanjib pohit
6. The Importance of Accurate, Timely, Credible Crime Data to Inform Crime and Justice Policy
Alex R. R. Piquero
7. Combining Information from Multiple Sources in Official Statistics
Changbao Wu
8. Active Learning of Computer Experiment with both Quantitative and Qualitative Inputs
Chunfang Devon Lin, Xinwei Deng and Anita Shahrokhian
9. On the use of machine learning methods for missing data problems
Sixia Chen
10. Optimal Experimental Planning for Experiments Based on Coherent Systems with Industrial Applications
Hon Keung Tony Ng, Erhard Cramer and Yang Yu
11. An overview of models for one-shot device testing data analysis.
Man Ho Ling
12. TBA
Qing Yin
13. TBA
Ram C. Tiwari, JIXIAN WANG and Hongtao Zhang
14. Statistical Innovation: Transforming Pharmaceutical Research and Development
Pandurang M. Kulkarni, Wei Shen, Demissie Alemayehu and Yongming Qu

Über Autor(innen)

Khattree, Ravindra
Ravindra Khattree is a distinguished professor of statistics at Oakland University, U.S.A. and a co-director of the Center for Data Science and Big Data Analytics at the same university. In 1985, he earned a doctorate from the University of Pittsburgh with Calyampudi Radhakrishna Rao as his advisor. His contribution to the Fountain–Khattree–Peddada Theorem in Pitman measure of closeness is one of the important results of his work. Khattree is the coauthor of two books and has coedited two volumes. He has served as an associate editor of the Communications in Statistics journal. He was Chief editor of Journal of Statistics and Applications for more than ten years. He is an elected fellow of the American Statistical Association. In 2002, Khattree received the Young Researcher Award from the International Indian Statistical Association. Khattree was honored with a fellowship in the American Statistical Association in 2003 and became an elected member of the International Statistical Institute in 2004. He is also a recipient of Oakland University Research Excellence Award (2008).

So, Hon Yiu
Dr. Hon Yiu So is an Assistant Professor in the Department of Mathematics and Statistics at Oakland University. He received his Ph.D. in Mathematics with a concentration in Statistics from McMaster University in 2016. Dr. So's research lies at the intersection of reliability analysis, survey sampling, machine learning, and missing data methodologies. He has authored or co-authored over 25 peer-reviewed journal articles and book chapters in these fields. He is the co-author of the monograph Accelerated Life Testing of One-Shot Devices: Data Collection and Analysis. His work has contributed to both theoretical advancement and practical application in statistical science, particularly in the context of reliability engineering and public health research.