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.
1. 1 Introduction - p. 51 2. 2 Lifetime distributions for reliability studies - p. 53 3. 3 Devices with multiple failure modes - p. 60 4. 4 Proportional hazards model - p. 68 5. 5 Step-stress accelerated life tests - p. 69 6. 6 One-shot devices with manufacturing defects - p. 72 7. 7 Concluding remarks - p. 74 8. References - p. 75 9. Chapter 3: Optimal experimental planning for experiments based on coherent systems with industrial applications - p. 81 10. Abstract - p. 81 11. 1 Introduction - p. 82 12. 2 System structure, coherent system and system signature - p. 85 13. 3 Progressively type-II censored experiments with coherent systems - p. 88 14. 4 Maximum likelihood estimation - p. 90 15. 5 Experimental planning and optimality criteria - p. 92 16. 6 Specific coherent systems being considered - p. 95 17. 7 Numerical Illustrations - p. 103 18. 7.3 Discussions - p. 113 19. 8 Practical example - p. 122 20. 9 Concluding remarks and future research directions - p. 125 21. References - p. 126 22. Chapter 4: Active learning of computer experiment with both quantitative and qualitative inputs - p. 129 23. Abstract - p. 129 24. 1 Introduction - p. 130 25. 2 Background - p. 132 26. 3 Active designs for computer experiments with quantitative and qualitative inputs - p. 138 27. 4 Numerical studies - p. 144 28. 5 Concluding remarks - p. 157 29. References - p. 157 30. Chapter 5: On the use of machine learning methods for missing data problems - p. 161 31. Chapter 6: Robust Bayesian approaches for leveraging external controls in drug development: Recent advances, statistical and regulatory challenges - p. 175 32. Chapter 7: Statistical innovation: Transforming pharmaceutical research and development - p. 207 33. Chapter 8: Analysis of official time series with Ecce Signum, an R package for multivariate signal extraction and forecasting - p. 243 34. Chapter 9: Combining information from multiple sources in official statistics - p. 295 35. Abstract - p. 295 36. 1 Introduction - p. 296 37. 2 Two probability survey samples - p. 297 38. 3 Dual-frame and multiple-frame samples - p. 300 39. 4 Use of auxiliary population information - p. 304 40. 5 Non-probability survey samples - p. 309 41. 6 Mitigating under-coverage biases - p. 313 42. 7 Additional remarks - p. 316 43. Acknowledgements - p. 316 44. References - p. 316 45. Chapter 10: The maturity structure of public debt: A granular approach using Indian data - p. 319 46. Chapter 11: The importance of accurate, timely, credible crime data to inform crime and justice policy - p. 353 47. Index - p. 381
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.
1. 1 Introduction - p. 51 2. 2 Lifetime distributions for reliability studies - p. 53 3. 3 Devices with multiple failure modes - p. 60 4. 4 Proportional hazards model - p. 68 5. 5 Step-stress accelerated life tests - p. 69 6. 6 One-shot devices with manufacturing defects - p. 72 7. 7 Concluding remarks - p. 74 8. References - p. 75 9. Chapter 3: Optimal experimental planning for experiments based on coherent systems with industrial applications - p. 81 10. Abstract - p. 81 11. 1 Introduction - p. 82 12. 2 System structure, coherent system and system signature - p. 85 13. 3 Progressively type-II censored experiments with coherent systems - p. 88 14. 4 Maximum likelihood estimation - p. 90 15. 5 Experimental planning and optimality criteria - p. 92 16. 6 Specific coherent systems being considered - p. 95 17. 7 Numerical Illustrations - p. 103 18. 7.3 Discussions - p. 113 19. 8 Practical example - p. 122 20. 9 Concluding remarks and future research directions - p. 125 21. References - p. 126 22. Chapter 4: Active learning of computer experiment with both quantitative and qualitative inputs - p. 129 23. Abstract - p. 129 24. 1 Introduction - p. 130 25. 2 Background - p. 132 26. 3 Active designs for computer experiments with quantitative and qualitative inputs - p. 138 27. 4 Numerical studies - p. 144 28. 5 Concluding remarks - p. 157 29. References - p. 157 30. Chapter 5: On the use of machine learning methods for missing data problems - p. 161 31. Chapter 6: Robust Bayesian approaches for leveraging external controls in drug development: Recent advances, statistical and regulatory challenges - p. 175 32. Chapter 7: Statistical innovation: Transforming pharmaceutical research and development - p. 207 33. Chapter 8: Analysis of official time series with Ecce Signum, an R package for multivariate signal extraction and forecasting - p. 243 34. Chapter 9: Combining information from multiple sources in official statistics - p. 295 35. Abstract - p. 295 36. 1 Introduction - p. 296 37. 2 Two probability survey samples - p. 297 38. 3 Dual-frame and multiple-frame samples - p. 300 39. 4 Use of auxiliary population information - p. 304 40. 5 Non-probability survey samples - p. 309 41. 6 Mitigating under-coverage biases - p. 313 42. 7 Additional remarks - p. 316 43. Acknowledgements - p. 316 44. References - p. 316 45. Chapter 10: The maturity structure of public debt: A granular approach using Indian data - p. 319 46. Chapter 11: The importance of accurate, timely, credible crime data to inform crime and justice policy - p. 353 47. Index - p. 381
