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Conference Matrices for Optimizing and Applications [[electronic resource] ] : High-Precision Estimation Method with Small Number of Experiments / / by Teruo Mori
| Conference Matrices for Optimizing and Applications [[electronic resource] ] : High-Precision Estimation Method with Small Number of Experiments / / by Teruo Mori |
| Autore | Mori Teruo |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (XIX, 254 p. 146 illus., 82 illus. in color.) |
| Disciplina | 6,200,042 |
| Soggetto topico |
Engineering design
Experimental design Environment Engineering Design Design of Experiments Environmental Sciences |
| ISBN | 981-9968-39-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Theory and Analysis -- Conference matrices and families -- Regression Analysis -- Quality improvements -- Parameter design. |
| Record Nr. | UNINA-9910855381803321 |
Mori Teruo
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Introduction to Probability, Statistical Methods, Design of Experiments and Statistical Quality Control [[electronic resource] /] / by Dharmaraja Selvamuthu, Dipayan Das
| Introduction to Probability, Statistical Methods, Design of Experiments and Statistical Quality Control [[electronic resource] /] / by Dharmaraja Selvamuthu, Dipayan Das |
| Autore | Selvamuthu Dharmaraja |
| Edizione | [2nd ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (623 pages) |
| Disciplina | 519.5 |
| Altri autori (Persone) | DasDipayan |
| Collana | University Texts in the Mathematical Sciences |
| Soggetto topico |
Statistics
Probabilities Experimental design Statistical Theory and Methods Probability Theory Design of Experiments |
| ISBN | 981-9993-63-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. Basic Concepts of Probability -- Chapter 3. Random Variable and Distribution Function -- Chapter 4. Standard Deviation -- Chapter 5. Multiple Random Variable and Joint Distribution -- Chapter 6. Limiting Distributions -- Chapter 7. Descriptive Statistics -- Chapter 8. Sampling Distributions -- Chapter 9. Estimation -- Chapter 10. Testing of Hypothesis -- Chapter 11. Analysis of Correlation and Regression -- Chapter 12. Single Factor Experimental Design -- Chapter 13. Multi-Factor Experimental Designs -- Chapter 14. Response Surface Methodology -- Chapter 15. Acceptance Sampling -- Chapter 16. Control Charts. Appendix A. Statistical Tables -- Appendix B. Introduction to the R Software Program. . |
| Record Nr. | UNINA-9910847592103321 |
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Selvamuthu Dharmaraja
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mindful Topics on Risk Analysis and Design of Experiments [[electronic resource] ] : Selected contributions from ICRA8, Vienna 2019 / / edited by Jürgen Pilz, Teresa A. Oliveira, Karl Moder, Christos P. Kitsos
| Mindful Topics on Risk Analysis and Design of Experiments [[electronic resource] ] : Selected contributions from ICRA8, Vienna 2019 / / edited by Jürgen Pilz, Teresa A. Oliveira, Karl Moder, Christos P. Kitsos |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (166 pages) |
| Disciplina | 519.2 |
| Soggetto topico |
Statistics
Experimental design Financial risk management Statistical Theory and Methods Design of Experiments Risk Management Avaluació del risc Estadística matemàtica Disseny d'experiments |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-06685-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996479369303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Mindful Topics on Risk Analysis and Design of Experiments [[electronic resource] ] : Selected contributions from ICRA8, Vienna 2019 / / edited by Jürgen Pilz, Teresa A. Oliveira, Karl Moder, Christos P. Kitsos
| Mindful Topics on Risk Analysis and Design of Experiments [[electronic resource] ] : Selected contributions from ICRA8, Vienna 2019 / / edited by Jürgen Pilz, Teresa A. Oliveira, Karl Moder, Christos P. Kitsos |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (166 pages) |
| Disciplina | 519.2 |
| Soggetto topico |
Statistics
Experimental design Financial risk management Statistical Theory and Methods Design of Experiments Risk Management Avaluació del risc Estadística matemàtica Disseny d'experiments |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-06685-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910574043603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications [[electronic resource] ] : Selected Contributions from SimStat 2019 and Invited Papers / / edited by Jürgen Pilz, Viatcheslav B. Melas, Arne Bathke
| Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications [[electronic resource] ] : Selected Contributions from SimStat 2019 and Invited Papers / / edited by Jürgen Pilz, Viatcheslav B. Melas, Arne Bathke |
| Autore | Pilz Jürgen |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (265 pages) |
| Disciplina | 519.57 |
| Altri autori (Persone) |
MelasViatcheslav B
BathkeArne |
| Collana | Contributions to Statistics |
| Soggetto topico |
Statistics
Mathematical statistics - Data processing Experimental design Machine learning Stochastic models Statistical Theory and Methods Statistics and Computing Design of Experiments Machine Learning Applied Statistics Stochastic Modelling in Statistics |
| ISBN | 3-031-40055-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Part I Invited Papers -- 1 Likelihood Ratios in Forensics: What They Are and What They Are Not -- 1.1 Introduction -- 1.2 Lindley's Likelihood Ratio (LLR) -- 1.2.1 Notations -- 1.2.2 A Frequentist Framework for Lindley's Likelihood Ratio (LLR) -- 1.3 Score-Based Likelihood Ratio (SLR) -- 1.3.1 The Expression of the SLR -- 1.3.2 The Glass Example -- 1.4 Discussion -- References -- 2 MANOVA for Large Number of Treatments -- 2.1 Introduction -- 2.2 Notations and Model Setup -- 2.3 Simulations -- 2.3.1 MANOVA Tests for Large g -- 2.3.2 Special Case: ANOVA for Large g -- 2.4 Discussion and Outlook -- References -- 3 Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model -- 3.1 Introduction -- 3.2 Meteorological Monitoring Network -- 3.3 Wind Field Modeling -- 3.3.1 Mass Correction of the Wind Field -- 3.3.2 Plume Rise -- 3.4 Stochastic Particle Model -- 3.4.1 Deposition -- 3.4.2 Implementation -- 3.5 Dynamic Gaussian Plume Model -- 3.6 Implementation on the Server -- 3.7 A Real-World Example with Application to an Alpine Valley -- 3.8 Conclusions and Outlook -- References -- 4 On an Alternative Trigonometric Strategy for StatisticalModeling -- 4.1 Introduction -- 4.2 The Alternative Sine Distribution -- 4.2.1 Presentation -- 4.2.2 Moment Properties -- 4.2.3 Parametric Extensions -- 4.3 AS Generated Family -- 4.3.1 Definition -- 4.3.2 Series Expansions -- 4.3.3 Example: The ASE Exponential Distribution -- 4.3.4 Moment Properties -- 4.4 Application to a Famous Cancer Data -- 4.5 Conclusion -- References -- Part II Design of Experiments -- 5 Incremental Construction of Nested Designs Basedon Two-Level Fractional Factorial Designs -- 5.1 Introduction -- 5.2 Greedy Coffee-House Design -- 5.3 Two-Level Fractional Factorial Designs -- 5.3.1 Half Fractions: m=1.
5.3.2 Several Generators -- 5.3.2.1 Defining Relations -- 5.3.2.2 Resolution -- 5.3.2.3 Word Length Pattern -- 5.3.3 Minimum Size -- 5.4 Two-Level Factorial Designs and Error-Correcting Codes -- 5.4.1 Definitions and Properties -- 5.4.2 Examples -- 5.5 Maximin Distance Properties of Two-Level Factorial Designs -- 5.5.1 Neighbouring Pattern and Distant Site Pattern -- 5.5.2 Optimal Selection of Generators by Simulated Annealing -- 5.5.2.1 SA Algorithm for the Maximisation of ρH -- 5.6 Covering Properties of Two-Level Factorial Designs -- 5.6.1 Bounds on CRH(Xn) -- 5.6.2 Calculation of CRH(Xn) -- 5.6.2.1 Algorithmic Construction of a Lower Bound on CRH(Xn) -- 5.7 Greedy Constructions Based on Fractional Factorial Designs -- 5.7.1 Base Designs -- 5.7.2 Rescaled Designs -- 5.7.3 Projection Properties -- 5.8 Summary and Future Work -- Appendix -- References -- 6 A Study of L-Optimal Designs for the Two-Dimensional Exponential Model -- 6.1 Introduction -- 6.2 Equivalence Theorem for L-Optimal Designs -- 6.3 General Case -- 6.4 Excess and Saturated Designs -- References -- 7 Testing for Randomized Block Single-Case Designsby Combined Permutation Tests with Multivariate Mixed Data -- 7.1 Introduction -- 7.2 Randomized Block Single-Case Designs and NPC -- 7.3 Simulation Study -- 7.4 A Real Case Study -- 7.5 Conclusions -- References -- 8 Adaptive Design Criteria Motivated by a Plug-In Percentile Estimator -- 8.1 Introduction -- 8.2 Problem Formulation and Background -- 8.2.1 Problem Formulation -- 8.2.2 Background -- 8.3 The Plug-In Estimator -- 8.4 Adaptive ``Plug-In'' Criteria -- 8.4.1 Monte Carlo Approximation -- 8.4.2 Monte Carlo Approximation Assuming Independency -- 8.4.3 Assuming Independency and Neglecting Uncertainty -- 8.4.4 Using SUR Design Criterion for Exceedance Probability -- 8.5 Numerical Implementation -- 8.6 Numerical Study. 8.6.1 Comparison Study -- 8.6.2 Methodology -- 8.6.2.1 Case Studies -- 8.6.2.2 Performance Indicators -- 8.6.3 Numerical Results -- 8.6.3.1 Estimators Performance -- 8.6.3.2 Implementation -- 8.6.3.3 Criteria -- 8.7 Conclusions -- Appendix 1 -- Posterior Mean and Variance of f Under the Gaussian Process Assumption -- SUR Design Criteria for Exceedance Probability Estimation -- Appendix 2 -- References -- Part III Queueing and Inventory Analysis -- 9 On a Parametric Estimation for a Convolutionof Exponential Densities -- 9.1 Introduction -- 9.2 Convolution of the Exponential Densities -- 9.3 ML Estimation of the Parameters -- 9.4 Parameter's Estimation by the Moments' Method -- 9.5 Approximation of the Density -- 9.6 Experimental Study -- 9.7 Application to a Single Queueing System M/G/1/k -- 9.8 Conclusions -- References -- 10 Statistical Estimation with a Known Quantileand Its Application in a Modified ABC-XYZ Analysis -- 10.1 Introduction -- 10.2 Methods -- 10.2.1 Statistical Estimation with a Known Quantile -- 10.2.2 ABC-XYZ Analysis -- 10.3 ABC-XYZ Analysis Modified with a Known Quantile -- 10.4 Conclusions -- References -- Part IV Machine Learning and Applications -- 11 A Study of Design of Experiments and Machine Learning Methods to Improve Fault Detection Algorithms -- 11.1 Introduction -- 11.2 Design of Experiments and Machine Learning Modelling -- 11.3 Application to Fault Detection -- 11.3.1 Design of Experiments Step -- 11.3.2 Machine Learning Modelling Step -- 11.3.2.1 Refrigerant Undercharge: Fault Detection -- 11.3.2.2 Condenser Fouling: Fault Detection -- 11.4 Conclusions -- References -- 12 Microstructure Image Segmentation Using Patch-Based Clustering Approach -- 12.1 Introduction -- 12.2 Input Data -- 12.3 Previous Work -- 12.4 Grain Segmentation -- 12.4.1 Seeded Region Growing (SRG) -- 12.4.2 Image Denoising and Patch Determination. 12.4.3 Feature Extraction -- 12.4.4 Patch Clustering -- 12.4.5 Implementation -- 12.5 Results -- 12.6 Conclusion and Outlook -- References -- 13 Clustering and Symptom Analysis in Binary Datawith Application -- 13.1 Introduction -- 13.2 The Symptom Analysis -- 13.2.1 The Symptom and Syndrome Definition -- 13.2.2 Impulse Vector and Super-symptoms -- 13.2.3 Prefigurations of Super-symptom -- 13.2.4 The Super-symptom Recovery by Vector β -- 13.2.5 Clustering in Dichotomous Space and Symptom Analysis -- 13.3 The Medical Application of the Clustering and Symptom Analysis in Binary Data -- 13.3.1 Dataset -- 13.3.2 Result and Discussion -- 13.4 Conclusion -- References -- 14 Big Data for Credit Risk Analysis: Efficient Machine Learning Models Using PySpark -- 14.1 Introduction -- 14.2 Data Processing -- 14.2.1 Data Treatment -- 14.2.2 Data Storage and Distribution -- 14.2.3 Munge Data -- 14.2.4 Creating New Measures -- 14.2.5 Missing Values Imputation and Outliers Treatment -- 14.2.6 One-Hot Code and Dummy Variables -- 14.2.7 Final Dataset -- 14.3 Method and Models -- 14.3.1 Method -- 14.3.2 Model Building -- 14.4 Results and Credit Scorecard Conversion -- 14.5 Conclusion -- Appendix 1 -- Appendix 2 -- References. |
| Record Nr. | UNINA-9910754092903321 |
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Pilz Jürgen
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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