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Biblioteca

MARC Lista (tabellare)
Parametric and Nonparametric Statistics for Sample Surveys and Customer Satisfaction Data / / by Rosa Arboretti, Arne Bathke, Stefano Bonnini, Paolo Bordignon, Eleonora Carrozzo, Livio Corain, Luigi Salmaso
Parametric and Nonparametric Statistics for Sample Surveys and Customer Satisfaction Data / / by Rosa Arboretti, Arne Bathke, Stefano Bonnini, Paolo Bordignon, Eleonora Carrozzo, Livio Corain, Luigi Salmaso
Autore Arboretti Rosa
Edizione [1st ed. 2018.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Descrizione fisica 1 online resource (90 pages)
Disciplina 382.072
Collana SpringerBriefs in Statistics
Soggetto topico Statistics
Computer science - Mathematics
Mathematical statistics
Mathematics - Data processing
Statistical Theory and Methods
Probability and Statistics in Computer Science
Computational Mathematics and Numerical Analysis
ISBN 3-319-91740-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. The CUB models -- Chapter 2. Customer satisfaction heterogeneity -- Chapter 3. Ranking multivariate populations -- Chapter 4. Composite indicators and satisfaction profiles -- Chapter 5. Analyzing Survey Data Using Multivariate Rank-Based Inference.
Record Nr. UNINA-9910300120603321
Arboretti Rosa  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications : 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 : 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
Disseny d'experiments
Aprenentatge automàtic
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 9783031400551
3031400550
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
Pilz Jürgen  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui