Autore	Livingstone D (David)
Pubbl/distr/stampa	Hoboken, N.J., : Wiley, 2009
Descrizione fisica	1 online resource (359 p.)
Disciplina	519.57 540.72
Soggetto topico	Science - Statistical methods Experimental design
Soggetto genere / forma	Electronic books.
ISBN	1-282-47203-8 9786612472039 0-470-01791-0 0-470-68481-X
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	A Practical Guide toScientific Data Analysis; Contents; Preface; Abbreviations; 1 Introduction: Data and Its Properties, Analytical Methods and Jargon; 1.1 Introduction; 1.2 Types of Data; 1.3 Sources of Data; 1.3.1 Dependent Data; 1.3.2 Independent Data; 1.4 The Nature of Data; 1.4.1 Types of Data and Scales of Measurement; 1.4.2 Data Distribution; 1.4.3 Deviations in Distribution; 1.5 Analytical Methods; 1.6 Summary; References; 2 Experimental Design - Experiment and Set Selection; 2.1 What is Experimental Design?; 2.2 Experimental Design Techniques; 2.2.1 Single-factor Design Methods 2.2.2 Factorial Design (Multiple-factor Design)2.2.3 D-optimal Design; 2.3 Strategies for Compound Selection; 2.4 High Throughput Experiments; 2.5 Summary; References; 3 Data Pre-treatment and Variable Selection; 3.1 Introduction; 3.2 Data Distribution; 3.3 Scaling; 3.4 Correlations; 3.5 Data Reduction; 3.6 Variable Selection; 3.7 Summary; References; 4 Data Display; 4.1 Introduction; 4.2 Linear Methods; 4.3 Nonlinear Methods; 4.3.1 Nonlinear Mapping; 4.3.2 Self-organizing Map; 4.4 Faces, Flowerplots and Friends; 4.5 Summary; References; 5 Unsupervised Learning; 5.1 Introduction 5.2 Nearest-neighbour Methods5.3 Factor Analysis; 5.4 Cluster Analysis; 5.5 Cluster Significance Analysis; 5.6 Summary; References; 6 Regression Analysis; 6.1 Introduction; 6.2 Simple Linear Regression; 6.3 Multiple Linear Regression; 6.3.1 Creating Multiple Regression Models; 6.3.1.1 Forward Inclusion; 6.3.1.2 Backward Elimination; 6.3.1.3 Stepwise Regression; 6.3.1.4 All Subsets; 6.3.1.5 Model Selection by Genetic Algorithm; 6.3.2 Nonlinear Regression Models; 6.3.3 Regression with Indicator Variables 6.4 Multiple Regression: Robustness, Chance Effects, the Comparison of Models and Selection Bias6.4.1 Robustness (Cross-validation); 6.4.2 Chance Effects; 6.4.3 Comparison of Regression Models; 6.4.4 Selection Bias; 6.5 Summary; References; 7 Supervised Learning; 7.1 Introduction; 7.2 Discriminant Techniques; 7.2.1 Discriminant Analysis; 7.2.2 SIMCA; 7.2.3 Confusion Matrices; 7.2.4 Conditions and Cautions for Discriminant Analysis; 7.3 Regression on Principal Components and PLS; 7.3.1 Regression on Principal Components; 7.3.2 Partial Least Squares; 7.3.3 Continuum Regression 7.4 Feature Selection7.5 Summary; References; 8 Multivariate Dependent Data; 8.1 Introduction; 8.2 Principal Components and Factor Analysis; 8.3 Cluster Analysis; 8.4 Spectral Map Analysis; 8.5 Models with Multivariate Dependent and Independent Data; 8.6 Summary; References; 9 Artificial Intelligence and Friends; 9.1 Introduction; 9.2 Expert Systems; 9.2.1 Log P Prediction; 9.2.2 Toxicity Prediction; 9.2.3 Reaction and Structure Prediction; 9.3 Neural Networks; 9.3.1 Data Display Using ANN; 9.3.2 Data Analysis Using ANN; 9.3.3 Building ANN Models; 9.3.4 Interrogating ANN Models 9.4 Miscellaneous AI Techniques
Record Nr.	UNINA-9910139536203321

Autore	Box George E. P
Edizione	[2nd ed.]
Pubbl/distr/stampa	Hoboken, N.J., : John Wiley, c2007
Descrizione fisica	1 online resource (873 p.)
Disciplina	519.57
Altri autori (Persone)	DraperNorman Richard
Collana	Wiley Series in Probability and Statistics
Soggetto topico	Experimental design Response surfaces (Statistics) Mixture distributions (Probability theory) Ridge regression (Statistics)
Soggetto genere / forma	Electronic books.
ISBN	1-282-24228-8 9786613813404 0-470-07276-8 0-470-07275-X
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Response Surfaces, Mixtures, and Ridge Analyses; Contents; Preface to the Second Edition; 1. Introduction to Response Surface Methodology; 1.1. Response Surface Methodology (RSM); 1.2. Indeterminancy of Experimentation; 1.3. Iterative Nature of the Experimental Learning Process; 1.4. Some Classes of Problems (Which, How, Why); 1.5. Need for Experimental Design; 1.6. Geometric Representation of Response Relationships; 1.7. Three Kinds of Applications; 2. The Use Of Graduating Functions; 2.1. Approximating Response Functions; 2.2. An Example; Appendix 2A. A Theoretical Response Function 3. Least Squares for Response Surface Work3.1. The Method of Least Squares; 3.2. Linear Models; 3.3. Matrix Formulas for Least Squares; 3.4. Geometry of Least Squares; 3.5. Analysis of Variance for One Regressor; 3.6. Least Squares for Two Regressors; 3.7. Geometry of the Analysis of Variance for Two Regressors; 3.8. Orthogonalizing the Second Regressor, Extra Sum of Squares Principle; 3.9. Generalization to p Regressors; 3.10. Bias in Least-Squares Estimators Arising from an Inadequate Model; 3.11. Pure Error and Lack of Fit; 3.12. Confidence Intervals and Confidence Regions 3.13. Robust Estimation, Maximum Likelihood, and Least SquaresAppendix 3A. Iteratively Reweighted Least Squares; Appendix 3B. Justification of Least Squares by the Gauss-Markov Theorem; Robustness; Appendix 3C. Matrix Theory; Appendix 3D. Nonlinear Estimation; Appendix 3E. Results Involving V(y); Exercises; 4. Factorial Designs at Two Levels; 4.1. The Value of Factorial Designs; 4.2. Two-Level Factorials; 4.3. A 2(6) Design Used in a Study of Dyestuffs Manufacture; 4.4. Diagnostic Checking of the Fitted Models, 2(6) Dyestuffs Example; 4.5. Response Surface Analysis of the 2(6) Design Data Appendix 4A. Yates' Method for Obtaining the Factorial Effects for a Two-Level DesignAppendix 4B. Normal Plots on Probability Paper; Appendix 4C. Confidence Regions for Contour Planes (see Section 4.5); Exercises; 5. Blocking and Fractionating 2(k) Factorial Designs; 5.1. Blocking the 2(6) Design; 5.2. Fractionating the 2(6) Design; 5.3. Resolution of a 2(k-p) Factorial Design; 5.4. Construction of 2(k-p) Designs of Resolution III and IV; 5.5. Combination of Designs from the Same Family; 5.6. Screening, Using 2(k-p) Designs (Involving Projections to Lower Dimensions) 5.7. Complete Factorials Within Fractional Factorial Designs5.8. Plackett and Burman Designs for n = 12 to 60 (but not 52); 5.9. Screening, Using Plackett and Burman Designs (Involving Projections to Lower Dimensions); 5.10. Efficient Estimation of Main Effects and Two-Factor Interactions Using Relatively Small Two-Level Designs; 5.11. Designs of Resolution V and of Higher Resolution; 5.12. Application of Fractional Factorial Designs to Response Surface Methodology; 5.13. Plotting Effects from Fractional Factorials on Probability Paper; Exercises 6. The Use of Steepest Ascent to Achieve Process Improvement
Record Nr.	UNINA-9910143691403321

Autore	CASELLA, George
Pubbl/distr/stampa	New York : Springer, c2008
Descrizione fisica	XXIII, 307 p. ; 25 cm.
Disciplina	519.57(Disegno sperimentale in statistica)
Collana	Springer texts in statistics
Soggetto topico	Disegni sperimentali
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Record Nr.	UNISA-990005551900203316

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

Autore	Federer Walter Theodore <1915->
Pubbl/distr/stampa	Hoboken, N.J., : Wiley-Interscience, 2007
Descrizione fisica	1 online resource (286 p.)
Disciplina	519.5 519.57
Altri autori (Persone)	KingFreedom <1955->
Collana	Wiley series in probability and statistics
Soggetto topico	Experimental design Blocks (Group theory)
Soggetto genere / forma	Electronic books.
ISBN	1-280-72186-3 9786610721863 0-470-10858-4 0-470-10857-6
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Variations on Split Plot and Split Block Experiment Designs; Contents; Preface; Chapter 1. The standard split plot experiment design; 1.1. Introduction; 1.2. Statistical design; 1.3. Examples of split-plot-designed experiments; 1.4. Analysis of variance; 1.5. F-tests; 1.6. Standard errors for means and differences between means; 1.7. Numerical examples; 1.8. Multiple comparisons of means; 1.9. One replicate of a split plot experiment design and missing observations; 1.10. Nature of experimental variation; 1.11. Repeated measures experiments; 1.12. Precision of contrasts; 1.13. Problems 1.14. ReferencesAppendix 1.1. Example 1.1 code; Appendix 1.2. Example 1.2 code; Chapter 2. Standard split block experiment design; 2.1. Introduction; 2.2. Examples; 2.3. Analysis of variance; 2.4. F-tests; 2.5. Standard errors for contrasts of effects; 2.6. Numerical examples; 2.7. Multiple comparisons; 2.8. One replicate of a split block design; 2.9. Precision; 2.10. Comments; 2.11. Problems; 2.12. References; Appendix 2.1. Example 2.1 code; Appendix 2.2. Example 2.2 code; Appendix 2.3. Problems 2.1 and 2.2 data; Chapter 3. Variations of the split plot experiment design; 3.1. Introduction 3.2. Split split plot experiment design3.3. Split split split plot experiment design; 3.4. Whole plots not in a factorial arrangement; 3.5. Split plot treatments in an incomplete block experiment design within each whole plot; 3.6. Split plot treatments in a row-column arrangement within each whole plot treatment and in different whole plot treatments; 3.7. Whole plots in a systematic arrangement; 3.8. Split plots in a systematic arrangement; 3.9. Characters or responses as split plot treatments; 3.10. Observational or experimental error? 3.11. Time as a discrete factor rather than as a continuous factor3.12. Inappropriate model?; 3.13. Complete confounding of some effects and split plot experiment designs; 3.14. Comments; 3.15. Problems; 3.16. References; Appendix 3.1. Table 3.1 code and data; Chapter 4. Variations of the split block experiment design; 4.1. Introduction; 4.2. One set of treatments in a randomized complete block and the other in a Latin square experiment design; 4.3. Both sets of treatments in split block arrangements; 4.4. Split block split block or strip strip block experiment design 4.5. One set of treatments in an incomplete block design and the second set in a randomized complete block design4.6. An experiment design split blocked across the entire experiment; 4.7. Confounding in a factorial treatment design and in a split block experiment design; 4.8. Split block experiment design with a control; 4.9. Comments; 4.10. Problems; 4.11. References; Appendix 4.1. Example 4.1 code; Chapter 5. Combinations of SPEDs and SBEDs; 5.1. Introduction; 5.2. Factors A and B in a split block experiment design and factor C in a split plot arrangement to factors A and B 5.3. Factor A treatments are the whole plot treatments and factors B and C treatments are in a split block arrangement within each whole plot
Record Nr.	UNINA-9910143685403321