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GenEst statistical models--a generalized estimator of mortality / / by Daniel Dalthorp [and 7 others]
| GenEst statistical models--a generalized estimator of mortality / / by Daniel Dalthorp [and 7 others] |
| Autore | Dalthorp Daniel H. |
| Pubbl/distr/stampa | Reston, Virginia : , : U.S. Department of the Interior, U.S. Geological Survey, , 2018 |
| Descrizione fisica | 1 online resource (iv, 13 pages) |
| Collana | Techniques and methods |
| Soggetto topico |
Regression analysis - Mathematical models
Regression analysis - Computer programs |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910716925003321 |
Dalthorp Daniel H.
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| Reston, Virginia : , : U.S. Department of the Interior, U.S. Geological Survey, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Methods for estimating annual exceedance probability discharges for streams in Arkansas, based on data through water year 2013 / / by by Daniel M. Wagner, Joshua D. Krieger, and Andrea G. Veilleux ; prepared in cooperation with the Arkansas State Highway and Transportation Department and the U.S. Army Corps of Engineers, Little Rock District
| Methods for estimating annual exceedance probability discharges for streams in Arkansas, based on data through water year 2013 / / by by Daniel M. Wagner, Joshua D. Krieger, and Andrea G. Veilleux ; prepared in cooperation with the Arkansas State Highway and Transportation Department and the U.S. Army Corps of Engineers, Little Rock District |
| Autore | Wagner Daniel M. |
| Pubbl/distr/stampa | Reston, Virginia : , : U.S. Department of the Interior, U.S. Geological Survey, , 2016 |
| Descrizione fisica | 1 online resource (ix, 136 pages) : illustrations (some color) + + 4 appendix tables |
| Collana | Scientific investigations report |
| Soggetto topico |
Streamflow - Estimates - Arkansas - Mathematical models
Stream measurements - Arkansas - Mathematical models Stream-gaging stations - Arkansas Flood forecasting - Southern States - Mathematical models Regression analysis - Mathematical models |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910707303603321 |
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Wagner Daniel M.
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| Reston, Virginia : , : U.S. Department of the Interior, U.S. Geological Survey, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Nonlinear regression modeling for engineering applications : modeling, model validation, and enabling design of experiments / / R. Russell Rhinehart
| Nonlinear regression modeling for engineering applications : modeling, model validation, and enabling design of experiments / / R. Russell Rhinehart |
| Autore | Rhinehart R. Russell <1946-> |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Chichester, England : , : Wiley : , : ASME Press, , 2016 |
| Descrizione fisica | 1 online resource (403 p.) |
| Disciplina | 620.001/519536 |
| Collana | Wiley-ASME Press Series |
| Soggetto topico |
Regression analysis - Mathematical models
Engineering - Mathematical models |
| ISBN |
1-5231-5487-X
1-118-59795-8 1-118-59793-1 1-118-59797-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Series Preface; Preface; Acknowledgments; Nomenclature; Symbols; Part I Introduction; Chapter 1 Introductory Concepts; 1.1 Illustrative Example-Traditional Linear Least-Squares Regression; 1.2 How Models Are Used; 1.3 Nonlinear Regression; 1.4 Variable Types; 1.5 Simulation; 1.6 Issues; 1.7 Takeaway; Exercises; Chapter 2 Model Types; 2.1 Model Terminology; 2.2 A Classification of Mathematical Model Types; 2.3 Steady-State and Dynamic Models; 2.4 Pseudo-First Principles-Appropriated First Principles; 2.5 Pseudo-First Principles-Pseudo-Components
2.6 Empirical Models with Theoretical Grounding2.7 Empirical Models with No Theoretical Grounding; 2.8 Partitioned Models; 2.9 Empirical or Phenomenological?; 2.10 Ensemble Models; 2.11 Simulators; 2.12 Stochastic and Probabilistic Models; 2.13 Linearity; 2.14 Discrete or Continuous; 2.15 Constraints; 2.16 Model Design (Architecture, Functionality, Structure); 2.17 Takeaway; Exercises; Part II Preparation for Underlying Skills; Chapter 3 Propagation of Uncertainty; 3.1 Introduction; 3.2 Sources of Error and Uncertainty; 3.3 Significant Digits; 3.4 Rounding Off 3.5 Estimating Uncertainty on Values3.6 Propagation of Uncertainty-Overview-Two Types, Two Ways Each; 3.7 Which to Report? Maximum or Probable Uncertainty; 3.8 Bootstrapping; 3.9 Bias and Precision; 3.10 Takeaway; Exercises; Chapter 4 Essential Probability and Statistics; 4.1 Variation and Its Role in Topics; 4.2 Histogram and Its PDF and CDF Views; 4.3 Constructing a Data-Based View of PDF and CDF; 4.4 Parameters that Characterize the Distribution; 4.5 Some Representative Distributions; 4.6 Confidence Interval; 4.7 Central Limit Theorem; 4.8 Hypothesis and Testing 4.9 Type I and Type II Errors, Alpha and Beta4.10 Essential Statistics for This Text; 4.11 Takeaway; Exercises; Chapter 5 Simulation; 5.1 Introduction; 5.2 Three Sources of Deviation: Measurement, Inputs, Coefficients; 5.3 Two Types of Perturbations: Noise (Independent) and Drifts (Persistence); 5.4 Two Types of Influence: Additive and Scaled with Level; 5.5 Using the Inverse CDF to Generate n and u from UID(0, 1); 5.6 Takeaway; Exercises; Chapter 6 Steady and Transient State Detection; 6.1 Introduction; 6.2 Method; 6.3 Applications; 6.4 Takeaway; Exercises Part III Regression, Validation, DesignChapter 7 Regression Target - Objective Function; 7.1 Introduction; 7.2 Experimental and Measurement Uncertainty-Static and Continuous Valued; 7.3 Likelihood; 7.4 Maximum Likelihood; 7.5 Estimating x and y Values; 7.6 Vertical SSD-A Limiting Consideration of Variability Only in the Response Measurement; 7.7 r-Square as a Measure of Fit; 7.8 Normal, Total, or Perpendicular SSD; 7.9 Akaho's Method; 7.10 Using a Model Inverse for Regression; 7.11 Choosing the Dependent Variable; 7.12 Model Prediction with Dynamic Models 7.13 Model Prediction with Classification Models |
| Record Nr. | UNINA-9910134878003321 |
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Rhinehart R. Russell <1946->
|
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| Chichester, England : , : Wiley : , : ASME Press, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Nonlinear regression modeling for engineering applications : modeling, model validation, and enabling design of experiments / / R. Russell Rhinehart
| Nonlinear regression modeling for engineering applications : modeling, model validation, and enabling design of experiments / / R. Russell Rhinehart |
| Autore | Rhinehart R. Russell <1946-> |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Chichester, England : , : Wiley : , : ASME Press, , 2016 |
| Descrizione fisica | 1 online resource (403 p.) |
| Disciplina | 620.001/519536 |
| Collana | Wiley-ASME Press Series |
| Soggetto topico |
Regression analysis - Mathematical models
Engineering - Mathematical models |
| ISBN |
1-5231-5487-X
1-118-59795-8 1-118-59793-1 1-118-59797-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Series Preface; Preface; Acknowledgments; Nomenclature; Symbols; Part I Introduction; Chapter 1 Introductory Concepts; 1.1 Illustrative Example-Traditional Linear Least-Squares Regression; 1.2 How Models Are Used; 1.3 Nonlinear Regression; 1.4 Variable Types; 1.5 Simulation; 1.6 Issues; 1.7 Takeaway; Exercises; Chapter 2 Model Types; 2.1 Model Terminology; 2.2 A Classification of Mathematical Model Types; 2.3 Steady-State and Dynamic Models; 2.4 Pseudo-First Principles-Appropriated First Principles; 2.5 Pseudo-First Principles-Pseudo-Components
2.6 Empirical Models with Theoretical Grounding2.7 Empirical Models with No Theoretical Grounding; 2.8 Partitioned Models; 2.9 Empirical or Phenomenological?; 2.10 Ensemble Models; 2.11 Simulators; 2.12 Stochastic and Probabilistic Models; 2.13 Linearity; 2.14 Discrete or Continuous; 2.15 Constraints; 2.16 Model Design (Architecture, Functionality, Structure); 2.17 Takeaway; Exercises; Part II Preparation for Underlying Skills; Chapter 3 Propagation of Uncertainty; 3.1 Introduction; 3.2 Sources of Error and Uncertainty; 3.3 Significant Digits; 3.4 Rounding Off 3.5 Estimating Uncertainty on Values3.6 Propagation of Uncertainty-Overview-Two Types, Two Ways Each; 3.7 Which to Report? Maximum or Probable Uncertainty; 3.8 Bootstrapping; 3.9 Bias and Precision; 3.10 Takeaway; Exercises; Chapter 4 Essential Probability and Statistics; 4.1 Variation and Its Role in Topics; 4.2 Histogram and Its PDF and CDF Views; 4.3 Constructing a Data-Based View of PDF and CDF; 4.4 Parameters that Characterize the Distribution; 4.5 Some Representative Distributions; 4.6 Confidence Interval; 4.7 Central Limit Theorem; 4.8 Hypothesis and Testing 4.9 Type I and Type II Errors, Alpha and Beta4.10 Essential Statistics for This Text; 4.11 Takeaway; Exercises; Chapter 5 Simulation; 5.1 Introduction; 5.2 Three Sources of Deviation: Measurement, Inputs, Coefficients; 5.3 Two Types of Perturbations: Noise (Independent) and Drifts (Persistence); 5.4 Two Types of Influence: Additive and Scaled with Level; 5.5 Using the Inverse CDF to Generate n and u from UID(0, 1); 5.6 Takeaway; Exercises; Chapter 6 Steady and Transient State Detection; 6.1 Introduction; 6.2 Method; 6.3 Applications; 6.4 Takeaway; Exercises Part III Regression, Validation, DesignChapter 7 Regression Target - Objective Function; 7.1 Introduction; 7.2 Experimental and Measurement Uncertainty-Static and Continuous Valued; 7.3 Likelihood; 7.4 Maximum Likelihood; 7.5 Estimating x and y Values; 7.6 Vertical SSD-A Limiting Consideration of Variability Only in the Response Measurement; 7.7 r-Square as a Measure of Fit; 7.8 Normal, Total, or Perpendicular SSD; 7.9 Akaho's Method; 7.10 Using a Model Inverse for Regression; 7.11 Choosing the Dependent Variable; 7.12 Model Prediction with Dynamic Models 7.13 Model Prediction with Classification Models |
| Record Nr. | UNINA-9910823773703321 |
|
Rhinehart R. Russell <1946->
|
||
| Chichester, England : , : Wiley : , : ASME Press, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
