Data science: theory and applications
Gespeichert in:
| Weitere beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Amsterdam
North-Holland, an imprint of Elsevier
[2021]
|
| Schriftenreihe: | Handbook of statistics
volume 44 |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032342563&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xv, 331 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780323852005 |
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| 245 | 1 | 0 | |a Data science |b theory and applications |c edited by Arni S.R. Srinivasa Rao, Medical College of Georgie, Augusta, Georgia, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India |
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Datensatz im Suchindex
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| adam_text | Contents Contributors Preface x¡ x¡¡¡ Section I Animal models and ecological large data methods 1. Statistical outline of animal home ranges: An application of set estimation Amparo Bailio and José Enrique Chacón 1 Introduction to home range estimation 2. 3 3 3 5 7 8 9 9 21 28 1.1 Problem statement 1.2 Connection to the set estimationproblem 1.3 Characteristics of animal location data 1.4 A real data set: Mongolian wolves 2 Statistical techniques for home range estimation 2.1 Assuming location data are independent 2.2 Incorporating time dependency 3 Developing trends, open problems, and future work 3.1 Incorporating time dependence into the home range estimator 3.2 Taking advantage of explanatory variables 3.3 Selecting the optimal home range Acknowledgments References 29 29 30 31 32 Modeling extreme climatic events using the generalized extreme value (GEV) distribution 39 Diana Rypkema and Shripad Tuljapurkar 1 2 Introduction 1.1 Previous models of extreme events inecology The GEV value distribution 2.1 A brief history of the GEV 2.2 Definitions 2.3 Parameterizing the GEV 2.4 Return level vs return period 2.5 Evaluating model fit 2.6 Applying climate change to the GEV 40 43 45 45 45 47 47 48 49 v
Contents VI 3 Case study: Hurricanes and Ardisia escallonioides 3.1 Traditional hurricane modeling 3.2 The data: HURDAT2 3.3 Study region and storm selection criteria 3.4 Fitting the CEV model to our case study 3.5 The demographic model 3.6 Constructing the environmental transition matrix (P) 3.7 Incorporating climate change in the environmental transition matrix (P) 3.8 Population simulations 3.9 Sensitivity analysis 3.10 Effects of changing hurricane frequency, intensity, damage levels, and/or canopy recovery rates 4 Challenges 4.1 Defining extreme events 4.2 Measuring the impacts of extreme events 5 Open questions and future applications 6 Summary 7 Code availability Acknowledgments References 49 50 50 50 52 53 53 55 55 56 57 60 60 63 64 65 65 65 66 Section II Engineering sciences data 3. Blockchain technology:Theory and practice Srikanth Cherukupally 1 Introduction 1.1 Bitcoin network 1.2 Blockchain 1.3 How is blockchain different from present database systems 2 Cryptographic foundations 2.1 Secure hash function 2.2 Open issue in theoretical computer science 2.3 Encryption methods 2.4 Digital signature mechanism 2.5 Pseudo random number generators 2.6 Zero-knowledge protocols 3 Blockchain platforms 3.1 Commonalities of public and private blockchains 3.2 Consistency of Ledger across the network 4 Public blockchains 4.1 Bitcoin network 4.2 Ethereum network 75 76 76 77 78 80 80 83 85 87 87 88 92 92 94 94 95 96
Contents 5 Private blockchains 5.1 Hyperledger fabric 5.2 Applications 5.3 Discussion andconclusions References Further reading 4. vii 98 98 100 101 102 103 Application of data handling techniques to predict pavement performance 105 Sireesh Saride, Pranav R.T. Peddinti, and B. Munwar Basha 1 Introduction 2 Importance of data in pavement designs 3 Challenges with pavement data handling 4 Data handling techniques in pavement analysis 5 Data handling and automation framework forpavement design 5.1 Data generation 5.2 Data appending 5.3 Automation 5.4 Data extraction 6 Efficiency of the automation-based data handling framework 7 Regression analysis using the generated data 8 Validation of proposed damage models 9 Summary and recommendations References 105 109 111 111 112 113 113 116 117 117 118 123 124 125 Section III Statistical estimation designs: Fractional fields, biostatistics and non-parametrics 5. On the usefulness of lattice approximations for fractional Gaussian fields 131 Somák Dutta and Debashis Mondai 1 Introduction 2 Fractional Gaussian fields and their approximations 2.1 Fractional Gaussian fields 2.2 Lattice approximations 3 Model based geostatistics 3.1 Maximum likelihood estimation 4 Simulation studies 4.1 An experiment with power-law variogram 4.2 Large scale computation with latticeapproximations 5 Indian ocean surface temperature from Argo floats 6 Concluding remarks Acknowledgments Appendix MATLAB codes for Section 3.1.1 MATLAB codes for Section 3.1.2 References 131 133 133 134 137 137 141 141 143 143 147 149 149 149 150 153
Contents VIII 6. Estimating individual-level average treatment effects: Challenges, modeling approaches, and practical applications 155 Victor B. Talisa and Chung-Chou H. Chang 1 2 Introduction Introduction to statistical learning 2.1 Preliminaries 2.2 Bias-variance decomposition 2.3 Examples of the bias-variance tradeoff 2.4 Regularization 2.5 Evaluating a model s prediction performance 3 Causal inference 3.1 Notation and counterfactual theory 3.2 Theoretical MSE criteria for causal inference 4 Challenges in learning the CATE function 4.1 The naive optimization strategy fails 4.2 Selection bias 4.3 Regularization biases 5 Survey of CATE modeling approaches 5.1 Meta-learners 5.2 Transformed outcome methods 5.3 Shared basis methods 5.4 Neural networks 5.5 Bayesian nonparametric methods 6 CATE model selection and assessment 6.1 Survey of evaluation methods for estimators of r(x) 7 Practical application of CATE estimates 7.1 Stratification on the CATEs 7.2 Subgroup identification 8 Summary References 7. Nonparametric data science: Testing hypotheses in large complex data 156 157 157 159 160 163 165 166 166 168 168 169 174 175 178 178 180 181 182 183 183 184 186 186 192 196 197 201 Sunil Math ur 1 Introduction 2 Nonparametric data science 3 One-sample methods 4 Two-sample methods 5 Multi-sample methods 6 Multivariate methods 7 Ranked-set based methods 8 Changepoint detection 9 Final remarks References 202 204 204 209 211 212 216 223 227 229
Contents ¡X Section IV Network models and COVID-19 modeling 8. Network models in epidemiology 235 Tae Jin Lee, Masayuki Kakehashi, and Arni S.R. Srinivasa Rao 1 2 Introduction Network models 2.1 2.2 2.3 3 Static network Dynamic network Multi-layered model Syringe exchange on Hepatitis C 3.1 3.2 Syringe exchange SIRS model Syringe exchange SIRS model adjusted with network 4 Discussion References 9. Modeling and forecasting the spreadof COVID-19 pandemic in India and significance of lockdown: A mathematical outlook 235 238 240 244 249 250 250 252 255 255 257 Brijesh P. Singh 1 2 Background Why mathematical modeling? 2.1 3 SIR and SEIR model Formulation of SEIR model 3.1 3.2 3.3 3.4 Growthmodels Significance of lockdown Propagation model (based on Newton s law of cooling) Joinpoint regression model 4 Conclusions References Further reading I0. Mathematical modeling as a tool for policy decision making: Applications to the COVID-19 pandemic 258 262 264 266 266 270 274 278 284 287 289 291 J. Panovska-Griffiths, C.C. Kerr, W. Waites, and R.M. Stuart 1 Introduction 1.1 Overview of mathematical modeling 1.2 Modeling to explain or to predict 1.3 What does it mean for models to be right ? 1.4 Aims and purposes of this work 292 292 293 294 2 Modeling of infectious disease 294 2.1 2.2 2.3 294 296 305 The essence of modeling infectious diseases History ofmodeling infectious diseases Challenges of modeling infectious diseases 292
Contents x 3 Models for COVID-19 3.1 Compartmental modeling and the SEIR framework: Overview and examplesof COVID-19 models 3.2 Agent-based models: Overview and examples of COVID-19 models 3.3 Branching process models: Overview and examples of COVID-19 models 4 Applications of modelingof COVID-19: Three case studies 4.1 Case study 1 : Application of SEIR-TTI model to the UK COVID-19 epidemic 4.2 Case study 2: Application of Covăsim to the UK COVID-19 epidemic 4.3 Case study 3: Application of rule-basedmodeling 5 Conclusions Acknowledgments References Index 306 306 307 307 308 308 311 317 322 322 323 327
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| indexdate | 2024-12-20T19:05:03Z |
| institution | BVB |
| isbn | 9780323852005 |
| language | English |
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| physical | xv, 331 Seiten Illustrationen, Diagramme 24 cm |
| publishDate | 2021 |
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| publishDateSort | 2021 |
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| series2 | Handbook of statistics |
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| title | Data science theory and applications |
| title_auth | Data science theory and applications |
| title_exact_search | Data science theory and applications |
| title_full | Data science theory and applications edited by Arni S.R. Srinivasa Rao, Medical College of Georgie, Augusta, Georgia, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India |
| title_fullStr | Data science theory and applications edited by Arni S.R. Srinivasa Rao, Medical College of Georgie, Augusta, Georgia, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India |
| title_full_unstemmed | Data science theory and applications edited by Arni S.R. Srinivasa Rao, Medical College of Georgie, Augusta, Georgia, United States, C.R. Rao, AIMSCS, University of Hyderabad Campus, Hyderabad, India |
| title_short | Data science |
| title_sort | data science theory and applications |
| title_sub | theory and applications |
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| topic_facet | Theorie Anwendung Data Science Aufsatzsammlung |
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