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000826713 24504 $$aThe mathematics of the uncertain :$$ba tribute to Pedro Gil /$$cEduardo Gil, Eva Gil, Juan Gil, María Ángeles Gil, editors.
000826713 264_1 $$aCham :$$bSpringer,$$c2018.
000826713 300__ $$a1 online resource (xxv, 917 pages) :$$billustrations.
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000826713 4901_ $$aStudies in systems, decision and control,$$x2198-4182 ;$$vvolume 142
000826713 5050_ $$aIntro; Foreword; Preface; Contents; Part I Pedro Gil's Vision on the Mathematics of the Uncertain; The Mathematics of the Uncertain; 1 The Mathematics of Chance: Probability Calculus and Statistics; 1.1 The Use of Statistics...; 1.2 ...and the Abuse of Statistics; 1.3 Polls and More; 2 The Mathematics of Communication: Information Theory; 2.1 Coding and Cryptology; 2.2 Chicken and Egg: Information or Probability?; 3 The Mathematics of Imprecision: Fuzzy Sets; 3.1 Fuzzy Technology and...; 4 Epilogue; Part II The Mathematics of Chance: Probability, Statistics and Applications
000826713 5058_ $$aUsing Mathematica to Calculate Shortest Confidence Intervals1 Preliminaries; 2 Confidence Interval for the Variance and Standard Deviation of a Normal; 3 Confidence Interval for Ratios of Variances and Standard Deviations of Independent Normal Distributions; 4 Bayesian Confidence Intervals; References; An Optimal Transportation Approach for Assessing Almost Stochastic Order; 1 Introduction; 2 Main Results; 3 Appendix; References; An Alternative to the Variation Coefficient; 1 Introduction; 2 Definition of a New Coefficient
000826713 5058_ $$a3 The Trimmed Representation Coefficient for a Normally Distributed Random Variable4 Interpretation of the Trimmed Representation Coefficient; 5 Trimmed Representation Coefficient for Other Theoretical Distributions; References; Nonparametric Mean Estimation for Big-But-Biased Data; 1 Introduction; 2 The Samples and Their Sizes; 3 Mean Estimation in B3D; 4 Case Study with Simulated Data; References; Band Depths Based on Multiple Time Instances; 1 Introduction; 2 Regions Formed by Samples in Functional Spaces; 3 Simplicial-Type Band Depths; 4 Data Examples; References
000826713 5058_ $$aOn the Combination of Depth-Based Ranks1 Introduction; 2 Depths, Depth-Based Ranks, and Control Charts; 3 The Proposal; 4 Comparison Results; 5 Application; References; A Bayesian Network Model for the Probabilistic Safety Assessment of Roads; 1 Introduction; 2 Building the Bayesian Network; 3 Learning the Bayesian Network Model; 4 Network Partition; 5 Example of Application; 6 Conclusions; References; The Spiking Problem in the Context of the Isotonic Regression; 1 Introduction; 2 The New Procedures; 3 Simulations; 4 Discussion and Acknowledgement; References
000826713 5058_ $$aEstimation of the Inter-occurrence Time Between Events from Incomplete Data. Analysis of Periods of Unemployment in Spain1 Introduction. Objectives and Methodology; 2 Redistribution of the Observations. Correction of the Heaping Effect; 3 Conditional Mean Estimation for Each Sex over the Different Age Ranges; 4 Non-parametric Estimation of Conditional Distributions and Quantile Comparison; References; Modelling Dynamic Hotel Pricing with Isotonic Functions; 1 Introduction; 2 Data Sources and Methodology; 3 Research Findings and Conclusion; References
000826713 5058_ $$aOn Multistage Stochastic Mixed -- 1 Optimization with Time-Consistent Stochastic Dominance Risk Averse Management
000826713 506__ $$aAccess limited to authorized users.
000826713 520__ $$aThis book is a tribute to Professor Pedro Gil, who created the Department of Statistics, OR and TM at the University of Oviedo, and a former President of the Spanish Society of Statistics and OR (SEIO). In more than eighty original contributions, it illustrates the extent to which Mathematics can help manage uncertainty, a factor that is inherent to real life. Today it goes without saying that, in order to model experiments and systems and to analyze related outcomes and data, it is necessary to consider formal ideas and develop scientific approaches and techniques for dealing with uncertainty. Mathematics is crucial in this endeavor, as this book demonstrates. As Professor Pedro Gil highlighted twenty years ago, there are several well-known mathematical branches for this purpose, including Mathematics of chance (Probability and Statistics), Mathematics of communication (Information Theory), and Mathematics of imprecision (Fuzzy Sets Theory and others). These branches often intertwine, since different sources of uncertainty can coexist, and they are not exhaustive. While most of the papers presented here address the three aforementioned fields, some hail from other Mathematical disciplines such as Operations Research; others, in turn, put the spotlight on real-world studies and applications. The intended audience of this book is mainly statisticians, mathematicians and computer scientists, but practitioners in these areas will certainly also find the book a very interesting read.
000826713 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 6, 2018).
000826713 650_0 $$aMathematical statistics.
000826713 650_0 $$aUncertainty$$xMathematical models.
000826713 7001_ $$aGil, Eduardo,$$eeditor.
000826713 7001_ $$aGil, Eva,$$eeditor.
000826713 7001_ $$aGil, Juan,$$d1968-$$eeditor.
000826713 7001_ $$aGil, María Á.$$q(Maria-Ángeles),$$eeditor.
000826713 7001_ $$aGil, Pedro,$$ehonouree.
000826713 77608 $$iPrint version: $$z331973847X$$z9783319738475$$w(OCoLC)1015240389
000826713 830_0 $$aStudies in systems, decision and control ;$$vv. 142.
000826713 852__ $$bebk
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