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001482639 001__ 1482639
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001482639 020__ $$a9783031400551$$q(electronic bk.)
001482639 020__ $$a3031400550$$q(electronic bk.)
001482639 020__ $$z9783031400544
001482639 020__ $$z3031400542
001482639 0247_ $$a10.1007/978-3-031-40055-1$$2doi
001482639 035__ $$aSP(OCoLC)1405967224
001482639 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLCO$$dOCLCF
001482639 049__ $$aISEA
001482639 050_4 $$aQA270$$b.I58 2019
001482639 08204 $$a519.5/7$$223/eng/20231025
001482639 08204 $$a519.5$$223/eng/20231025
001482639 1112_ $$aInternational Workshop on Simulation and Statistics$$n(10th :$$d2019 :$$cSalzburg, Austria).
001482639 24510 $$aStatistical modeling and simulation for experimental design and machine learning applications :$$bselected contributions from SimStat 2019 and invited papers /$$cJürgen Pilz, Viatcheslav B. Melas, Arne Bathke, editors.
001482639 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2023]
001482639 300__ $$a1 online resource (x, 265 pages) :$$billustrations (black and white, and colour).
001482639 336__ $$atext$$btxt$$2rdacontent
001482639 337__ $$acomputer$$bc$$2rdamedia
001482639 338__ $$aonline resource$$bcr$$2rdacarrier
001482639 4901_ $$aContributions to statistics
001482639 5050_ $$aIntro -- 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
001482639 5058_ $$a2.3.2 Special Case: ANOVA for Large g -- 2.4 Discussion and Outlook -- References -- 3 Pollutant Dispersion Simulation by Means of a Stochastic Particle Model and a Dynamic Gaussian Plume Model -- 3.1 Introduction -- 3.2 Meteorological Monitoring Network -- 3.3 Wind Field Modeling -- 3.3.1 Mass Correction of the Wind Field -- 3.3.2 Plume Rise -- 3.4 Stochastic Particle Model -- 3.4.1 Deposition -- 3.4.2 Implementation -- 3.5 Dynamic Gaussian Plume Model -- 3.6 Implementation on the Server -- 3.7 A Real-World Example with Application to an Alpine Valley -- 3.8 Conclusions and Outlook -- References
001482639 5058_ $$a4 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
001482639 5058_ $$a5.6 Covering Properties of Two-Level Factorial Designs -- 5.6.1 Bounds on CRH(Xn) -- 5.6.2 Calculation of CRH(Xn) -- 5.6.2.1 Algorithmic Construction of a Lower Bound on CRH(Xn) -- 5.7 Greedy Constructions Based on Fractional Factorial Designs -- 5.7.1 Base Designs -- 5.7.2 Rescaled Designs -- 5.7.3 Projection Properties -- 5.8 Summary and Future Work -- Appendix -- References -- 6 A Study of L-Optimal Designs for the Two-Dimensional Exponential Model -- 6.1 Introduction -- 6.2 Equivalence Theorem for L-Optimal Designs -- 6.3 General Case -- 6.4 Excess and Saturated Designs -- References
001482639 506__ $$aAccess limited to authorized users.
001482639 520__ $$aThis volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications. The refereed contributions originate from the 10th International Workshop on Simulation and Statistics, SimStat 2019, which was held in Salzburg, Austria, September 26, 2019, and were either presented at the conference or developed afterwards, relating closely to the topics of the workshop. The book is intended for statisticians and Ph.D. students who seek current developments and applications in the field.
001482639 588__ $$aDescription based on print version record.
001482639 650_6 $$aPlan d'expérience$$xMéthodes statistiques$$vCongrès.
001482639 650_6 $$aApprentissage automatique$$xMéthodes statistiques$$vCongrès.
001482639 650_0 $$aExperimental design$$xStatistical methods$$vCongresses.$$0(DLC)sh 85046441
001482639 650_0 $$aMachine learning$$xStatistical methods$$vCongresses.$$vCongresses$$0(DLC)sh2008107143
001482639 655_0 $$aElectronic books.
001482639 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001482639 7001_ $$aPilz, Jürgen,$$d1951-$$eeditor.$$1https://isni.org/isni/0000000109358883
001482639 7001_ $$aMelas, V. B.$$q(Vi͡acheslav Borisovich),$$eeditor.$$1https://isni.org/isni/0000000383397244
001482639 7001_ $$aBathke, Arne,$$eeditor.
001482639 77608 $$iPrint version:$$aInternational Workshop on Simulation and Statistics (10th : 2019 : Salzburg, Austria), creator.$$tStatistical modeling and simulation for experimental design and machine learning applications.$$dCham : Springer, 2023$$z9783031400544$$w(OCoLC)1400933945
001482639 830_0 $$aContributions to statistics.
001482639 852__ $$bebk
001482639 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-40055-1$$zOnline Access$$91397441.1
001482639 909CO $$ooai:library.usi.edu:1482639$$pGLOBAL_SET
001482639 980__ $$aBIB
001482639 980__ $$aEBOOK
001482639 982__ $$aEbook
001482639 983__ $$aOnline
001482639 994__ $$a92$$bISE