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on1405967224 |
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OCoLC |
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20240909213021.0 |
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m o d |
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cr cnu---unuuu |
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231025s2023 sz a o 100 0 eng d |
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|d OCLCF
|d OCLCQ
|d OCLCO
|d WSU
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|d UKAHL
|d SFB
|d OCLCO
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|a 9783031400551
|q (electronic bk.)
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|a 3031400550
|q (electronic bk.)
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|z 9783031400544
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|z 3031400542
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|a 10.1007/978-3-031-40055-1
|2 doi
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|a (OCoLC)1405967224
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|a QA270
|b .I58 2019
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|a PBT
|2 bicssc
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|a MAT029000
|2 bisacsh
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|a HCDD
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|a International Workshop on Simulation and Statistics
|n (10th :
|d 2019 :
|c Salzburg, Austria).
|
| 245 |
1 |
0 |
|a Statistical modeling and simulation for experimental design and machine learning applications :
|b selected contributions from SimStat 2019 and invited papers /
|c Jürgen Pilz, Viatcheslav B. Melas, Arne Bathke, editors.
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c [2023]
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| 300 |
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|a 1 online resource (x, 265 pages) :
|b illustrations (black and white, and colour).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Contributions to statistics
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| 520 |
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|a This 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.
|
| 588 |
0 |
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|a Print version record.
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0 |
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|a 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
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| 505 |
8 |
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|a 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
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| 505 |
8 |
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|a 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
|
| 505 |
8 |
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|a 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
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| 650 |
|
0 |
|a Experimental design
|x Statistical methods
|v Congresses.
|
| 650 |
|
0 |
|a Machine learning
|x Statistical methods
|v Congresses.
|
| 650 |
|
7 |
|a Experimental design
|x Statistical methods
|2 fast
|
| 650 |
|
7 |
|a Machine learning
|x Statistical methods
|2 fast
|
| 650 |
|
7 |
|a Disseny d'experiments.
|2 thub
|
| 650 |
|
7 |
|a Aprenentatge automàtic.
|2 thub
|
| 650 |
|
7 |
|a Estadística matemàtica.
|2 thub
|
| 655 |
|
7 |
|a proceedings (reports)
|2 aat
|
| 655 |
|
7 |
|a Conference papers and proceedings
|2 fast
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| 655 |
|
7 |
|a Conference papers and proceedings.
|2 lcgft
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| 655 |
|
7 |
|a Actes de congrès.
|2 rvmgf
|
| 655 |
|
7 |
|a Llibres electrònics.
|2 thub
|
| 700 |
1 |
|
|a Pilz, Jürgen,
|d 1951-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PCjwkrD66CBYmgj6qCtWjQ3
|1 https://isni.org/isni/0000000109358883
|
| 700 |
1 |
|
|a Melas, V. B.
|q (Vi︠a︡cheslav Borisovich),
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PCjvdCDHdHdkfPx4BPCRjfq
|1 https://isni.org/isni/0000000383397244
|
| 700 |
1 |
|
|a Bathke, Arne,
|e editor.
|
| 776 |
0 |
8 |
|i Print version:
|a International Workshop on Simulation and Statistics (10th : 2019 : Salzburg, Austria), creator.
|t Statistical modeling and simulation for experimental design and machine learning applications.
|d Cham : Springer, 2023
|z 9783031400544
|w (OCoLC)1400933945
|
| 830 |
|
0 |
|a Contributions to statistics.
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-031-40055-1
|y Click for online access
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| 903 |
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|a SPRING-ALL2023
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| 994 |
|
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|a 92
|b HCD
|