03194nam 2200661 a 450 991045412850332120170815123012.097866118978571-281-89785-X1-84920-922-71-84860-758-X(CKB)1000000000579123(EBL)370503(OCoLC)437237577(SSID)ssj0000297519(PQKBManifestationID)11228019(PQKBTitleCode)TC0000297519(PQKBWorkID)10333775(PQKB)10352384(MiAaPQ)EBC370503(OCoLC)646770602(StDuBDS)EDZ0000018549(PPN)227908376(EXLCZ)99100000000057912320110110d2007 fy 0engur|n|---|||||txtccrGetting your PhD[electronic resource] a practical insider's guide /Harriet Churchill and Teela SandersLos Angeles, [Calif.] ;London SAGE20071 online resource (241 p.)Description based upon print version of record.1-4129-1993-2 1-4129-1994-0 Includes bibliographical references and index.Contents; Acknowledgements; Contributors; Introduction; Part I Negotiating the Research Process; 1 Motivations for Doing a PhD; 2 Formulating a Research Question; 3 Choosing and Changing Supervisor; 4 Managing the Ethics of Academia; 5 What to do With your Data; Part II Writing, Publishing and Networking; 6 Writing Up and Writer's Block; 7 Papers and Publishing; 8 Networking; 9 Missing the Deadline; 10 The Viva and Beyond; Part III Shifting Identities and Institutions; 11 Non-traditional Routes into the PhD; 12 Undertaking a PhD Part-time; 13 Combining Teaching and Doctoral Studies14 Reconciling the Research Role with the PersonalPart IV Relationships of Support; 15 What to Expect from Your Supervisor; 16 Enabling Research Environments; 17 Combining Family Commitments and Doctoral Studies; 18 Coping with Stress; Final Thoughts; References; Appendix 1 Example of an Informed Consent Form; Appendix 2 Example of an Information Sheet; Index'How to Get Your PhD' is an original study guide aimed at prospective and current postgraduate students, covering the process of accessing, undertaking and completing doctoral research in the social sciences and the humanities.Social sciencesStudy and teaching (Graduate)Social sciencesResearchHumanitiesStudy and teaching (Graduate)HumanitiesResearchElectronic books.Social sciencesStudy and teaching (Graduate)Social sciencesResearch.HumanitiesStudy and teaching (Graduate)HumanitiesResearch.378.170281Churchill Harriet1036997Sanders Teela932905StDuBDSStDuBDSBOOK9910454128503321Getting your PhD2457661UNINA10689nam 22008895 450 991075409290332120250602123742.09783031400551303140055010.1007/978-3-031-40055-1(MiAaPQ)EBC30799979(Au-PeEL)EBL30799979(DE-He213)978-3-031-40055-1(PPN)272914657(CKB)28528639000041(OCoLC)1405967224(EXLCZ)992852863900004120231019d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierStatistical Modeling and Simulation for Experimental Design and Machine Learning Applications Selected Contributions from SimStat 2019 and Invited Papers /edited by Jürgen Pilz, Viatcheslav B. Melas, Arne Bathke1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (265 pages)Contributions to Statistics,2628-8966Print version: Pilz, Jürgen Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications Cham : Springer International Publishing AG,c2023 9783031400544 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 -- 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 -- 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 -- 5.2 Greedy Coffee-House Design -- 5.3 Two-Level Fractional Factorial Designs -- 5.3.1 Half Fractions: m=1.5.3.2 Several Generators -- 5.3.2.1 Defining Relations -- 5.3.2.2 Resolution -- 5.3.2.3 Word Length Pattern -- 5.3.3 Minimum Size -- 5.4 Two-Level Factorial Designs and Error-Correcting Codes -- 5.4.1 Definitions and Properties -- 5.4.2 Examples -- 5.5 Maximin Distance Properties of Two-Level Factorial Designs -- 5.5.1 Neighbouring Pattern and Distant Site Pattern -- 5.5.2 Optimal Selection of Generators by Simulated Annealing -- 5.5.2.1 SA Algorithm for the Maximisation of ρH -- 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 -- 7 Testing for Randomized Block Single-Case Designsby Combined Permutation Tests with Multivariate Mixed Data -- 7.1 Introduction -- 7.2 Randomized Block Single-Case Designs and NPC -- 7.3 Simulation Study -- 7.4 A Real Case Study -- 7.5 Conclusions -- References -- 8 Adaptive Design Criteria Motivated by a Plug-In Percentile Estimator -- 8.1 Introduction -- 8.2 Problem Formulation and Background -- 8.2.1 Problem Formulation -- 8.2.2 Background -- 8.3 The Plug-In Estimator -- 8.4 Adaptive ``Plug-In'' Criteria -- 8.4.1 Monte Carlo Approximation -- 8.4.2 Monte Carlo Approximation Assuming Independency -- 8.4.3 Assuming Independency and Neglecting Uncertainty -- 8.4.4 Using SUR Design Criterion for Exceedance Probability -- 8.5 Numerical Implementation -- 8.6 Numerical Study.8.6.1 Comparison Study -- 8.6.2 Methodology -- 8.6.2.1 Case Studies -- 8.6.2.2 Performance Indicators -- 8.6.3 Numerical Results -- 8.6.3.1 Estimators Performance -- 8.6.3.2 Implementation -- 8.6.3.3 Criteria -- 8.7 Conclusions -- Appendix 1 -- Posterior Mean and Variance of f Under the Gaussian Process Assumption -- SUR Design Criteria for Exceedance Probability Estimation -- Appendix 2 -- References -- Part III Queueing and Inventory Analysis -- 9 On a Parametric Estimation for a Convolutionof Exponential Densities -- 9.1 Introduction -- 9.2 Convolution of the Exponential Densities -- 9.3 ML Estimation of the Parameters -- 9.4 Parameter's Estimation by the Moments' Method -- 9.5 Approximation of the Density -- 9.6 Experimental Study -- 9.7 Application to a Single Queueing System M/G/1/k -- 9.8 Conclusions -- References -- 10 Statistical Estimation with a Known Quantileand Its Application in a Modified ABC-XYZ Analysis -- 10.1 Introduction -- 10.2 Methods -- 10.2.1 Statistical Estimation with a Known Quantile -- 10.2.2 ABC-XYZ Analysis -- 10.3 ABC-XYZ Analysis Modified with a Known Quantile -- 10.4 Conclusions -- References -- Part IV Machine Learning and Applications -- 11 A Study of Design of Experiments and Machine Learning Methods to Improve Fault Detection Algorithms -- 11.1 Introduction -- 11.2 Design of Experiments and Machine Learning Modelling -- 11.3 Application to Fault Detection -- 11.3.1 Design of Experiments Step -- 11.3.2 Machine Learning Modelling Step -- 11.3.2.1 Refrigerant Undercharge: Fault Detection -- 11.3.2.2 Condenser Fouling: Fault Detection -- 11.4 Conclusions -- References -- 12 Microstructure Image Segmentation Using Patch-Based Clustering Approach -- 12.1 Introduction -- 12.2 Input Data -- 12.3 Previous Work -- 12.4 Grain Segmentation -- 12.4.1 Seeded Region Growing (SRG) -- 12.4.2 Image Denoising and Patch Determination.12.4.3 Feature Extraction -- 12.4.4 Patch Clustering -- 12.4.5 Implementation -- 12.5 Results -- 12.6 Conclusion and Outlook -- References -- 13 Clustering and Symptom Analysis in Binary Datawith Application -- 13.1 Introduction -- 13.2 The Symptom Analysis -- 13.2.1 The Symptom and Syndrome Definition -- 13.2.2 Impulse Vector and Super-symptoms -- 13.2.3 Prefigurations of Super-symptom -- 13.2.4 The Super-symptom Recovery by Vector β -- 13.2.5 Clustering in Dichotomous Space and Symptom Analysis -- 13.3 The Medical Application of the Clustering and Symptom Analysis in Binary Data -- 13.3.1 Dataset -- 13.3.2 Result and Discussion -- 13.4 Conclusion -- References -- 14 Big Data for Credit Risk Analysis: Efficient Machine Learning Models Using PySpark -- 14.1 Introduction -- 14.2 Data Processing -- 14.2.1 Data Treatment -- 14.2.2 Data Storage and Distribution -- 14.2.3 Munge Data -- 14.2.4 Creating New Measures -- 14.2.5 Missing Values Imputation and Outliers Treatment -- 14.2.6 One-Hot Code and Dummy Variables -- 14.2.7 Final Dataset -- 14.3 Method and Models -- 14.3.1 Method -- 14.3.2 Model Building -- 14.4 Results and Credit Scorecard Conversion -- 14.5 Conclusion -- Appendix 1 -- Appendix 2 -- References.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 2–6, 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.Contributions to Statistics,2628-8966StatisticsMathematical statisticsData processingExperimental designMachine learningStatisticsStochastic modelsStatistical Theory and MethodsStatistics and ComputingDesign of ExperimentsMachine LearningApplied StatisticsStochastic Modelling in StatisticsDisseny d'experimentsthubAprenentatge automàticthubEstadística matemàticathubLlibres electrònicsthubStatistics.Mathematical statisticsData processing.Experimental design.Machine learning.Statistics.Stochastic models.Statistical Theory and Methods.Statistics and Computing.Design of Experiments.Machine Learning.Applied Statistics.Stochastic Modelling in Statistics.Disseny d'experimentsAprenentatge automàticEstadística matemàtica519.57Pilz Jürgen337838Melas V. B(Vi︠a︡cheslav Borisovich)730339Bathke Arne1434339MiAaPQMiAaPQMiAaPQBOOK9910754092903321Statistical Modeling and Simulation for Experimental Design and Machine Learning Applications3587993UNINA