Table Of ContentAnsgar Steland
Kwok-Leung Tsui Editors
Artificial
Intelligence,
Big Data and
Data Science
in Statistics
Challenges and Solutions in
Environmetrics, the Natural Sciences
and Technology
Artificial Intelligence, Big Data and Data Science
in Statistics
Ansgar Steland • Kwok-Leung Tsui
Editors
Artificial Intelligence,
Big Data and Data Science
in Statistics
Challenges and Solutions in Environmetrics,
the Natural Sciences and Technology
Editors
AnsgarSteland Kwok-LeungTsui
InstituteofStatisticsandAICenter GradoDepartmentofIndustrial
RWTHAachenUniversity andSystemsEngineering
Aachen,Germany VirginiaPolytechnicInstituteandState
University
Blacksburg,VA,USA
ISBN978-3-031-07154-6 ISBN978-3-031-07155-3 (eBook)
https://doi.org/10.1007/978-3-031-07155-3
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Preface
The change to data-centrism in many fields, the need to extract information and
knowledge from big data, and the increasing success of machine learning (ML)
andartificialintelligence(AI)havecreatedbothopportunitiesandchallengestothe
field of statistics. These developmentshave, to some extent, led to the creationof
datascience,partiallyregardedasanewdiscipline,relatedtostatisticsandcomputer
science.TheintersectionsamongML/AI,datascience,andstatisticsaremuchlarger
thanpeopleexpect,particularlyontheory,models,practicalmethods,andproblems
underinvestigation.Allcommunitiescanlearnalotfromeachother.
The impressive successes of ML and AI methods, especially deep learners
and convolutional networks, in many practical problems might seem to devalue
statistical approaches. Quite a few researchers as well as practitioners regard
machine learning as being more focused on problem solving and benchmarkdata
sets than statistics. But, on the other hand, ML solutions are often tailored to a
specific problemand thus can be difficult to generalizeand implementfor a wide
rangeofapplications.
Further,thereiswiderangeofproblemsrelatedtodataforwhichstatisticspro-
videsmoreappropriateorevenoptimalsolutionsand allowsspecific interpretable
models. Stochastic models often provide mathematical descriptions of physical
processesratherthanrelyingonblackboxes.Indeed,lackofmodelinterpretability,
potentialbias,causality,andstability,andwhyandwhendeeplearnersmayworkare
commonquestionsfortheMLapproaches.Statisticalthinkingandapproachesare
goodalternativesto rectifythese problems,in termsof boththeories, models,and
practicalmethods.A furtherissue where statistics is indispensable is the question
whetheragivendatasetsatisfiespropersamplingdesigns,asstudiedbystatistical
samplingtheory,andthesoundstatisticalpreprocessing,handling,andcleaningof
data.Both topicsare importantto evaluategivendata, to ensurehighdata quality,
and to clarify what can be learnt from a certain data set. On the other hand, the
flexibility of many ML and AI methods may yield superior results when reliable
first-classdatafromwell-selectedvariablesarenotavailableandonehastorelyon
noisyandsurrogatedata.
v
vi Preface
Focusing on environmentalscience, natural science, and technology,this book
contributes to the discussions of various issues and general interplay among
statistics, data science, machine learning, and artificial intelligence. The chapters
cover theoretical studies of machine learning methods, expositions of general
methodologies for sound statistical analyses of data, as well as novel approaches
for modeling and analyzing data in specific areas and problems. In terms of
applications, the chapters deal with data as arising in industrial quality control,
autonomousdriving,transportationand traffic, chip manufacturing,photovoltaics,
football,transmissionofinfectiousdiseases,Covid-19,andpublichealth.
The idea for this volume came from the meetings of the Section on Environ-
metrics, Natural Science and Technologyof Deutsche Statistische Gesellschaft of
the last few years, andmostauthorshave presentedresearchatthe annualconfer-
encesStatistischeWoche.Allchaptersofthisvolumehavebeenpeerreviewed,and
the editors are grateful to those colleagues who helped in the evaluation process
as anonymous reviewers. Nevertheless, the authors of each chapters are solely
responsiblefortheirwork.
Aachen,Germany AnsgarSteland
Blacksburg,VA,USA Kwok-LeungTsui
November2021
Contents
PartI MethodologiesandTheoreticalStudies
One-RoundCross-ValidationandUncertaintyDeterminationfor
RandomizedNeuralNetworkswithApplicationstoMobileSensors ...... 3
AnsgarStelandandBartE.Pieters
ScaleInvariantandRobustPatternIdentificationinUnivariate
Time Series,withApplicationtoGrowthTrendDetectionin
MusicStreamingData........................................................... 25
NerminaMumic,OliverLeodolter,AlexanderSchwaiger,
andPeterFilzmoser
Fine-TunedParallelPiecewiseSequentialConfidenceInterval
and Point EstimationStrategiesfor the Mean of a Normal
Population:BigDataContext................................................... 51
NitisMukhopadhyayandChenZhang
StatisticalLearningforChangePointandAnomalyDetection
inGraphs.......................................................................... 85
AnnaMalinovskaya,PhilippOtto,andTorbenPeters
OntheRobustnessofKernel-BasedPairwiseLearning ..................... 111
PatrickGenslerandAndreasChristmann
GlobalSensitivityAnalysisfortheInterpretationofMachine
LearningAlgorithms............................................................. 155
SonjaKuhntandArkadiusKalka
ImprovingGaussianProcessEmulatorswithBoundaryInformation..... 171
ZhaohuiLiandMatthiasHwaiYongTan
vii
viii Contents
PartII ChallengesandSolutionsinApplications
An Overview and GeneralFrameworkfor Spatiotemporal
ModelingandApplicationsinTransportationandPublicHealth.......... 195
LishuaiLi,Kwok-LeungTsui,andYangZhao
IntroductiontoWaferTomography:Likelihood-BasedPrediction
ofIntegrated-CircuitYield...................................................... 227
MichaelBaron,EmmanuelYashchin,andAsyaTakken
UncertaintyQuantificationBasedonBayesianNeuralNetworks
forPredictiveQuality............................................................ 253
SimonCramer,MeikeHuber,andRobertH.Schmitt
TwoStatisticalDegradationModelsofBatteriesUnderDifferent
OperatingConditions............................................................ 269
Jin-ZhenKongandDongWang
DetectingDiamondBreakoutsofDiamondImpregnatedTools
forCoreDrillingofConcretebyForceMeasurements ...................... 283
Christine H. Müller, Hendrik Dohme, Dennis Malcherczyk, Dirk
Biermann,andWolfgangTillmann
Visualising Complex Data Within a Data Science Loop: A
Spatio-TemporalExamplefromFootball ..................................... 301
LeoN.Geppert,KatjaIckstadt,FabianKarl,JonasMünch,andMichael
Steinbrecher
Application of the Singular Spectrum Analysis on
Electroluminescence Images of Thin-Film Photovoltaic
Modules ........................................................................... 321
EvgeniiSovetkinandBartE.Pieters
TheImpactoftheLockdownRestrictionsonAirQualityDuring
COVID-19PandemicinLombardy,Italy ..................................... 343
PaoloMaranzanoandAlessandroFassó
AuthorIndex...................................................................... 375
Part I
Methodologies and Theoretical Studies
One-Round Cross-Validation
and Uncertainty Determination
for Randomized Neural Networks
with Applications to Mobile Sensors
AnsgarStelandandBartE.Pieters
Abstract Randomized artificial neural networks such as extreme learning
machinesprovidean attractive and efficientmethodfor supervisedlearning under
limitedcomputingresourcesandforgreenmachinelearning.Thisespeciallyapplies
whenequippingmobiledevices(sensors)withweakartificialintelligence.Results
arediscussedaboutsupervisedlearningwithsuchnetworksandregressionmethods
in terms of consistency and bounds for the generalization and prediction error.
Especially,somerecentresultsarereviewedaddressinglearningwithdatasampled
bymovingsensorsleadingtonon-stationaryanddependentsamples.Asrandomized
networks lead to random out-of-sampleperformancemeasures, we study a cross-
validation approach to handle the randomness and make use of it to improve
out-of-sample performance. Additionally, a computationally efficient approach
to determine the resulting uncertainty in terms of a confidence interval for the
mean out-of-sample prediction error is discussed based on two-stage estimation.
The approach is applied to a prediction problem arising in vehicle integrated
photovoltaics.
Keywords Cross-validation · Extremelearning · Modelcomparison · Neural
network · Photovoltaics · Uncertaintyinterval
1 Introduction
Artificialneuralnetworksarean attractiveclassofmodelsforsupervisedlearning
tasks arising in data science such as nonlinearregressionand predictiveanalytics.
There is a growing interest which is mainly driven by the developmentof highly
A.Steland((cid:2))
InstituteofStatisticsandAICenter,RWTHAachenUniversity,Aachen,Germany
e-mail:[email protected]
B.E.Pieters
ForschungszentrumJülich,InstitutfürEnergie-undKlimaforschung,Jülich,Germany
e-mail:[email protected]
©TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2022 3
A.Steland,K.-L.Tsui(eds.),ArtificialIntelligence,BigDataandDataScience
inStatistics,https://doi.org/10.1007/978-3-031-07155-3_1
