Recent Studies on Risk Analysis and Statistical Modeling

392 Pages·2018·8.61 MB·English

by  Teresa A. Oliveira

Contributions to Statistics
Teresa A. Oliveira · Christos P. Kitsos 
Amílcar Oliveira · Luís Grilo Editors
Recent Studies
on Risk Analysis
and Statistical
Modeling
Contributions to Statistics
The series Contributions to Statistics contains publications in theoretical and
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Teresa A. Oliveira • Christos P. Kitsos •
Amílcar Oliveira • Luís Grilo
Editors
Recent Studies on Risk
Analysis and Statistical
Modeling
123
Editors
TeresaA.Oliveira ChristosP.Kitsos
UniversidadeAberta DepartmentofInformatics
Lisboa,Portugal TechnologicalEducationalInstituteof
Athens
Egaleo,Greece
AmílcarOliveira LuísGrilo
UniversidadeAberta UnidadeDeptdeMatematicaeFisica
Lisboa,Portugal InstitutoPolitécnicodeTomar
Tomar,Portugal
ISSN1431-1968
ContributionstoStatistics
ISBN978-3-319-76604-1 ISBN978-3-319-76605-8 (eBook)
https://doi.org/10.1007/978-3-319-76605-8
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Preface
Scientificprogressdependsongoodmethods,andinordertotrytoaccomplishthe
developmentsinourdays,iturgestoexploreanddevelopmethodologiesinvolving
risk analysis and statistical modeling.Trying to minimize or even avoid risks and
to have good ways to be preparedto futureresults based on real data observedin
thepastisinfactmandatory.Withrecentadvancesintheseareasfromtheoretical,
computational, and practical points of view, the problems analysis has become
morecomplex,andyetthereisa needforguidanceto getintothemoreadvanced
literature.Mostofthisliteraturecanbefoundinscientificjournalsandproceedings.
Besides some bookscover a few methodsverywell, most of them do notdo it in
acomprehensiveway,mainlytothepractitionersintheseareas.Fromthispointof
view,ourvolumedetachesthedifference.Thisbooktriestoovercomethatproblem
by covering an essential part of the quantitative approach in risk analysis, where
statisticalmodelsand/ormathematicalmethodsarelinkedwithsomephenomenon
underinvestigation.Alongthebook,applicationstorealdataareobservedinseveral
areas, like engineering, medicine, health sciences, education sciences, economy,
finances,andindustry.
Atthesametimemodelingissuesprovidethemethodologytogatheracompact
structure for the data. In the first stage of risk analysis, data were studied from
the decision theory point of view. However, nowadays data analysis is expanded
to medical and biologicalmodels, and moreover,it coverseconomical, industrial,
environmental, and management problems. Very general definition could be that:
risk analysis is the review of (estimation of) the risks associated with a particular
eventoraction,resultinginanotherone.Thatiswhyinprinciple,riskmanagement
istheprocessofplanning,organizing,leading,andcontrollingactivitiestominimize
the adverse effects of accidental losses on the organization, such as a firm or
an industrial unit. Similar is the definition of the environmental risk assessment
(ERA): it aims at assessing the effects of stressors, often chemicals, on the
local environment. But risk assessment is concerned with the determination of
quantitative or qualitative estimate of the associated risk related to a well-defined
situationandarecognizedthreat:thus,thehazardfunctionisanessential“tool”for
thethreatevaluation.
v
vi Preface
The common target for risk assessment studies, either for toxicology/medicine
or for biology/environmental,was the cancer risk assessment. Thus, interest was
focused on the design of experiments and mixtures of experiments at the early
stages,andlaterthestudyoftumorwasthroughthe“birth-death”stochasticprocess.
Here the “risk” was the cell to be transformed to a tumor! The experiments of
performing on rats were restricting from the “size of the experiment” for ethical
and economic reasons. But such studies of risk cannot be applied on economic
problems which need the estimation of the involved risk. Actuary mathematics is
anotherapproachtoreducetheriskforinsurancecompaniesandothers.
OurpreviousSpringervolumeinTheoryandPracticeofRiskAssessmentreflects
a first step to extend the applications of risk analysis, to obtain a broader area of
research, rather than the one centered on biostatistics, as an extension (political)
fromgametheory.
Needless to say, at the first stage of using risk methods, from a mathematical
point of view relative risk was really a simple index, but so useful (distance)
measure. We adopt this line of thought in this volume, thanks to Springer, and
we include more areas of applying risk theory. Thus, we believe that we cement
our point of view that risk analysis can be considered an independent branch of
statistics,tacklingareasofinterestsuchasmanagement,industry,andeconomics.
The problem of data is always at the first line of interest, not only if it exists
or not. We must recall that in cases where the data set is small, less than 30
observations,orforbigdatasets,weneedaspecialtreatmentofthedata.Insome
cases(duetocost!)onlyveryfewobservationscanbeobtained.Wemovedfromthe
“dataofstatus”thatisstatisticstoanalyzingdatasetsfromanumberofareas,when
weweredevelopingDataAnalysis(thankstoJohnTukey,whonamedthemethod)
and,nowdealingwithbigdatasetsandhigh-techcomputers,wearemovingtoData
Analytics(subsetofBusinessIntelligence).Butalwaysthesourceofdatawetryto
analyze is very essential and related to the risk we try to eliminate, minimize, or
estimate.
It was essential to create biological data sets. Molecular biology through
genomics, proteomics, and metabolomics increased our knowledge of biological
processes, and several databases are now accessible through the Internet. Similar
databases,notsoeasyaccessible,weredevelopedoncancer.Studiesonriskanalysis
were therefore based on more reliable data sets, as far as cancer was concerned.
Statisticalmodelingalsotacklesthedataanalysisproblem.Thatiswhythisbookis
dividedintotwoparts:
PartI.RiskMethodologiesandApplications
PartII.StatisticalModelingandRiskIssuesinSeveralAreas
The papers submitted to this volume were carefully reviewed by referees. The
selected paperswere placed appropriatelyand offerthe readersthe opportunityto
lookforanumberofdifferentapproachesandabroadrangeofareasofapplication.
Webelievethatthisbookwilloffersolutionstotheexistingproblems,willprovide
the appropriateframeworkand backgroundto real-life problems,and will covera
Preface vii
gapthatusuallyexists:somemoretimeisneededfornewtheoreticalresultstobe
publishedinonebook.
The book reflects contributionsfrom invited experts, providingthe reader with
acomprehensiveoverviewofthemainareasbyfosteringlinkswithseveralrelated
disciplines, exploring computational issues, and presenting some research future
trends. As this volume is multiauthor, multinational and covers different areas of
applications,itoffersachancetotheresearchersworkingindifferentareasofhaving
itasareferencebook.
Lisboa,Portugal TeresaA.Oliveira
Athens,Greece ChristosP.Kitsos
Lisboa,Portugal AmílcarOliveira
Tomar,Portugal LuísGrilo
Introduction
Doubtisthebeginning,nottheend,ofwisdom.
—GeorgeIles,1852–1942.
Infact,doubtraisesthenotionofhazard,promotesriskresearchandfostersnew
knowledge.This book tries to coveran essential part of the quantitativeapproach
inRiskAnalysis,wherestatisticalmodelsand/ormathematicalmethodsarelinked
withsomephenomenonunderinvestigation.ThegeneraltopicRISKisexploredin
order to understand, simulate, design and promote the analysis of real problems,
fostering new challenges in several areas, such as Engineering, Medicine, Health
Sciences,EducationSciences,Economy,FinancesandIndustry.
In an attempt to recognize the role that statistics and computation play in
risk analysis, the International Committee on Risk Analysis of the International
StatisticalInstitute,theISI-CRA,decidedtoselectaseriesofinterestingpapersin
ordertoattemptasthisbookchapters,consistingofsomeofthemostimportantand
current methodologies under the Risk topic. With this book we aim to reinforce
the bridge connecting theoretical topics and new methodologies to the practical
applications,fosteringadeepinsightamongthepractitionersofseveralareas.
Thebookispresentedintotwomainpartsbasedonthesubjectmattercovered:
PartIisdevotedtoRiskMethodologiesandApplications
PartIIisfocusedonStatisticalModelingandRiskIssuesinSeveralAreas
Part I:RiskMethodologies andApplications
The papers in Part I mainly cover Risk theoretical issues and methodologies,
with focus on applications in Health Sciences, Medicine, Economics, Finance,
EngineeringandonspecialissuesinthemainareasofMathematicsandStatistics.
ix
x Introduction
Thechaptersare organizedinto sectionsbasedon the primaryfocusof the papers
included.Somebrieflydescriptionofthetopicscoveredinthesesectionsfollows:
Section 1 The first section deals with Risk Analysis in Health Sciences and
Medicine.
Chapter“AssessmentofMaximumAPosterioriImageEstimationAlgorithmsfor
ReducedAcquisitionTimeMedicalPositronEmissionTomographyData”considers
a study to examine the effects of reduced radioactive dosage data collection on
positronemissionstomographyreconstructionreliability.Theefficiencyofvarious
reconstructionmethodsisalsoinvestigated.
Chapter “On Mixed Cancer Risk Assessment” consider both mammary cancer
and Wilms tumors, as two typical examples from oncology generating difficult
multicriterial decision problems. The authors fit mixture models to box-counting
fractal dimensions in order to better understand the variability, they explore the
effect of chemotherapyand present a discussion on the shape analysis for Wilms
tumors.
Section 2 The second section is devoted to Risk Analysis in Economics and
Financeapplications.
Chapter “Traditional Versus Alternative Risk Measures in Hedge Fund Invest-
mentEfficiency”dealswiththeHedgefundswhicharefinancialinstitutionsaiming
at generating absolute rates of return, that is at realizing profits regardless of the
market situation. Some measures of investment are explored and compared in a
particularperiod.
Chapter “Estimating the Extremal Coefficient: A Simulation Comparison of
Methods”.Taildependenceisanimportantissuetoevaluateriskandthemultivari-
ateextremevaluestheoryisthemostsuitabletodealwiththeextremaldependence.
Theextremalcoefficientmeasuresthedegreeofdependencebetweenthemarginals
ofmax-stabledistributions,anaturalclassofmodelsinthisframework.Theauthors
addresstheestimationoftheextremalcoefficientandanewestimatoriscompared
through simulation with existing methods. An illustration with real data is also
presented.
InChapter“OnaBusinessConfidenceIndexandItsDataAnalytics:AChilean
Case” a methodologyon a novelChilean business confidence index is presented,
which allows the description of some aspects of the market at a global level, as
wellasatindustrialandsectorlevelsofChileangreatbrands.Someissuesrelated
tobusinessintelligence,customerandbusinesssurveys,marketvariablesandofthe
mentionedconfidenceindexarediscussed.DescriptiveandInferentialresultsonthis
indexarepresented,aswell asresults onthe competitivenessof the Chilean great
brands.
InChapter“OntheApplicationofSampleCoefficientofVariationforManaging
Loan Portfolio Risks” the application of Sample Coefficient of Variation for
ManagingLoanPortfolioRiskispresented.Theauthorsobtainthelowerandupper
bounds for sample Coefficient Variation and study the possibility of using it for
measuring the risk concentration in a loan portfolio. The capital adequacy and
thesingleborrowerlimitareconsideredandsometheoreticalresultsareobtained.