Table Of ContentICSA Book Series in Statistics
Series Editors: Jiahua Chen · Ding-Geng (Din) Chen
Ding-Geng (Din) Chen
Jiahua Chen
Xuewen Lu
Grace Y. Yi
Hao Yu Editors
Advanced
Statistical
Methods in
Data Science
ICSA Book Series in Statistics
Serieseditors
JiahuaChen
DepartmentofStatistics
UniversityofBritishColumbia
Vancouver
Canada
Ding-Geng(Din)Chen
UniversityofNorthCarolina
ChapelHill,NC,USA
Moreinformationaboutthisseriesathttp://www.springer.com/series/13402
Ding-Geng (Din) Chen • Jiahua Chen (cid:129)
Xuewen Lu (cid:129) Grace Y. Yi (cid:129) Hao Yu
Editors
Advanced Statistical
Methods in Data Science
123
Editors
Ding-Geng(Din)Chen JiahuaChen
SchoolofSocialWork DepartmentofStatistics
UniversityofNorthCarolinaatChapelHill UniversityofBritishColumbia
ChapelHill,NC,USA Vancouver,BC,Canada
DepartmentofBiostatistics GraceY.Yi
GillingsSchoolofGlobalPublicHealth DepartmentofStatisticsandActuarial
UniversityofNorthCarolinaatChapelHill Science
ChapelHill,NC,USA UniversityofWaterloo
Waterloo,ON,Canada
XuewenLu HaoYu
DepartmentofMathematicsandStatistics DepartmentofStatisticsandActuarial
UniversityofCalgary Science
Calgary,AB,Canada WesternUniversity
London,ON,Canada
ISSN2199-0980 ISSN2199-0999 (electronic)
ICSABookSeriesinStatistics
ISBN978-981-10-2593-8 ISBN978-981-10-2594-5 (eBook)
DOI10.1007/978-981-10-2594-5
LibraryofCongressControlNumber:2016959593
©SpringerScience+BusinessMediaSingapore2016
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To myparentsandparents-in-law,who value
highereducationandhardwork; tomywife
Ke, forher love,support,andpatience;and
tomysonJohn D.Chen andmydaughter
JennyK. Chen fortheirloveandsupport.
Ding-Geng(Din)Chen, PhD
To mywife, mydaughterAmy, andmyson
Andy,whoseadmiringconversations
transformedintolastingenthusiasmformy
researchactivities.
JiahuaChen, PhD
To mywifeXiaobo,mydaughterSophia,and
mysonSamuel,fortheirsupportand
understanding.
XuewenLu,PhD
To myfamily,WenqingHe, MorganHe, and
JoyHe, for beingmyinspirationandoffering
everlastingsupport.
Grace Y. Yi,PhD
Preface
Thisbookisacompilationofinvitedpresentationsandlecturesthatwerepresented
at the Second Symposium of the International Chinese Statistical Association–
Canada Chapter (ICSA–CANADA) held at the University of Calgary, Canada,
August 4–6, 2015 (http://www.ucalgary.ca/icsa-canadachapter2015).The Sympo-
siumwasorganizedaroundthetheme“EmbracingChallengesandOpportunitiesof
StatisticsandDataScienceintheModernWorld”withathreefoldgoal:topromote
advancedstatistical methodsin big data sciences, to create an opportunityfor the
exchangeideasamongresearchersinstatisticsanddatascience,andtoembracethe
opportunitiesinherent in the challenges of using statistics and data science in the
modernworld.
The Symposium encompassed diverse topics in advanced statistical analysis
in big data sciences, including methods for administrative data analysis, survival
data analysis, missing data analysis, high-dimensional and genetic data analysis,
and longitudinal and functional data analysis; design and analysis of studies
with response-dependentandmultiphasedesigns;time series androbuststatistics;
and statistical inference based on likelihood, empirical likelihood, and estimating
functions. This book compiles 12 research articles generated from Symposium
presentations.
Our aim in creating this bookwas to providea venue for timely dissemination
of the research presented during the Symposium to promote further research and
collaborative work in advanced statistics. In the era of big data, this collection
of innovativeresearch not only has high potential to have a substantial impact on
thedevelopmentofadvancedstatisticalmodelsacrossawidespectrumofbigdata
sciencesbutalso hasgreatpromiseforfosteringmoreresearchandcollaborations
addressing the ever-changing challenges and opportunities of statistics and data
science.Theauthorshavemadetheirdataandcomputerprogramspubliclyavailable
so that readers can replicate the model development and data analysis presented
in each chapter, enabling them to readily apply these new methods in their own
research.
vii
viii Preface
The 12 chaptersare organizedinto three sections. Part I includesfourchapters
that present and discuss data analyses based on latent variable models in data
sciences.PartIIcomprisesfourchaptersthatshareacommonfocusonlifetimedata
analyses.PartIIIiscomposedoffourchaptersthataddressapplieddataanalysesin
bigdatasciences.
PartIDataAnalysisBasedonLatentorDependentVariableModels(Chaps.1,
2,3,and4)
Chapter 1 presents a weighted multiple testing procedure commonly used and
known in clinical trials. Given this wide use, many researchers have proposed
methodsformakingmultipletestingadjustmentstocontrolfamily-wiseerrorrates
whileaccountingforthelogicalrelationsamongthenullhypotheses.However,most
ofthosemethodsnotonlydisregardthecorrelationamongtheendpointswithinthe
samefamilybutalsoassumethehypothesesassociatedwitheachfamilyareequally
weighted. Authors Enas Ghulam, Kesheng Wang, and Changchun Xie report on
their work in which they proposed and tested a gatekeeping procedure based on
Xie’sweightedmultipletestingcorrectionforcorrelatedtests.Theproposedmethod
isillustratedwithanexampletoclearlydemonstratehowitcanbeusedincomplex
clinicaltrials.
In Chap.2, Abbas Khalili, Jiahua Chen, and David A. Stephens consider
the regime-switching Gaussian autoregressive model as an effective platform
for analyzing financial and economic time series. The authors first explain the
heterogeneousbehaviorinvolatilityovertimeandmultimodalityoftheconditional
or marginaldistributionsand then propose a computationallymore efficient regu-
larization method for simultaneousautoregressive-orderand parameter estimation
whenthenumberofautoregressiveregimesispredetermined.Theauthorsprovide
ahelpfuldemonstrationbyapplyingthismethodtoanalysisofthegrowthoftheUS
grossdomesticproductandUSunemploymentratedata.
Chapter 3 deals with a practical problem of healthcare use for understanding
the risk factors associated with the length of hospital stay. In this chapter, Cindy
XinFengandLonghaiLidevelophurdleandzero-inflatedmodelstoaccommodate
both the excess zeros and skewness of data with various configurationsof spatial
random effects. In addition, these models allow for the analysis of the nonlinear
effectofseasonalityandotherfixedeffectcovariates.Thisresearchdrawsattention
to considerable drawbacks regarding model misspecifications. The modeling and
inference presented by Feng and Li use the fully Bayesian approach via Markov
ChainMonteCarlo(MCMC)simulationtechniques.
Chapter 4 discusses emerging issues in the era of precision medicine and the
developmentofmulti-agentcombinationtherapyorpolytherapy.Priorresearchhas
established that, as compared with conventional single-agent therapy (monother-
apy),polytherapyoftenleadstoahigh-dimensionaldosesearchingspace,especially
when a treatment combines three or more drugs. To overcome the burden of
calibration of multiple design parameters, Ruitao Lin and Guosheng Yin propose
arobustoptimalinterval(ROI)designtolocatethemaximumtolerateddose(MTD)
in Phase I clinical trials. The optimal interval is determined by minimizing the
probability of incorrect decisions under the Bayesian paradigm. To tackle high-
Preface ix
dimensional drug combinations, the authors develop a random-walk ROI design
to identify the MTD combination in the multi-agent dose space. The authors of
thischapterdesignedextensivesimulationstudiestodemonstratethefinite-sample
performanceoftheproposedmethods.
PartIILifetimeDataAnalysis(Chaps.5,6,7,and8)
In Chap.5, Longlong Huang, Karen Kopciuk, and Xuewen Lu present a new
method for group selection in an accelerated failure time (AFT) model with a
groupbridgepenalty.Thismethodiscapableofsimultaneouslycarryingoutfeature
selection at the group and within-group individual variable levels. The authors
conducteda series of simulation studies to demonstratethe capacity of this group
bridge approachto identify the correctgroup and correct individualvariable even
with high censoring rates. Real data analysis illustrates the application of the
proposedmethodtoscientificproblems.
Chapter 6 considers issues around Case I interval censored data, also known
as current status data, commonly encountered in areas such as demography,
economics,epidemiology,andmedicalscience.Inthischapter,PoonehPordeliand
XuewenLufirstintroduceapartiallylinearsingle-indexproportionaloddsmodelto
analyzethesetypesofdataandthenproposeamethodforsimultaneoussievemax-
imumlikelihoodestimation.Theresultantestimatorofregressionparametervector
isasymptoticallynormal,and,undersomeregularityconditions,thisestimatorcan
achievethesemiparametricinformationbound.
Chapter 7 presents a framework for general empirical likelihood inference of
Type I censored multiple samples. Authors Song Cai and Jiahua Chen develop
an effective empirical likelihood ratio test and efficient methods for distribution
functionandquantileestimationforTypeIcensoredsamples.Thisnewlydeveloped
approach can achieve high efficiency without requiring risky model assumptions.
Themaximumempiricallikelihoodestimatorisasymptoticallynormal.Simulation
studiesshowthat,ascomparedto somesemiparametriccompetitors,theproposed
empiricallikelihoodratiotesthassuperiorpowerunderawiderangeofpopulation
distributionsettings.
Chapter 8 provides readers with an overview of recent developments in the
joint modeling of longitudinal quality of life (QoL) measurements and survival
timeforcancerpatientsthatpromisemoreefficientestimation.AuthorsHuiSong,
YingweiPeng,andDongshengTuthenproposesemiparametricestimationmethods
to estimate the parameters in these joint models and illustrate the applications of
these joint modeling procedures to analyze longitudinal QoL measurements and
recurrencetimesusingdatafromaclinicaltrialsampleofwomenwithearlybreast
cancer.
PartIIIAppliedDataAnalysis(Chaps.9,10,11,and12)
Chapter 9 presents an interesting discussion of a confidence weighting model
appliedtomultiple-choicetestscommonlyusedinundergraduatemathematicsand
statisticscourses.MichaelCaversandJosephLingdiscussanapproachtomultiple-
choice testing called the student-weightedmodel and report on findings based on
theimplementationofthismethodintwosectionsofafirst-yearcalculuscourseat
theUniversityofCalgary(2014and2015).
x Preface
Chapter 10 discusses parametric imputation in missing data analysis. Author
Peisong Han proposes to estimate and subtract the asymptotic bias to obtain
consistent estimators. Han demonstrates that the resulting estimator is consistent
if any of the missingness mechanismmodelsor the imputationmodelis correctly
specified.
Chapter 11 considersone of the basic and importantproblemsin statistics: the
estimationofthecenterofasymmetricdistribution.Inthischapter,authorsPengfei
Li and Zhaoyang Tian propose a new estimator by maximizing the smoothed
likelihood.LiandTian’ssimulationstudiesshowthat,ascomparedwiththeexisting
methods, their proposed estimator has much smaller mean square errors under
uniform distribution, t-distribution with one degree of freedom, and mixtures of
normaldistributionsonthemeanparameter.Additionally,theproposedestimatoris
comparabletotheexistingmethodsunderothersymmetricdistributions.
Chapter12presentstheworkofJingjiaChu,RegKulperger,andHaoYuinwhich
theyproposeanewclassofmultivariatetimeseriesmodels.Specifically,theauthors
propose a multivariate time series model with an additive GARCH-type structure
to capture the common risk among equities. The dynamic conditional covariance
betweenseriesisaggregatedbyacommonriskterm,whichiskeytocharacterizing
theconditionalcorrelation.
As a general note, the references for each chapter are included immediately
followingthe chaptertext. We have organizedthe chaptersas self-containedunits
soreaderscanmoreeasilyandreadilyrefertothecitedsourcesforeachchapter.
The editors are deeply gratefulto many organizationsand individualsfor their
supportoftheresearchandeffortsthathavegoneintothecreationofthiscollection
of impressive, innovative work. First, we would like to thank the authors of
each chapter for the contribution of their knowledge, time, and expertise to this
book as well as to the Second Symposium of the ICSA–CANADA. Second, our
sinceregratitudegoestothesponsorsoftheSymposiumfortheirfinancialsupport:
the Canadian Statistical Sciences Institute (CANSSI), the Pacific Institute for the
MathematicalSciences(PIMS),andtheDepartmentofMathematicsandStatistics,
University of Calgary; without their support, this book would not have become a
reality. We also owe big thanks to the volunteers and the staff of the University
of Calgary for their assistance at the Symposium. We express our sincere thanks
to the Symposium organizers: Gemai Chen, PhD, University of Calgary; Jiahua
Chen, PhD, University of British Columbia; X. Joan Hu, PhD, Simon Fraser
University;WendyLou,PhD,UniversityofToronto;XuewenLu,PhD,University
of Calgary; Chao Qiu, PhD, University of Calgary; Bingrui (Cindy) Sun, PhD,
University of Calgary; Jingjing Wu, PhD, University of Calgary; Grace Y. Yi,
PhD,UniversityofWaterloo;andYingZhang,PhD,AcadiaUniversity.Theeditors
wishtoacknowledgetheprofessionalsupportofHannahQiu(Springer/ICSABook
Seriescoordinator)andWeiZhao(associateeditor)fromSpringerBeijingthatmade
publishingthisbookwithSpringerareality.
