Table Of ContentSpringer Proceedings in Mathematics & Statistics
Raffaele Argiento
Ettore Lanzarone
Isadora Antoniano Villalobos
Alessandra Mattei E ditors
Bayesian
Statistics in
Action
BAYSM 2016, Florence, Italy, June 19–21
Springer Proceedings in Mathematics & Statistics
Volume 194
Springer Proceedings in Mathematics & Statistics
This book series features volumes composed of selected contributions from
workshops and conferences in all areas of current research in mathematics and
statistics, including operation research and optimization. In addition to an overall
evaluation of the interest, scientific quality, and timeliness of each proposal at the
hands of the publisher, individual contributions are all refereed to the high quality
standards of leading journals in the field. Thus, this series provides the research
community with well-edited, authoritative reports on developments in the most
exciting areas of mathematical and statistical research today.
More information about this series at http://www.springer.com/series/10533
Raffaele Argiento Ettore Lanzarone
(cid:129)
Isadora Antoniano Villalobos
Alessandra Mattei
Editors
Bayesian Statistics in Action
–
BAYSM 2016, Florence, Italy, June 19 21
123
Editors
Raffaele Argiento IsadoraAntoniano Villalobos
ESOMASDepartment Department ofDecision Sciences
University of Torino Bocconi University
Turin Milan
Italy Italy
and Alessandra Mattei
Department ofStatistica, Informatica,
Collegio CarloAlberto Applicazioni “G.Parenti”
Moncalieri (TO) University of Florence
Italy Florence
Italy
Ettore Lanzarone
CNR–IMATI
Milan
Italy
ISSN 2194-1009 ISSN 2194-1017 (electronic)
SpringerProceedings in Mathematics& Statistics
ISBN978-3-319-54083-2 ISBN978-3-319-54084-9 (eBook)
DOI 10.1007/978-3-319-54084-9
LibraryofCongressControlNumber:2017933863
MathematicsSubjectClassification(2010): 62F15,97K80
©SpringerInternationalPublishingAG2017
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar
methodologynowknownorhereafterdeveloped.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom
therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor
for any errors or omissions that may have been made. The publisher remains neutral with regard to
jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations.
Printedonacid-freepaper
ThisSpringerimprintispublishedbySpringerNature
TheregisteredcompanyisSpringerInternationalPublishingAG
Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland
Preface
Thisvolumeincludesacollectionofpeer-reviewedcontributionsfromamongthose
presented at the third Bayesian Young Statisticians Meeting (BAYSM 2016).
TheconferencewasorganizedbytheIstitutodiMatematicaApplicataeTecnologie
Informatiche “E. Magenes” (IMATI) of the National Research Council of Italy
(CNR), in collaboration with the Dipartimento di Statistica, Informatica,
Applicazioni“G.Parenti”,UniversityofFlorence,andtheDepartmentofDecision
Sciences, Bocconi University. The conference was held in Florence from 19 to 21
June 2016.
TheBayesianapproachtostatisticsisbecomingincreasinglypopularwithinthe
scientific community. Over the years, various problems involving complex data
(e.g., big data) and posing challenging methodological issues have stimulated the
scientific community to develop and apply novel Bayesian statistical methods.
BAYSM represents an opportunity for MS and Ph.D. students, post-docs, and
young researchers interested in Bayesian statistics to connect with their scientific
community at the beginning of their careers. The goal of BAYSM is to stimulate
collaborationsandencouragediscussionsbothwithcolleaguesatthesamelevelof
experienceandwithmoreseniorresearchers,inordertopromoteresearchinawide
spectrum offields where Bayesian methods can be employed.
ThescientificprogramofBAYSM2016includedcontributionsthatdevelopand
apply Bayesian methods in a variety of fields, ranging from the traditional (e.g.,
biostatistics and reliability) to the most innovative (e.g., big data and networks).
BAYSM 2016 included four plenary sessions and a keynote session as well as
contributed talk sessions and a poster session. The plenary sessions offered four
brilliant lectures by Fabrizia Mealli (University of Florence, Italy), Peter Müller
(The University of Texas at Austin, TX, USA), Steve Scott (Google Inc., CA,
USA), and Marina Vannucci (Rice University, TX, USA). The exciting keynote
speech on the foundations of Bayesian statistics was delivered by Alessandra
Guglielmi (Politecnico di Milano, Italy). Each standard session was chaired by a
senior discussant, who provided helpful feedback to the young speakers on their
currentactivity,encouragingthemtopursuetheirresearch.Networking amongthe
young participants was supported by vivid and inspirational discussions.
v
vi Preface
Three prizes were assigned: best methodological talk, best talk on applications,
and best poster.
We acknowledge all participants who made BAYSM 2016 an outstanding sci-
entificeventandanenjoyableexperience.Wethankallofthespeakers,youngand
senior. We express our gratitude to the discussants (Daniela Cocchi, Emanuela
Dreassi,BruneroLiseo,AntonioPievatolo,andFabrizioRuggeri)fortheirvaluable
work. Special acknowledgments are also owed to Ilaria Bianchini (Politecnico di
Milano, Italy) and Michela Tizzani (Promoest S.r.l., Italy) for their support in
organizing the conference. Finally, we give credit to our sponsors for their out-
standing support: Google Inc., Comune di Firenze, International Society for
Bayesian Analysis (ISBA), and Società Italiana di Statistica (SIS).
Hosting this meeting was an exciting and rewarding experience for the orga-
nizers. We expect that future BAYSM conferences will continue to have the same
success as the first three editions, inspiring the new generations of Bayesian
statisticians. Stay tuned at www.baysm.org!
ThisvolumeincludescontributionsfromyoungerBayesianstatisticiansinwhich
theoretical aspects and application issues, often considered separately, are blended
togethertosuccessfullytacklecomplexproblems.Itisstructuredintwoparts.The
first, Theory and Methods, is mainly devoted to mathematical statistics, model
building, and methodological works. The second, Applications and Case Studies,
deals with applications of complex methods to real-world problems and data.
Turin, Italy Raffaele Argiento
Milan, Italy Ettore Lanzarone
Milan, Italy Isadora Antoniano Villalobos
Florence, Italy Alessandra Mattei
Contents
Part I Theory and Methods
Sequential Monte Carlo Methods in Random Intercept Models
for Longitudinal Data... .... ..... .... .... .... .... .... ..... .... 3
Danilo Alvares, Carmen Armero, Anabel Forte and Nicolas Chopin
On the Truncation Error of a Superposed Gamma Process . ..... .... 11
Julyan Arbel and Igor Prünster
On the Study of Two Models for Integer-Valued
High-Frequency Data ... .... ..... .... .... .... .... .... ..... .... 21
Andrea Cremaschi and Jim E. Griffin
Identification and Estimation of Principal Causal Effects
in Randomized Experiments with Treatment Switching. .... ..... .... 31
Emanuele Gramuglia
A Bayesian Joint Dispersion Model with Flexible Links .... ..... .... 39
Rui Martins
Local Posterior Concentration Rate for Multilevel Sparse
Sequences. .... .... .... .... ..... .... .... .... .... .... ..... .... 51
Eduard Belitser and Nurzhan Nurushev
Likelihood Tempering in Dynamic Model Averaging... .... ..... .... 67
Jan Reichl and Kamil Dedecius
Localization in High-Dimensional Monte Carlo Filtering.... ..... .... 79
Sylvain Robert and Hans R. Künsch
Linear Inverse Problem with Range Prior on Correlations
and Its Variational Bayes Inference. .... .... .... .... .... ..... .... 91
Ondřej Tichý and Václav Šmídl
vii
viii Contents
Part II Applications and Case Studies
Bayesian Hierarchical Model for Assessment of Climate
Model Biases.. .... .... .... ..... .... .... .... .... .... ..... .... 105
Maeregu Woldeyes Arisido, Carlo Gaetan, Davide Zanchettin
and Angelo Rubino
An Application of Bayesian Seemingly Unrelated Regression
Models with Flexible Tails ... ..... .... .... .... .... .... ..... .... 115
Charles Au and S.T. Boris Choy
Bayesian Inference of Stochastic Pursuit Models from Basketball
Tracking Data. .... .... .... ..... .... .... .... .... .... ..... .... 127
Harish S. Bhat, R.W.M.A. Madushani and Shagun Rawat
Identification of Patient-Specific Parameters in a Kinetic Model
of Fluid and Mass Transfer During Dialysis.. .... .... .... ..... .... 139
Camilla Bianchi, Ettore Lanzarone, Giustina Casagrande
and Maria Laura Costantino
A Bayesian Nonparametric Approach to Ecological Risk
Assessment.... .... .... .... ..... .... .... .... .... .... ..... .... 151
Guillaume Kon Kam King, Julyan Arbel and Igor Prünster
Approximate Bayesian Computation Methods in the Identification
of Atmospheric Contamination Sources for DAPPLE Experiment. .... 161
Piotr Kopka, Anna Wawrzynczak and Mieczyslaw Borysiewicz
Bayesian Survival Analysis to Model Plant Resistance
and Tolerance to Virus Diseases ... .... .... .... .... .... ..... .... 173
Elena Lázaro, Carmen Armero and Luis Rubio
Randomization Inference and Bayesian Inference in Regression
Discontinuity Designs: An Application to Italian University
Grants ... .... .... .... .... ..... .... .... .... .... .... ..... .... 183
Federica Licari
Bayesian Methods for Microsimulation Models ... .... .... ..... .... 193
Consuelo R. Nava, Cinzia Carota and Ugo Colombino
A Bayesian Model for Describing and Predicting the Stochastic
Demand of Emergency Calls . ..... .... .... .... .... .... ..... .... 203
Vittorio Nicoletta, Ettore Lanzarone, Alessandra Guglielmi,
Valérie Bélanger and Angel Ruiz
Flexible Parallel Split-Merge MCMC for the HDP. .... .... ..... .... 213
Debora Parisi and Stefania Perego
Contents ix
Bayesian Inference for Continuous Time Animal Movement
Based on Steps and Turns ... ..... .... .... .... .... .... ..... .... 223
Alison Parton, Paul G. Blackwell and Anna Skarin
Explaining the Lethality of Boko Haram’s Terrorist Attacks
in Nigeria, 2009–2014: A Hierarchical Bayesian Approach.. ..... .... 231
André Python, Janine Illian, Charlotte Jones-Todd
and Marta Blangiardo
Optimizing Movement of Cooperating Pedestrians by Exploiting
Floor-Field Model and Markov Decision Process.. .... .... ..... .... 241
Vladimíra Sečkárová and Pavel Hrabák
