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Proceedings of the 31St International Workshop on Statistical Modelling Proceedings of the 31st International Workshop on Statistical Modelling Volume II July 4{8, 2016 Rennes, France Jean-Fran¸coisDupuy, Julie Josse (editors) Proceedings of the 31st International Workshop on Statistical Modelling volume II, Rennes, July 4 { 8, 2016, Jean-Fran¸coisDupuy, Julie Josse (editors), Rennes, 2016. Editors: Jean-Fran¸coisDupuy, [email protected] Department of Mathematical Engineering Institut National des Sciences Appliqu´eesRennes 20 avenue des Buttes de Co¨esmes 35708 Rennes cedex 7, France Julie Josse, [email protected] Department of Applied Mathematics Agrocampus Ouest 65 rue de Saint-Brieuc CS 84215 35042 Rennes Cedex, France Scientific Programme Committee Avner Bar Hen • Paris Descartes University, France Francesco Bartolucci • Perugia University, Italy Dankmar B¨ohning • Southampton University, UK Jean-Fran¸coisDupuy • INSA Rennes, France Jochen Einbeck • Durham University, UK Marco Grzegorczyk • Groningen University, Netherlands Ardo van den Hout • University College London, UK Julie Josse • Agrocampus Ouest, France Benoit Liquet • University of Queensland, Australia Gerhard Tutz • Munich University, Germany Helga Wagner • Johannes Kepler University Linz, Austria Local Organizing Committee Pierrette Chagneau (INSA Rennes) • Jean-Fran¸coisDupuy (INSA Rennes, Chair) • Martine Fixot (INSA Rennes) • Sabrina Jonin (INSA Rennes) • Julie Josse (Agrocampus Ouest) • Nathalie Krell (Rennes 1 University) • James Ledoux (INSA Rennes) • Audrey Poterie (INSA Rennes) • Laurent Rouvi`ere(Rennes 2 University) • Myriam Vimond (ENSAI) • Contents Part III { Posters Pariya Behrouzi, Ernst C. Wit: Penalized Gaussian Copula Graphical Models for Detecting Epistatic Selection. 3 Paul Brown, Chaitanya Joshi, Stephen Joe, Nigel Mc- Carter: Spatio-temporal modelling of crime using low discrep- ancy sequences . 7 Alba Carballo, Mar´ıa Durban,´ Dae-Jin Lee, Paul Eilers: The memory of extrapolating P-splines . 11 Claudia Castro-Kuriss, V´ıctor Leiva: Modelling censored and uncensored data: comparing treatments with GOF techniques. 15 Linda Chaba, John Odhiambo, Bernard Omolo : Comparison of the SAM and a Bayesian method for differential gene expression analysis . 19 Fernanda De Bastiani, Mikis D. Stasinopoulos, Robert A. Rigby, Miguel Uribe-Opazo and Audrey H.M.A. Cys- neiros: Gaussian Markov Random Fields within GAMLSS . 25 Helton Graziadei de Carvalho, Luis Gustavo Esteves: A Bayesian Test for Marginal Homogeneity in Contingency Tables. 29 Idemauro Antonio Rodrigues de Lara, John Hinde, Ariane Cristina de Castro: Markov transition models for ordinal re- sponses applied to pig behavior . 33 Cristina Ehrmann-Bostan, Birgit Prodinger, Gerold Stucki: Describing and understanding functioning in Spinal Cord Injury - a graphical model approach . 37 Mouloud Elhafidi, Abdessamad Ouchen: Econometric detec- tion of speculative bubbles: application of the Markov regime switching model . 41 Nazia P. Gill, Letterio D'Arrigo, Francesco Savoca, An- gela Costa, Astrid Bonaccorsi, Ernst C. Wit and Michele Pennisi: Use of GAM for determining the efficacy of RIRS in kidney stone removal . 45 vi Contents Andreas Groll, Jasmin Abedieh, Manuel J. A. Eugster: Modeling Concentration in Data with an Exogenous Order. 49 Freddy Hernandez,´ Olga Usuga, Jorge I. Velez:´ Impact of misspecified random effects distribution on the variance compo- nent test in GLMM. 55 Ting Hsiang Lin, Min-Hsiao Tsai: Modeling data with truncated and inflated Poisson distribution. 59 Antonio Lopez-Qu´ ´ılez, Miriam Marco, Enrique Gracia, Marisol Lila: Bayesian joint spatial models for intimate partner violence and child maltreatment . 63 Wan Jing Low, Paul Wilson, Mike Thelwall: Variations of compound models . 67 Ana M. Mart´ınez-Rodr´ıguez, Antonio Conde-Sanchez,´ An- tonio J. Saez-Castillo:´ Zero inflated hyper-Poisson regression model.......................................................... 71 Ivana Mala:´ Modelling of the unemployment duration in the Czech Republic with the use of a finite mixture model . 75 Lu´ıs Meira-Machado: Presmoothed Landmark estimators of the transition probabilities. 79 Joanna Morais, Christine Thomas-Agnan, Michel Simioni: Modeling market-shares or compositional data: An application to the automobile market. 83 Rossella Murtas, Alexander Philip Dawid, Monica Musio: Probability of causation: Bounds and identification for partial and complete mediation analysis . 87 Josu Najera-Zuloaga, Dae-Jin Lee, Inmaculada Arostegui: Comparison of beta-binomial regression approaches to analyze health related quality of life data . 91 Luiz R. Nakamura, Robert A. Rigby, Dimitrios M. Stasinopoulos, Roseli A. Leandro, Cristian Villegas, Rodrigo R. Pescim: A new flexible distribution with unimodal and bimodal shapes . 95 Vicente Nu´nez-Ant~ on,´ Ainhoa Oguiza, Jorge Virto: Linear mixed models innovative methodological approaches in pseudo- panels . 99 Contents vii M. J. Olmo-Jimenez,´ J. Rodr´ıguez-Avi, V. Cueva-Lopez:´ CTP distribution versus other usual models for count data . 105 Gilberto A. Paula and Luis Hernando Vanegas: Semipara- metric log-symmetric regression models for censored data . 109 Anna Pidnebesna, Katerinaˇ Helisova,´ Jirˇ´ı Dvorˇak,´ Radka Lechnerova,´ Toma´ˇs Lechner: Statistical Modelling of Sub- missions to Municipalities in the Czech Republic . 113 Alu´ısio Pinheiro, Abdourrahmane Atto, Emmanuel Trouve:´ Wavelet statistical analysis in multitemporal SAR images . 117 Hildete P. Pinheiro, Rafael P. Maia, Eufrasio´ Lima Neto, Mariana R. Motta: Modelling the proportion of failed courses and GPA scores for engineering major students . 121 Xiaoyun Quan, James G. Booth: Double-saddlepoint approxima- tion for enumeration of tables for exact tests of Hardy-Weinberg proportions. 125 Wasseem Rumjaun, Naushad Mamodekhan: Constructing a Bi- variate Com-Poisson Model Using Copulas. 129 Gianluca Sottile, Vito M. R. Muggeo: Tuning parameter se- lection in LASSO regression . 133 Ines^ Sousa, Ana Borges, Luis Castro: Longitudinal models with informative time measurements . 137 Reto Stauffer, Nikolaus Umlauf, Jakob W. Messner, Georg J. Mayr, Achim Zeileis: Ensemble Post-Processing over Complex Terrain Using High-Resolution Anomalies . 141 Elizabeth Stojanovski: Bayesian Meta-Analysis to incorporate Uncertainty . 145 Daniel Tait, Nadine Geiger, Bruce J. Worton, Andrew Bell, Ian G. Main: Nonlinear modelling of experimental rock failure data . 151 Alicia Thiery,´ Franc¸ois Severac,´ Mickael¨ Schaeffer, Marie-Astrid Metten, Monica Musio, Erik-A. Sauleau: Methods for multicausality in epidemiology: application to sur- vival data of patients with end stage renal disease . 155 Tita Vanichbuncha, Martin S. Ridout, Diogo Ver´ıssimo: Partial Ranking Analysis . 159 viii Contents S. Zafar, I. L. Hudson, E. J. Beh, S. A. Hudson, A. D. Abell: A non-iterative approach to ordinal log-linear models: investiga- tion of log D in drug discovery. 163 Author Index ................................................. 167 Part III - Posters Penalized Gaussian Copula Graphical Models for Detecting Epistatic Selection Pariya Behrouzi1, Ernst C. Wit1 1 Johann Bernoulli Institute, University of Groningen, Netherlands E-mail for correspondence: [email protected] Abstract: The detection of high-dimensional epistatic selection is an impor- tant goal in population genetics. The reconstruction of the signatures of epistatic selections during inbreeding is challenging as multiple testing approaches are under-power. Here we develop an efficient method for reconstructing an under- lying network of genomic signatures of high-dimensional epistatic selection from multi-locus genotype data. The network reveals \aberrant" marker-marker asso- ciations that are due to epistatic selection. The estimation procedure relies on penalized Gaussian copula graphical models. Keywords: Epistasis; Gaussian copula; High-dimensional inference. 1 Introduction Recombinant Inbred Lines (RILs) study design is a popular tool for study- ing the genetic and environmental basis of complex traits. RILs typically are derived by crossing two inbred parents followed by repeated genera- tions to produce an offspring whose genome is a mosaic of its parental lines. Assuming genotype of parent 1 is labeled AA and that of parent 2 is labeled BB, the routine way of coding the genotype data is to use 0; 1; 2 to represent AA; AB; BB , respectively. The construction of RILsf is notg always straightforward:f lowg fertility, or even complete lethality, of lines during inbreeding is common. These genomic signatures are indicative of epistatic selection having acted on entire networks of interacting loci during inbreeding. The reconstruction of multi-loci interaction networks from RIL genotyping data require methods for detecting genomic signatures of high-dimensional epistatic selection. One commonly used approach is hypothesis testing. The This paper was published as a part of the proceedings of the 31st Inter- national Workshop on Statistical Modelling, INSA Rennes, 4{8 July 2016. The copyright remains with the author(s). Permission to reproduce or extract any parts of this abstract should be requested from the author(s). 4 Detect epistatic via a latent Gaussian Copula Graphical Mode drawback with such an approach is that hypothesis testing at the genome- scale is typically underpowered. Furthermore, theory shows that pair-wise tests are not, statistically speaking, consistent when two interacting loci are conditionally dependent on other loci in the network, and may therefore lead to an incorrect signatures. In order
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