Conference Agenda
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Session Overview 
Session  
7B: Statistical modeling in R
Session Topics: Statistical models
 
Presentations  
9:15am  9:35am
TalkVideo ID: 207 / ses07B: 1 Regular Talk Topics: Statistical models Keywords: distributional regression, probabilistic forecasts, regression trees, random forests, graphical model assessment Probability Distribution Forecasts: Learning with Random Forests and Graphical Assessment ^{1}Department of Statistics, Faculty of Economics and Statistics, Universität Innsbruck; ^{2}Digital Science Center, Universität Innsbruck Forecasts in terms of entire probability distributions (often called "probabilistic forecasts" for short)  as opposed to predictions of only the mean of these distributions  are of prime importance in many different disciplines from natural sciences to social sciences and beyond. Hence, distributional regression models have been receiving increasing interest over the last decade. Here, we make contributions to two common challenges in distributional regression modeling: 1. Obtaining sufficiently flexible regression models that can capture complex patterns in a datadriven way. 2. Assessing the goodnessoffit of distributional models both insample and outofsample using visualizations that bring out potential deficits of these models. Regarding challenge 1, we present the R package "disttree" (Schlosser et al. 2021), that implements distributional trees and forests (Schlosser et al. 2019). These blend the recursive partitioning strategy of classical regression trees and random forests with distributional modeling. The resulting treebased models can capture nonlinear effects and interactions and automatically select the relevant covariates that determine differences in the underlying distributional parameters. For graphically evaluating the goodnessoffit of the resulting probabilistic forecasts (challenge 2), the R package "topmodels" (Zeileis et al. 2021) is introduced, providing extensible probabilistic forecasting infrastructure and corresponding diagnostic graphics such as QQ plots of randomized residuals, PIT (probability integral transform) histograms, reliability diagrams, and rootograms. In addition to distributional trees and forests other models can be plugged into these displays, which can be rendered both in base R graphics and "ggplot2" (Wickham 2016). Link to package or code repository.
https://RForge.Rproject.org/projects/partykit/pkg/disttree/, https://RForge.Rproject.org/projects/topmodels/pkg/topmodels/ 9:35am  9:55am
TalkLive ID: 178 / ses07B: 2 Regular Talk Topics: Statistical models spaMM: an R package to fit generalized, linear, and mixed models allowing for complex covariance structures ^{1}Univ. Montpellier, CNRS, Institut des Sciences de l'Evolution, Montpellier, France; ^{2}Leibniz Institute for Zoo and Wildlife Research, Berlin Introduced to make the fit of spatial Mixed Models accessible, the R package spaMM has grown a lot since its first CRAN release eight years ago. The package now offers the possibility to fit a variety of regression models, from simple linear models (LM) to generalised linear mixedeffects models (GLMM), including multivariateresponse models, and double hierarchical GLMMs (DHGLM) in which both the mean of a response and the residual variance can be modelled as a function of fixed and random effects. The package provides a diversity of response families beyond the standard ones, such as the (truncated or not) negative binomial, and the ConwayMaxwellPoisson, as well as nongaussian random effect families such as the inverse gaussian. Random effects can further be modelled using several autocorrelation functions for the consideration of spatial, temporal and other forms of dependence between observations (e.g. genetic pedigrees). spaMM handles this diversity of models through a simple formulabased interface akin to glm() or lme4::glmer(). Advanced users will nonetheless appreciate the possibility to fine tune many aspects of the fit (e.g. select among several likelihood approximations; set parameters to fixed values). The package also provides tailored methods for many generics, so that for instance anova() can be called to perform likelihood ratio tests by parametric bootstrap and that AIC() computes both the marginal and conditional AIC. The package is finally competitive in terms of computational speed, for both nonspatial, geostatistical, and autoregressive models Link to package or code repository.
https://cran.rproject.org/package=spaMM https://gitlab.mbb.univmontp2.fr/francois/spammref 9:55am  10:15am
withdrawn ID: 362 / ses07B: 3 Regular Talk Topics: Statistical models Keywords: big data Changed to Elevator Pitch: The onestep estimation procedure in R ^{1}Le Mans Université; ^{2}Université ParisDauphine In finitedimensional parameter estimation, the Le Cam onestep procedure is based on an initial guess estimator and a Fisher scoring step on the loglikelihood function. For an initial $\sqrt(n)$–consistent guess estimator, the onestep estimation procedure is asymptotically efficient. As soon as the guess estimator is in a closed form, it can also be computed faster than the maximum likelihood estimator. More recently, it has been shown that this procedure can be extended to an initial guess estimator with a slower speed of convergence. Based on this result, we propose in the OneStep package (available on CRAN) a procedure to compute the onestep estimator in any situation faster than the MLE for large datasets. MonteCarlo simulations are carried out for several examples of statistical experiments generated by i.i.d. observation samples (discrete and continuous probability distributions). Thereby, we exhibit the performance of Le Cam’s onestep estimation procedure in terms of efficiency and computational cost on observation samples of finite size. A real application and the future package developments will also be discussed. 10:15am  10:35am
TalkVideo ID: 206 / ses07B: 4 Regular Talk Topics: Statistical models Keywords: probabilistic graphical models The R Package stagedtrees for Structural Learning of Stratified Staged Trees ^{1}Università degli Studi di Genova, Dipartimento di Matematica, Italy; ^{2}IE University, School of Human Sciences and Technology, Spain; ^{3}Universitat de València, Image Processing Laboratory (IPL), Spain stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from categorical data. In the past twenty years there has been an explosion of the use of graphical models to represent the relationship among a vector of random variables and perform inference taking advantage of the underlying graphical representations. Bayesian networks are nowadays one of the most used graphical models, with applications to a wide array of domains and implementation in various software. However, they can only represent symmetric conditional independence statements which in practical applications may not be fully justified. Most often, the greater the number of levels of categorical variables involved, the more difficult it is for conditional independence to hold for all the variables’ levels. Therefore, models accommodating also asymmetric relations as contextspecific, partial and local independences have been developed. Staged trees are one such class. Staged tree modeling has proved its worth in many fields, as for instance cohort studies, causal analysis, casecontrol studies, Bayesian games and medical diagnosis. stagedtrees permits to estimate any type of nonsymmetric conditional independences from data via scorebased and clusteringbased algorithms. Various functionalities to provide inferential, visualization, descriptive and summary statistics tools for such models and about their graph structure are implemented. These functions help users in handling categorical experimental data and analyzing the learned models to untangle complex dependence structures. Link to package or code repository.
https://cran.rproject.org/package=stagedtrees; https://github.com/gherardovarando/stagedtrees 