Loglinear model selection and human mobility
Adrian Dobra,Abdolreza Mohammadi +1 more
TLDR
In this article, the authors developed a new algorithm for selecting graphical loglinear models that is suitable for analyzing hyper-sparse contingency tables, showing how multi-way contingency tables can be used to represent patterns of human mobility.Abstract:
Methods for selecting loglinear models were among Steve Fienberg’s research interests since the start of his long and fruitful career. After we dwell upon the string of papers focusing on loglinear models that can be partly attributed to Steve’s contributions and influential ideas, we develop a new algorithm for selecting graphical loglinear models that is suitable for analyzing hyper-sparse contingency tables. We show how multi-way contingency tables can be used to represent patterns of human mobility. We analyze a dataset of geolocated tweets from South Africa that comprises $46$ million latitude/longitude locations of $476\mbox{,}601$ Twitter users that is summarized as a contingency table with $214$ variables.read more
Citations
More filters
Graphical Models In Applied Multivariate Statistics
TL;DR: The graphical models in applied multivariate statistics is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can download it instantly.
Journal ArticleDOI
BDgraph: An R Package for Bayesian Structure Learning in Graphical Models
Reza Mohammadi,Ernst Wit +1 more
TL;DR: The R package BDgraph as discussed by the authors performs Bayesian structure learning for general undirected graphical models (decomposable and non-decompositionable) with continuous, discrete, and mixed variables.
Journal ArticleDOI
Bayesian Inference in Nonparanormal Graphical Models
TL;DR: A Bayesian approach is considered in the nonparanormal graphical model by putting priors on the unknown transformations through a random series based on B-splines where the coefficients are ordered to induce monotonicity and presents a posterior consistency result on the underlying transformation and the precision matrix.
Posted Content
Nonparametric graphical model for counts
Arkaprava Roy,David B. Dunson +1 more
TL;DR: A new class of pairwise Markov random field-type models for the joint distribution of a multivariate count vector is proposed by employing a novel type of transformation, to avoid restricting to non-negative dependence structures or inducing other restrictions through truncations.
Journal ArticleDOI
A method for statistical analysis of repeated residential movements to link human mobility and HIV acquisition
TL;DR: The method indicates that establishing a residence outside the rural study area is a strong predictor of HIV seroconversion in men (OR = 2.003, 95% CI = [1.718,2.332]), but not in women, and Residing inside the ruralStudy area in a single or in multiple locations is a less significant risk factor for HIV acquisition in both men and women compared to moving outside the Rural study area.
References
More filters
Journal ArticleDOI
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
TL;DR: In this article, the authors propose a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive.
Posted Content
Migration unemployment and development: a two-sector analysis.
Harris,Michael P. Todaro +1 more
TL;DR: In this paper, the authors examined why rural-urban labor migration persists and is even increasing in many developing nations despite the existence of positive marginal products in agriculture and significant levels of urban unemployment, and concluded that in the absence of wage flexibility an optimal policy would include both partial wage subsidies or direct government employment and measures to restrict free migration.
Book
Discrete multivariate analysis: theory and practice
TL;DR: Discrete Multivariate Analysis is a comprehensive text and general reference on the analysis of discrete multivariate data, particularly in the form of multidimensional tables, and contains a wealth of material on important topics.
Book
Graphical Models, Exponential Families, and Variational Inference
TL;DR: The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.