arXiv subjects

Juan Sosa

Publications and source records attributed to Juan Sosa.

45 records · Page 3Linked to original sources

arXiv2021

A Bayesian Approach for De-duplication in the Presence of Relational Data

In this paper, we study the impact of combining profile and network data in a de-duplication setting. We also assess the influence of a range of prior distributions on the linkage structure. Furthermore, we explore stochastic gradient Hamiltonian Monte Carlo methods as a faster alternative to obtain samples from the posterior distribution for network parameters. Our methodology is evaluated using the RLdata500 data, which is a popular dataset in the record linkage literature.

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arXiv2021

A Gentle Introduction to Bayesian Hierarchical Linear Regression Models

Considering the flexibility and applicability of Bayesian modeling, in this work we revise the main characteristics of two hierarchical models in a regression setting. We study the full probabilistic structure of the models along with the full conditional distribution for each model parameter. Under our hierarchical extensions, we allow the mean of the second stage of the model to have a linear dependency on a set of covariates. The Gibbs sampling algorithms used to obtain samples when fitting the models are fully described and derived. In addition, we consider a case study in which the plant size is characterized as a function of nitrogen soil concentration and a grouping factor (farm).

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arXiv2021

Bayesian spatial voting model to characterize the legislative behavior of the Colombian Senate 2010-2014

This paper applies Bayesian methodologies to characterize the legislative behavior of the Colombian Senate during the period 2010-2014. The analysis is carried out through the plenary roll call votes of this legislative chamber. In addition, parliamentary electoral behavior is operationalized by implementing the one-dimensional standard Bayesian ideal point estimator via the Markov chain Monte Carlo algorithms. The results contribute mainly to two points: political space dimensionality and the identification of pivot legislators. The pattern revealed by the estimated ideal points suggests a latent non-ideological trait (opposition - non-opposition) underlying the vote of deputies in the Senate. Thus, in addition to providing empirical evidence for a better understanding of legislative policy in Colombia during the period under analysis, this work also offers methodological and theoretical tools to guide the analysis of roll call vote data in contexts of unbalanced parliaments (as opposed to the U.S. parliament), taking the particular case of the Colombian Senate as a reference.

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arXiv2021

A Prior for Record Linkage Based on Allelic Partitions

In database management, record linkage aims to identify multiple records that correspond to the same individual. This task can be treated as a clustering problem, in which a latent entity is associated with one or more noisy database records. However, in contrast to traditional clustering applications, a large number of clusters with a few observations per cluster is expected in this context. In this paper, we introduce a new class of prior distributions based on allelic partitions that is specially suited for the small cluster setting of record linkage. Our approach makes it straightforward to introduce prior information about the cluster size distribution at different scales, and naturally enforces sublinear growth of the maximum cluster size -known as the microclustering property. We also introduce a set of novel microclustering conditions in order to impose further constraints on the cluster sizes a priori. We evaluate the performance of our proposed class of priors using simulated data and three official statistics data sets, and show that our models provide competitive results compared to state-of-the-art microclustering models in the record linkage literature. Moreover, we compare the performance of different loss functions for optimal point estimation of the partitions using decision-theoretical based approaches recently proposed in the literature.

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arXiv2021

Time-Varying Coefficient Model Estimation Through Radial Basis Functions

In this paper we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation and inference including a Frequentist approach using weighted least squares along with bootstrap methods, and a Bayesian approach through both Markov chain Monte Carlo and variational methods. We compare the estimation strategies mention above with each other, and our radial kernel functions proposal with an expansion based on regression spline, by means of an extensive simulation study considering multiples scenarios in terms of sample size, number of repeated measurements, and subject-specific correlation. Our experiments show that the capabilities of our proposal based on radial kernel functions are indeed comparable with or even better than those obtained from regression splines. We illustrate our methodology by analyzing data from two AIDS clinical studies.

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arXiv2021

A Latent Space Model for Multilayer Network Data

In this work, we propose a Bayesian statistical model to simultaneously characterize two or more social networks defined over a common set of actors. The key feature of the model is a hierarchical prior distribution that allows us to represent the entire system jointly, achieving a compromise between dependent and independent networks. Among others things, such a specification easily allows us to visualize multilayer network data in a low-dimensional Euclidean space, generate a weighted network that reflects the consensus affinity between actors, establish a measure of correlation between networks, assess cognitive judgements that subjects form about the relationships among actors, and perform clustering tasks at different social instances. Our model's capabilities are illustrated using several real-world data sets, taking into account different types of actors, sizes, and relations.

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arXiv2020

A Latent Space Model for Cognitive Social Structures Data

This paper introduces a novel approach for modeling a set of directed, binary networks in the context of cognitive social structures (CSSs) data. We adopt a relativist approach in which no assumption is made about the existence of an underlying true network. More specifically, we rely on a generalized linear model that incorporates a bilinear structure to model transitivity effects within networks, and a hierarchical specification on the bilinear effects to borrow information across networks. This is a spatial model, in which the perception of each individual about the strength of the relationships can be explained by the perceived position of the actors (themselves and others) on a latent social space. A key goal of the model is to provide a mechanism to formally assess the agreement between each actors' perception of their own social roles with that of the rest of the group. Our experiments with both real and simulated data show that the capabilities of our model are comparable with or, even superior to, other models for CSS data reported in the literature.

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arXiv2020

A Review of Latent Space Models for Social Networks

In this paper, we provide a review on both fundamentals of social networks and latent space modeling. The former discusses important topics related to network description, including vertex characteristics and network structure; whereas the latter articulates relevant advances in network modeling, including random graph models, generalized random graph models, exponential random graph models, and social space models. We discuss in detail several latent space models provided in literature, providing special attention to distance, class, and eigen models in the context of undirected, binary networks. In addition, we also examine empirically the behavior of these models in terms of prediction and goodness-of-fit using more than twenty popular datasets of the network literature.

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arXiv2018

A Record Linkage Model Incorporating Relational Data

In this paper we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different accounts across networks. In particular, we develop a latent model that allow us to jointly characterize the network and linkage structures relying in both relational and profile data. In contrast to other existing approaches in the machine learning literature, our Bayesian implementation naturally provides uncertainty quantification via posterior probabilities for the linkage structure itself or any function of it. Our findings clearly suggest that our methodology can produce accurate point estimates of the linkage structure even in the absence of profile information, and also, in an identity resolution setting, our results confirm that including relational data into the matching process improves the linkage accuracy. We illustrate our methodology using real data from popular social networks such as Twitter, Facebook, and YouTube.

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