arXiv subjects

Ernst C. Wit

Publications and source records attributed to Ernst C. Wit.

At least 37 records · Page 2Linked to original sources

arXiv2024

Stochastic Actor Oriented Model with Random Effects

The stochastic actor oriented model (SAOM) is a method for modelling social interactions and social behaviour over time. It can be used to model drivers of dynamic interactions using both exogenous covariates and endogenous network configurations, but also the co-evolution of behaviour and social interactions. In its standard implementations, it assumes that all individual have the same interaction evaluation function. This lack of heterogeneity is one of its limitations. The aim of this paper is to extend the inference framework for the SAOM to include random effects, so that the heterogeneity of individuals can be modeled more accurately. We decompose the linear evaluation function that models the probability of forming or removing a tie from the network, in a homogeneous fixed part and a random, individual-specific part. We extend the Robbins-Monro algorithm to the estimation of the variance of the random parameters. Our method is applicable for the general random effect formulations. We illustrate the method with a random out-degree model and show the parameter estimation of the random components, significance tests and model evaluation. We apply the method to the Kapferer's Tailor shop study. It is shown that a random out-degree constitutes a serious alternative to including transitivity and higher-order dependency effects.

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arXiv2023

Joint modelling of national cultures accounting for within and between-country heterogeneity

Cultural values vary significantly around the world. Despite a large heterogeneity, similarities across national cultures are present. This paper studies cross-country culture heterogeneity via the joint inference of country-specific copula graphical models from world-wide survey data. To this end, a random graph generative model of the cultural networks is introduced, with a latent space and proximity measures that embed cultural relatedness across countries. Within-country heterogeneity is also accounted for, via parametric modelling of the marginal distributions of each cultural trait. All together, the different components of the model are able to identify several dimensions of culture.

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arXiv2023

Modeling non-linear Effects with Neural Networks in Relational Event Models

Dynamic networks offer an insight of how relational systems evolve. However, modeling these networks efficiently remains a challenge, primarily due to computational constraints, especially as the number of observed events grows. This paper addresses this issue by introducing the Deep Relational Event Additive Model (DREAM) as a solution to the computational challenges presented by modeling non-linear effects in Relational Event Models (REMs). DREAM relies on Neural Additive Models to model non-linear effects, allowing each effect to be captured by an independent neural network. By strategically trading computational complexity for improved memory management and leveraging the computational capabilities of Graphic Processor Units (GPUs), DREAM efficiently captures complex non-linear relationships within data. This approach demonstrates the capability of DREAM in modeling dynamic networks and scaling to larger networks. Comparisons with traditional REM approaches showcase DREAM superior computational efficiency. The model potential is further demonstrated by an examination of the patent citation network, which contains nearly 8 million nodes and 100 million events.

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arXiv2023

Latent event history models for quasi-reaction systems

Various processes can be modelled as quasi-reaction systems of stochastic differential equations, such as cell differentiation and disease spreading. Since the underlying data of particle interactions, such as reactions between proteins or contacts between people, are typically unobserved, statistical inference of the parameters driving these systems is developed from concentration data measuring each unit in the system over time. While observing the continuous time process at a time scale as fine as possible should in theory help with parameter estimation, the existing Local Linear Approximation (LLA) methods fail in this case, due to numerical instability caused by small changes of the system at successive time points. On the other hand, one may be able to reconstruct the underlying unobserved interactions from the observed count data. Motivated by this, we first formalise the latent event history model underlying the observed count process. We then propose a computationally efficient Expectation-Maximation algorithm for parameter estimation, with an extended Kalman filtering procedure for the prediction of the latent states. A simulation study shows the performance of the proposed method and highlights the settings where it is particularly advantageous compared to the existing LLA approaches. Finally, we present an illustration of the methodology on the spreading of the COVID-19 pandemic in Italy.

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arXiv2023

Bayesian Poisson Regression and Tensor Train Decomposition Model for Learning Mortality Pattern Changes during COVID-19 Pandemic

COVID-19 has led to excess deaths around the world, however it remains unclear how the mortality of other causes of death has changed during the pandemic. Aiming at understanding the wider impact of COVID-19 on other death causes, we study Italian data set that consists of monthly mortality counts of different causes from January 2015 to December 2020. Due to the high dimensional nature of the data, we develop a model which combines conventional Poisson regression with tensor train decomposition to explore the lower dimensional residual structure of the data. We take a Bayesian approach, impose priors on model parameters. Posterior inference is performed using an efficient Metropolis-Hastings within Gibbs algorithm. The validity of our approach is tested in simulation studies. Our method not only identifies differential effects of interventions on cause specific mortality rates through the Poisson regression component, but also offers informative interpretations of the relationship between COVID-19 and other causes of death as well as latent classes that underline demographic characteristics, temporal patterns and causes of death respectively.

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arXiv2023

Relational Event Modeling

Advances in information technology have increased the availability of time-stamped relational data such as those produced by email exchanges or interaction through social media. Whereas the associated information flows could be aggregated into cross-sectional panels, the temporal ordering of the events frequently contains information that requires new models for the analysis of continuous-time interactions, subject to both endogenous and exogenous influences. The introduction of the Relational Event Model (REM) has been a major development that has led to further methodological improvements stimulated by new questions that REMs made possible. In this review, we track the intellectual history of the REM, define its core properties, and discuss why and how it has been considered useful in empirical research. We describe how the demands of novel applications have stimulated methodological, computational, and inferential advancements.

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arXiv2023

A Stochastic Gradient Relational Event Additive Model for modelling US patent citations from 1976 until 2022

Until 2022, the US patent citation network contained almost 10 million patents and over 100 million citations. To overcome limitations in analyzing such complex networks, we propose a stochastic gradient relational event additive model (STREAM) that models the relationships between citing patents as events that occur over time, where predictors are modeled through B-splines. Our model identifies key factors driving patent citation and reveals insights, such as time windows where citations are more likely and the relevance of the increasing citation numbers per patent. Overall, the STREAM offers the potential for capturing dynamics in large sparse networks while maintaining interpretability.

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arXiv2023

Efficient implementation of sets and multisets in R using hash tables

The package hset for the R language contains an implementation of a S4 class for sets and multisets of numbers. The implementation, based on the hash table data structure from the package hash (Brown, 2019), allows for quick operations when the set is a dynamic object. An important example is when a set or a multiset is part of the state of a Markov chain in which in each iteration various elements are moved in and out of the set.

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arXiv2023

Drivers of the decrease of patent similarities from 1976 to 2021

The citation network of patents citing prior art arises from the legal obligation of patent applicants to properly disclose their invention. One way to study the relationship between current patents and their antecedents is by analyzing the similarity between the textual elements of patents. Many patent similarity indicators have shown a constant decrease since the mid-70s. Although several explanations have been proposed, more comprehensive analyses of this phenomenon have been rare. In this paper, we use a computationally efficient measure of patent similarity scores that leverages state-of-the-art Natural Language Processing tools, to investigate potential drivers of this apparent similarity decrease. This is achieved by modeling patent similarity scores by means of generalized additive models. We found that non-linear modeling specifications are able to distinguish between distinct, temporally varying drivers of the patent similarity levels that explain more variation in the data ($R^2\sim 18\%$) compared to previous methods. Moreover, the model reveals an underlying trend in similarity scores that is fundamentally different from the one presented previously.

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arXiv2023

Analysing ecological dynamics with relational event models: the case of biological invasions

Aim: Spatio-temporal processes play a key role in ecology, from genes to large-scale macroecological and biogeographical processes. Existing methods studying such spatio-temporally structured data either simplify the dynamic structure or the complex interactions of ecological drivers. This paper aims to present a generic method for ecological research that allows analysing spatio-temporal patterns of biological processes at large spatial scales by including the time-varying variables that drive these dynamics. Methods: We introduce a method called relational event modelling (REM), which relies on temporal interaction dynamics, that encode sequences of relational events connecting a sender node to a recipient node at a specific point in time. We apply REM to the spread of alien species around the globe between 1880 and 2005, following accidental or deliberate introductions into geographical regions outside of their native range. In this context, a relational event represents the new occurrence of an alien species given its former distribution. Results: The application of REM to the first reported invasions of 4835 established alien species outside of their native ranges from four major taxonomic groups enables us to unravel the main drivers of the dynamics of the spread of invasive alien species. Combining the alien species first records data with other spatio-temporal information enables us to discover which factors have been responsible for the spread of species across the globe. Besides the usual drivers of species invasions, such as trade, land use and climatic conditions, we also find evidence for species-interconnectedness in alien species spread. Conclusions: REM offer the capacity to account for the temporal sequences of ecological events such as biological invasions and to investigate how relationships between these events and potential drivers change over time.

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arXiv2023

netgwas: An R Package for Network-Based Genome-Wide Association Studies

Graphical models are a powerful tool in modelling and analysing complex biological associations in high-dimensional data. The R-package netgwas implements the recent methodological development on copula graphical models to (i) construct linkage maps, (ii) infer linkage disequilibrium networks from genotype data, and (iii) detect high-dimensional genotype-phenotype networks. The netgwas learns the structure of networks from ordinal data and mixed ordinal-and-continuous data. Here, we apply the functionality in netgwas to various multivariate example datasets taken from the literature to demonstrate the kind of insight that can be obtained from the package. We show that our package offers a more realistic association analysis than the classical approaches, as it discriminates between direct and induced correlations by adjusting for the effect of all other variables while performing pairwise associations. This feature controls for spurious interactions between variables that can arise from conventional approaches in a biological sense. The netgwas package uses a parallelization strategy on multi-core processors to speed-up computations. The netgwas package is freely available at https://cran.r-project.org/web/packages/netgwas

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arXiv2022

Dynamic latent space relational event model

Dynamic relational processes, such as e-mail exchanges, bank loans and scientific citations, are important examples of dynamic networks, in which the relational events consistute time-stamped edges. There are contexts where the network might be considered a reflection of underlying dynamics in some latent space, whereby nodes are associated with dynamic locations and their relative distances drive their interaction tendencies. As time passes nodes can change their locations assuming new configurations, with different interaction patterns. The aim of this paper is to define a dynamic latent space relational event model. We then develop a computationally efficient method for inferring the locations of the nodes. We make use of the Expectation Maximization algorithm which embeds an extension of the universal Kalman filter. Kalman filters are known for being effective tools in the context of tracking objects in the space, with successful applications in fields such as geolocalization. We extend its application to dynamic networks by filtering the signal from a sequence of adjacency matrices and recovering the hidden movements. Besides the latent space our formulation includes also more traditional fixed and random effects, achieving a general model that can suit a large variety of applications.

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arXiv2022

A predictive model for planning emergency events rescue during COVID-19 in Lombardy, Italy

Italy, particularly the Lombardy region, was among the first countries outside of Asia to report cases of COVID-19. The emergency medical service called Regional Emergency Agency (AREU) coordinates the intra- and inter-regional non-hospital emergency network and the European emergency number service in Lombardy. AREU must deal with daily and seasonal variations of call volume. The number and type of emergency calls changed dramatically during the COVID-19 pandemic. A model to predict incoming calls and how many of these turn into events, i.e., dispatch of transport and equipment until the rescue is completed, was developed to address the emergency period. We used the generalized additive model with a negative binomial family to predict the number of events one, two, five, and seven days ahead. The over-dispersion of the data was tackled by using the negative binomial family and the nonlinear relationship between the number of events and covariates (e.g., seasonal effects) by smoothing splines. The model coefficients show the effect of variables, e.g., the day of the week, on the number of events and how these effects change during the pre-COVID-19 period. The proposed model returns reasonable mean absolute errors for most of the 2020-2021 period.

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arXiv2020

COVID-19 and the difficulty of inferring epidemiological parameters from clinical data

Knowing the infection fatality ratio (IFR) is of crucial importance for evidence-based epidemic management: for immediate planning; for balancing the life years saved against the life years lost due to the consequences of management; and for evaluating the ethical issues associated with the tacit willingness to pay substantially more for life years lost to the epidemic, than for those to other diseases. Against this background Verity et al. (2020, Lancet Infections Diseases) have rapidly assembled case data and used statistical modelling to infer the IFR for COVID-19. We have attempted an in-depth statistical review of their approach, to identify to what extent the data are sufficiently informative about the IFR to play a greater role than the modelling assumptions, and have tried to identify those assumptions that appear to play a key role. Given the difficulties with other data sources, we provide a crude alternative analysis based on the Diamond Princess Cruise ship data and case data from China, and argue that, given the data problems, modelling of clinical data to obtain the IFR can only be a stop-gap measure. What is needed is near direct measurement of epidemic size by PCR and/or antibody testing of random samples of the at risk population.

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arXiv2019

Identifying overlapping terrorist cells from the Noordin Top actor-event network

Actor-event data are common in sociological settings, whereby one registers the pattern of attendance of a group of social actors to a number of events. We focus on 79 members of the Noordin Top terrorist network, who were monitored attending 45 events. The attendance or non-attendance of the terrorist to events defines the social fabric, such as group coherence and social communities. The aim of the analysis of such data is to learn about the affiliation structure. Actor-event data is often transformed to actor-actor data in order to be further analysed by network models, such as stochastic block models. This transformation and such analyses lead to a natural loss of information, particularly when one is interested in identifying, possibly overlapping, subgroups or communities of actors on the basis of their attendances to events. In this paper we propose an actor-event model for overlapping communities of terrorists, which simplifies interpretation of the network. We propose a mixture model with overlapping clusters for the analysis of the binary actor-event network data, called {\tt manet}, and develop a Bayesian procedure for inference. After a simulation study, we show how this analysis of the terrorist network has clear interpretative advantages over the more traditional approaches of affiliation network analysis.

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arXiv2019

Mixtures of multivariate generalized linear models with overlapping clusters

With the advent of ubiquitous monitoring and measurement protocols, studies have started to focus more and more on complex, multivariate and heterogeneous datasets. In such studies, multivariate response variables are drawn from a heterogeneous population often in the presence of additional covariate information. In order to deal with this intrinsic heterogeneity, regression analyses have to be clustered for different groups of units. Up until now, mixture model approaches assigned units to distinct and non-overlapping groups. However, not rarely these units exhibit more complex organization and clustering. It is our aim to define a mixture of generalized linear models with overlapping clusters of units. This involves crucially an overlap function, that maps the coefficients of the parent clusters into the the coefficient of the multiple allocation units. We present a computationally efficient MCMC scheme that samples the posterior distribution of the parameters in the model. An example on a two-mode network study shows details of the implementation in the case of a multivariate probit regression setting. A simulation study shows the overall performance of the method, whereas an illustration of the voting behaviour on the US supreme court shows how the 9 justices split in two overlapping sets of justices.

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arXiv2019

BDgraph: An R Package for Bayesian Structure Learning in Graphical Models

Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decomposable) with continuous, discrete, and mixed variables. The package efficiently implements recent improvements in the Bayesian literature, including that of Mohammadi and Wit (2015) and Dobra and Mohammadi (2018). To speed up computations, the computationally intensive tasks have been implemented in C++ and interfaced with R, and the package has parallel computing capabilities. In addition, the package contains several functions for simulation and visualization, as well as several multivariate datasets taken from the literature and used to describe the package capabilities. The paper includes a brief overview of the statistical methods which have been implemented in the package. The main part of the paper explains how to use the package. Furthermore, we illustrate the package's functionality in both real and artificial examples.

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arXiv2018

Dynamic Chain Graph Models for Ordinal Time Series Data

This paper introduces sparse dynamic chain graph models for network inference in high dimensional non-Gaussian time series data. The proposed method parametrized by a precision matrix that encodes the intra time-slice conditional independence among variables at a fixed time point, and an autoregressive coefficient that contains dynamic conditional independences interactions among time series components across consecutive time steps. The proposed model is a Gaussian copula vector autoregressive model, which is used to model sparse interactions in a high-dimensional setting. Estimation is achieved via a penalized EM algorithm. In this paper, we use an efficient coordinate descent algorithm to optimize the penalized log-likelihood with the smoothly clipped absolute deviation penalty. We demonstrate our approach on simulated and genomic datasets. The method is implemented in an R package tsnetwork.

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