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This is a written discussion of the paper "Optimal treatment allocations in space and time for on-line control of an emerging infectious disease" by E. B. Laber, N. J. Meyer, B. J. Reich, K. Pacifici, J. A. Collazo and J. Drake, contributed to the Journal of the Royal Statistical Society Series C, Comment: This is the pre-peer reviewed version of the following article: Koskinen, J.H. (2018), Discussion of "Optimal treatment allocations in space and time for on-line control of an emerging infectious disease" by Laber, Meyer, Reich, Pacifici, Collazo and Drake, J.R.Statist.Soc. C 67, p779 which has been published in final form at https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssc.12266

The network influence model is a model for binary outcome variables that accounts for dependencies between outcomes for units that are relationally tied. The basic influence model was previously extended to afford a suite of new dependence assumptions and because of its relation to traditional Markov random field models it is often referred to as the auto logistic actor-attribute model (ALAAM). We extend on current approaches for fitting ALAAMs by presenting a comprehensive Bayesian inference scheme that supports testing of dependencies across subsets of data and the presence of missing data. We illustrate different aspects of the procedures through three empirical examples: masculinity attitudes in an all-male Australian school class, educational progression in Swedish schools, and unemployment among adults in a community sample in Australia. Funding Agencies|Department of DefenseUnited States Department of Defense [ARO W911NF-21-1-0335]; Melbourne climate futures accelerator grant; [NSF-CMMI-2005661]; [APP1073041]

We review the empirical comparison of SAOMs and TERGMs by Leifeld and Cranmer (2019) in Network Science. We note that their model specification uses nodal covariates calculated from observed degrees instead of using structural effects, thus turning endogeneity into circularity. In consequence, their out-of-sample predictions using TERGMs are based on out-of-sample information and thereby predict the future using observations from the future. We conclude that their analysis rest on erroneous model specifications that render the article's conclusions meaningless. Consequently, researchers should disregard recommendations from the criticized paper when making informed modelling choices.

The present paper examines the importance of integrating geographic contextual effects into the analysis of social networks. By considering spatial structures as both produced by and productive of social relations, geographic space seems to be more than the extent on which places, actors or events are located and separated by distance. Territoriality, bordering processes, the sense of place, spatial inequalities, scalar relations and spatial connectivity are among the socio-spatial arrangements and practices that are likely to affect social action. The present empirical analysis thus focuses on policy interactions within the cross-border region of Lille because the spatial dimension particularly influences relations in this area. Specifically, we examine three spatial effects, namely, distance, territorial borders and cross-border territoriality, and we use exponential random graph models to model how these contextual variables influence policy interactions. By addressing multiple spatial effects, we develop a specific approach to control for the interactions that occur between these variables in order to elaborate on the complex processes that lead to the formation of social networks. We also explicitly examine how the spatial interaction function is affected by including in the analysis endogenous network effects, exogenous covariates and border factors. In this regard, we use a novel Monte Carlo-based goodness-of-fit summary in order to demonstrate that the predicted spatial interaction function of our model ? net of other effects ? matches the empirical spatial interaction function.

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

Supplemental material, Appendix-Revision for International Students’ Cross-Cultural Adjustment: Social Selection or Social Influence? by Giovanni R. P. Sadewo, Emiko S. Kashima, Colin Gallagher, Yoshihisa Kashima and Johan Koskinen in Journal of Cross-Cultural Psychology

Supplemental material, Appendix-Revision for International Students’ Cross-Cultural Adjustment: Social Selection or Social Influence? by Giovanni R. P. Sadewo, Emiko S. Kashima, Colin Gallagher, Yoshihisa Kashima and Johan Koskinen in Journal of Cross-Cultural Psychology

This thesis seeks to extend the application of Social Network Analysis (SNA) to temporal graphs, in particular providing new insights for the understanding of covert networks. The analyses undertaken reveal informative features and properties of individuals’ affiliations under covertness that also illustrate how both individuals and events influence the network structure. The review of the literature on covert networks provided in the initial two chapters suggests the presence of some ambiguities concerning how authors define structural properties and dynamics of covert networks. Authors sometimes disagree and use their findings to explain opposite views about covert networks. The controversy in the field is used as a starting point in order to justify the methodological application of SNA to understand how individuals involved in criminal and illegal activities interact with each other. I attempt to use a deductive approach, without preconceived notions about covert network characteristics. In particular, I avoid considering covert networks as organisations in themselves or as cohesive groups. I focus on individuals and their linkages constructed from their common participation in illicit events such as secret meetings, bombing attacks and criminal operations. In order to tackle these processes I developed innovative methods for investigating criminals’ behaviours over time and their willingness to exchange tacit information. The strategy implies the formulation of a network model in order to represent and incorporate in a graph three types of information: individuals, events, and the temporal dimension of events. The inclusion of the temporal dimension offers the possibility of adopting a more comprehensive theoretical framework for considering individuals and event affiliations. This thesis expands the analysis of bipartite covert networks by adopting several avenues to explore in this perspective. Chapter 3 proposes a different way to represent two-mode networks starting from the use of line-graphs, namely the bi-dynamic line-graph data representation (BDLG), through which it is possible to represent the temporal evolution of individual’s trajectories. The following chapter 4 presents some reflections about the idea of cohesion and cohesive subgroups specific to the case of two-mode networks. Based on the affiliation matrices, the analysis of local clustering through bi-cliques offers an attempt to analyse the mechanism of selecting accomplices while taking into account time. Chapter 5 is concerned with the concept of centrality of individuals involved in flows of knowledge exchanges. The theoretical and analytical framework helps in elaborating how individuals share their acquired hands-on experiences with others by attending joint task activities over time. Chapter 6 provides an application of the approaches introduced in the preceding chapters to the specific case of the Noordin Top terrorist network. Here, the knowledge of experience flow centrality measure opens up a new way to quantify the transmission of information and investigate the formation of the criminal capital. Finally, the last Chapter 7 presents some future research extensions by illustrating the versatility of the proposed approaches in order to provide new insights for the understanding of criminals’ behaviours.

The economic wellbeing, physical and mental health of the ageing population in theUnited Kingdom is associated with continued participation in the labour force. Encouraginglater life employment is therefore a key policy issue. Research into older person’s employmenttrajectories is concentrated on male working patterns, and often takes an individualisticapproach that does not account for the domestic context. Previous research on women’slabour force participation has been informed by small scale qualitative studies that do considerthe household domain but these findings cannot be generalized to the wider population.This research investigates the factors associated with the continued employment of womenaged 50 to 59 using data from the English Longitudinal Study of Ageing (ELSA). Transitionrates out of employment between 2001 and 2011 are modeled using multilevel discrete timeevent history specifications that permit the inclusion of time varying covariates. Retirementis characterized as an ageing process which allows the impact of predictors on transitionrates to be assessed and measured as women approach state pension age. Alternative timestructures are considered, with parameter estimates from an age baseline model comparedwith those from a time on study specification. Results illustrate the sensitivity of parameterestimates in discrete time event history models to the measurement of time, and emphasizethe importance of adopting a time metric that is commensurate with the theoretical representation of retirement as a dynamic ageing process.The domestic context is realised as sampled women and their male partners are positionedwithin a household structure, and asymmetric effects of predictors on the transition rate ofeach gender are considered. Own poor health, caring responsibilities and a retired or inactivespouse accelerate labour market exit for women whilst high levels of accrued pension wealthpredict earlier transitions for their male partners. The age of employment exit for femalesis independent of pension wealth, but pension resources do predict the retirement pathwaytaken following any transition that does occur. Women residing in the wealthiest householdsare more likely to report as voluntary retired prior to state pension age whilst those in thepoorest of couples are at higher risk of following an involuntary pathway into an alternativeinactive state. These findings emphasize the importance of conducting research into later lifeemployment trajectories on a household, rather than individual, basis.

In this thesis I carry out an encompassing analysis of the problem of measurement error in retrospectively collected work histories using data from the “Longitudinal Study of the Unemployed”. This dataset has the unique feature of linking survey responses to a retrospective question on work status to administrative data from the Swedish Register of Unemployment. Under the assumption that the register data is a gold standard I explore three research questions: i) what is the prevalence of and the reasons for measurement error in retrospective reports of unemployment; ii) what are the consequences of using such survey data subject to measurement error in event history analysis; and iii) what are the most effective statistical methods to adjust for such measurement error.Regarding the first question I find substantial measurement error in retrospective reports of unemployment, e.g. only 54% of the subjects studied managed to report the correct number of spells of unemployment experienced in the year prior to the interview. Some reasons behind this problem are clear, e.g. the longer the recall period the higher the prevalence of measurement error. However, some others depend on how measurement error is defined, e.g. women were associated with a higher probability of misclassifying spells of unemployment but not with misdating them.To answer the second question I compare different event history models using duration data from the survey and the register as their response variable. Here I find that the impact of measurement error is very large, attenuating regression estimates by about 90% of their true value, and this impact is fairly consistent regardless of the type of event history model used. In the third part of the analysis I implement different adjustment methods and compare their effectiveness. Here I note how standard methods based on strong assumptions such as SIMEX or Regression Calibration are incapable of dealing with the complexity of the measurement process under analysis. More positive results are obtained through the implementation of ad hoc Bayesian adjustments capable of accounting for the different patterns of measurement error using a mixture model.

Criminology has a long history of trying to understand why people reoffend. People that are released from prison offer us the opportunity study the conditions under which some individuals continue to commit crimes and others do not in great detail. Although research in the last years have incorporated the context as a source of influence on recidivism, much of the literature has focused on attributing the explanations solely on the level of the individuals themselves. Taking this individualistic perspective as my point of departure, I take some steps towards incorporating effects of the environment and aspects associated with social influence and learning in explaining why people re-offend (after being released from prisons). Studying the Chilean prison system, I first establish individual factors associated with recidivism, then account for prison environment and characteristics, to finally attempt at accounting for larger community effects. This was done by analysing data from a cohort of offenders who served sentences in Chilean prisons. Individual factors associated with time until recidivism were analysed using Event history models. Then, multilevel models were used to account for prison-specific effects: the exclusive contribution of prison to recidivism. Finally, hierarchical spatial models were used to analyse how space can be associated with varying levels of recidivism. In addition to the effects of individual characteristics, strong evidence of prison-specific effects was found, which implies that individual propensity towards recidivism is not independent of the prison where the sentence is served. In other words, differences in prison settings have the potential to impact on the individual likelihood of re-offending either by reducing or incrementing the individual risk. Likewise, evidence of spatial clustering of recidivism was also found, which indicates that recidivism has also a spatial component operating beyond the individuals' control. The main contribution of this thesis lies in demonstrating that recidivism implies a complex system of interdependence between different actors and institutions, which needs to be considered to understand recidivism in a larger context. These findings have profound theoretical and policy implications, as they imply that the responsibility for recidivism falls not only on the offenders themselves but also on the wider context of the justice system's institutions and society itself.