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The Gaussian graphical model is one of the most used tools for inferring genetic networks. Nowadays, the data are often collected from different sources or under different biological conditions, resulting in heterogeneous datasets that exhibit a dependency structure that varies across groups. The complex structure of these data is typically recovered using regularized inferential procedures that use two penalties, one that encourages sparsity within each graph and the other that encourages common structures among the different groups. To this date, these approaches have not been developed for handling the case of censored data. However, these data are often generated by gene expression technologies such as RT-qPCR experiments. In this paper, we fill this gap and propose an extension of joint Gaussian graphical modelling to account for censored, or more generally missing, data.

The covariate adjusted glasso is one of the most used estimators for in- ferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data.

The earlier version of this paper appeared as a working paper at https://arxiv.org/abs/1911.04168. All the authors contributed equally to this work. Copyright © 2022 The Author(s). Motivated by reasons such as patients’ needs, lack of resources, technologies or skills, and quality, hospital managers may engage in cooperative behaviours. We study the determinants of patients’ transfers, a specific form of cooperation, and quantify its association with the quality delivered by the origin and destination hos- pital. Using Italian administrative data, we handle the network structure embedded in the data by adopting an over-dispersed Poisson mixed model. Our results show a positive relationship between hospital cooperation and quality. The introduction of transferal protocols that exploit information on hospital quality may further im- prove the overall quality of the healthcare sector. European Cooperation in Science and Technology (COST) [grant number CA15109].

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. In many applied fields, such as genomics, different types of data are collected on the same system, and it is not uncommon that some of these datasets are subject to censoring as a result of the measurement technologies used, such as data generated by polymerase chain reactions and flow cytometer. When the overall objective is that of network inference, at possibly different levels of a system, information coming from different sources and/or different steps of the analysis can be integrated into one model with the use of conditional graphical models. In this paper, we develop a doubly penalized inferential procedure for a conditional Gaussian graphical model when data can be subject to censoring. The computational challenges of handling censored data in high dimensionality are met with the development of an efficient expectation-maximization algorithm, based on approximate calculations of the moments of truncated Gaussian distributions and on a suitably derived two-step procedure alternating graphical lasso with a novel block-coordinate multivariate lasso approach. We evaluate the performance of this approach on an extensive simulation study and on gene expression data generated by RT-qPCR technologies, where we are able to integrate network inference, differential expression detection and data normalization into one model. European Cooperation in Science and Technology (COST) [COST Action CA15109EuropeanCooperationforStatisticsofNetworkDataScience (COSTNET)].

The authors of the paper “Bayesian Graphical Models for Modern Biological Applications” have put forward an important framework for making graphical models more useful in applied settings. In this discussion paper, we give a number of suggestions for making this framework even more suitable for practical scenarios. Firstly, we show that an alternative and simplified definition of covariate might make the framework more manageable in high-dimensional settings. Secondly, we point out that the inclusion of missing variables is important for practical data analysis. Finally, we comment on the effect that the Gaussianity assumption has in identifying the underlying conditional independence graph and how this can be circumvented. The Bayesian framework proposed by the authors is flexible enough to accommodate extensions that can deal with these aspects, which are often encountered in real data analyses such as the complex modern applications considered by the authors.

In this paper we study the impact of competition on hospital adverse health outcomes, using data on patients admitted to hospitals located in the Lombardy region in Italy between 2004 and 2013. We propose an economic framework that incorporates both short and long range forms of competition among hospitals. In a set up where prices are regulated, and under the assumption that hospitals are profit maximisers, hospital managers compete locally in quality to attract more patients. At the same time, managers have an incentive to compete with all other hospitals within the Lombardy region as their relative quality performance will potentially affect their future states. Our empirical model exploits methods from the graphical modelling literature to estimate local rivals, as well as the degree of local and global interdependence among hospitals. Our results show a significant positive degree of short and long range dependence, which suggests the existence of forms of local and global competition among hospitals with relevant implications for the healthcare policy.

H2A.Z is a H2A‐type histone variant essential for many aspects of cell biology, ranging from gene expression to genome stability. From deuterostomes, H2A.Z evolved into two paralogues, H2A.Z.1 and H2A.Z.2, that differ by only three amino acids and are encoded by different genes (H2AFZ and H2AFV, respectively). Despite the importance of this histone variant in development and cellular homeostasis, very little is known about the individual functions of each paralogue in mammals. Here, we have investigated the distinct roles of the two paralogues in cell cycle regulation and unveiled non‐redundant functions for H2A.Z.1 and H2A.Z.2 in cell division. Our findings show that H2A.Z.1 regulates the expression of cell cycle genes such as Myc and Ki‐67 and its depletion leads to a G1 arrest and cellular senescence. On the contrary, H2A.Z.2, in a transcription‐independent manner, is essential for centromere integrity and sister chromatid cohesion regulation, thus playing a key role in chromosome segregation., This study shows that the very similar histone variants H2A.Z.1 and H2A.Z.2 have different functions in chromatin organisation and cell cycle regulation.

SUMMARYH2A.Z is a H2A-type histone variant essential for many aspects of cell biology ranging from gene expression to genome stability. From deuterostomes, H2A.Z evolved into two paralogues H2A.Z.1 and H2A.Z.2 that differ by only three amino acids and are encoded by different genes (H2AFZ and H2AFV respectively). Despite the importance of this histone variant in development and cellular homeostasis, very little is known about the individual functions of each paralogue in mammals. Here we investigated the distinct roles of two paralogues in cell cycle regulation. Using a specific siRNA approach for each paralogue in human cells, we unveiled non-redundant functions for H2A.Z.1 and H2A.Z.2 in cell division: H2A.Z.1 regulates the expression of important cell cycle genes (including Myc and Ki-67) and its depletion leads to a G1 arrest, whereas H2A.Z.2 is essential for centromere integrity and function, thus playing a key role in chromosome segregation.

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., Comment: 24 pages, 5 figures; related R package (manet) available on CRAN

Making use of the information from the World Value Survey (WVS), and operationalizing a definition of national culture that encompasses both the relevance of specific cultural traits and the interdependence among them, this paper proposes a methodology to reveal the latent structure of national culture and to measure cultural distance between countries that takes into account both the difference in cultural traits and the difference in the network structure of national cultures. Exploiting the possibilities offered by copula graphical models for discrete data, this paper infers the cultural networks of all the countries included in the WVS (Wave 6) and proposes a novel unifying framework to measure national culture and international cultural distances. The Jeffreys' divergence between copula graphical models, taken as the measure of cultural distance between countries, captures the orthogonality of the two components of cultural distance: the one based on cultural traits and the one based on the network structure among them. Moreover, the two components are shown to correlate with different national and structural characteristics of cultural networks, thus encompassing the different informational sets related to national cultures., 27 pages, 12 figures, 4 tables

The conditional glasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data.

This paper deals with linear models for a time-dependent response and explanatory variables in a high-dimensional setting. We account for the time dependency in the data by explicitly adding autoregressive terms to the response variable in the model together with an autoregressive process for the residuals. We present a penalized likelihood approach for parameter estimation and discuss its theoretical properties. Finally, we show the successful application of the proposed methodology on simulated data and on two real applications, where we model air pollution and stock market indices, respectively. We provide an implementation of the method in the R package DREGAR, freely available on CRAN, http://CRAN.R-project.org/package=DREGAR .

The putative scale-free nature of real-world networks has generated a lot of interest in the past 20 years: if networks from many different fields share a common structure, then perhaps this suggests some underlying ‘network law’. Testing the degree distribution of networks for power-law tails has been a topic of considerable discussion. Ad hoc statistical methodology has been used both to discredit power-laws as well as to support them. This paper proposes a statistical testing procedure that considers the complex issues in testing degree distributions in networks that result from observing a finite network, having dependent degree sequences and suffering from insufficient power. We focus on testing whether the tail of the empirical degrees behaves like the tail of a de Solla Price model, a two-parameter power-law distribution. We modify the well-known Kolmogorov–Smirnov test to achieve even sensitivity along the tail, considering the dependence between the empirical degrees under the null distribution, while guaranteeing sufficient power of the test. We apply the method to many empirical degree distributions. Our results show that power-law network degree distributions are not rare, classifying almost 65% of the tested networks as having a power-law tail with at least 80% power.

Network data often come in the form of actor-event information, where two types of nodes comprise the very fabric of the network. Examples of such networks are: people voting in an election, users liking/disliking media content, or, more generally, individuals - actors - attending events. Interest lies in discovering communities among these actors, based on their patterns of attendance to the considered events. To achieve this goal, we propose an extension of the model introduced in [5]: our contribution injects covariates into the model, leveraging on parsimony for the parameters and giving insights about the influence of such characteristics on the attendances. We assess the performance of our approach in a simulated environment.

Regression testing is the process of retesting a system after it or its environment has changed. Many techniques aim to find the cheapest subset of the regression test suite that achieves full coverage. More recently, it has been observed that the tester might want to have a range of solutions providing different trade-offs between cost and one or more forms of coverage, this being a multi-objective optimisation problem. This paper further develops the multi-objective agenda by adapting a decomposition-based multi-objective evolutionary algorithm (MOEA/D). Experiments evaluated four approaches: a classic greedy algorithm; non-dominated sorting genetic algorithm II (NSGA-II); MOEA/D with a fixed value for a parameter c ; and MOEA/D in which tuning was used to choose the value of c . These used six programs from the SIR repository and one larger program, VoidAuth. In all of the experiments MOEA/D with tuning was the most effective technique. The relative performance of the other techniques varied, although MOEA/D with fixed c outperformed NSGA-II on the larger programs (Space and VoidAuth).

© 2019 The Authors. Despite the recognized importance of interfirm financial links in determining a company's performance, only a few studies have incorporated proxies for interfirm links in credit risk models, and none of these use real financial transactions. We estimate a credit risk model for small and medium-sized enterprises, augmented with information on observed interfirm financial transactions. We exploit a novel data set on about 60000 companies based in the UK and their financial transactions over the years 2015 and 2016. We develop several network-augmented credit risk models and compare their prediction performance with that of a conventional credit risk model that includes only a set of financial ratios. We find that augmenting a default risk model with information on the transaction network makes a significant contribution to increasing the default prediction power of risk models built specifically for small and medium-sized enterprises. Our results may help bankers and credit scoring agencies to improve the credit scoring of these companies, ultimately reducing their propensity to apply excessive lending restrictions. Engineering and Physical Sciences Research Council (grant EP/L021250/1).

In health care, multilevel models are typically used to evaluate hospitals' performance and to rank hospitals accordingly. While multilevel models capture the hierarchical structure in the data, such as the grouping of patients into hospitals, these models do not account for additional latent structures. In this paper, we develop a novel multilevel logistic cluster-weighted model which can predict a binary outcome, such as mortality within 30 days of discharge, while accounting both for known and latent structures of the data. We develop an Expectation-Maximization algorithm for parameter estimation and a parametric bootstrap approach for assessing the variability of the estimators. Using a rich data set of the Lombardy (Italy) health care system and focussing on the two wards of cardiosurgery and medicine, we show how the proposed model detects, in both cases, two well-defined clusters within the patient to hospital hierarchical structure of the data. A comparison with standard multilevel and cluster-weighted approaches reveals a better fit of the proposed model and a greater insight into the structure of the data. We show how this can have implications in the resulting league tables and thus how the proposed model can be a useful tool for policy-makers and healthcare managers to conduct hospital evaluations.

Pay-for-performance approaches have been widely adopted in order to drive improvements in the quality of healthcare provision. Previous studies evaluating the impact of these programs are either limited by the number of health outcomes or of medical conditions considered. In this paper, we evaluate the effectiveness of a pay-for-performance program on the basis of five health outcomes and across a wide range of medical conditions. The context of the study is the Lombardy region in Italy, where a rewarding program was introduced in 2012. The policy evaluation is based on a difference-in-differences approach. The model includes multiple dependent outcomes, that allow quantifying the joint effect of the program, and random effects, that account for the heterogeneity of the data at the ward and hospital level. Our results show that the policy had a positive effect on the hospitals' performance in terms of those outcomes that can be more influenced by a managerial activity, namely the number of readmissions, transfers and returns to the surgery room. No significant changes which can be related to the pay-for-performance introduction are observed for the number of voluntary discharges and for mortality. Finally, our study shows evidence that the medical wards have reacted more strongly to the pay-for-performance program than the surgical ones, whereas only limited evidence is found in support of a different policy reaction across different types of hospital ownership., Comment: 16 pages, 7 figures

This chapter reviews graphical modeling techniques for estimating large covariance matrices and their inverse. The chapter provides a selective survey of different models and estimators proposed by the graphical modeling literature and offers some practical examples where these methods could be applied in the area of health economics.

Applications of regression models for binary response are very common and models specific to these problems are widely used. Quantile regression for binary response data has recently attracted attention and regularized quantile regression methods have been proposed for high dimensional problems. When the predictors have a natural group structure, such as in the case of categorical predictors converted into dummy variables, then a group lasso penalty is used in regularized methods. In this paper, we present a Bayesian Gibbs sampling procedure to estimate the parameters of a quantile regression model under a group lasso penalty for classification problems with a binary response. Simulated and real data show a good performance of the proposed method in comparison to mean-based approaches and to quantile-based approaches which do not exploit the group structure of the predictors.

Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can be easily implemented in practice. In this paper, a discrete Weibull regression model is shown to be able to adapt in a simple way to different types of dispersions relative to Poisson regression: overdispersion, underdispersion and covariate-specific dispersion. Maximum likelihood can be used for efficient parameter estimation. The description of the model, parameter inference and model diagnostics is accompanied by simulated and real data analyses.