We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.

Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is an iterative method where a Gaussian process posterior of the underlying function is sequentially updated by new function evaluations. We propose a novel Bayesian optimization framework for situations where the user controls the computational effort and therefore the precision of the function evaluations. This is a common in econometrics where the marginal likelihood is often computed by Markov chain Monte Carlo or importance sampling methods. The new acquisition strategy gives the optimizer the option to explore the function with cheap noisy evaluations and therefore find the optimum faster. The method is applied to estimating the prior hyperparameters in two popular models on US macroeconomic time series data: the steady-state Bayesian vector autoregressive (BVAR) and the time-varying parameter BVAR with stochastic volatility.

The response of a treatment (direct effect) applied on a given unit may be affected by the treatments applied to its neighboring units (neighbor effects). Neighbor designs are considered robust to neighbor effects. Minimal neighbor designs are economical, therefore, these are preferred by the experimenters. Method of cyclic shifts (Rule I) provides the minimal neighbor designs for odd v (number of treatments). Method of cyclic shifts (Rule II) provides the minimal circular Quasi Rees neighbor designs for v even which are considered to be the good alternate to the minimal neighbor designs. In this article, for every case of v even, minimal circular Quasi Rees neighbor designs are constructed in such a way that these designs can also be converted directly into minimal circular balanced and strongly balanced neighbor designs.

A mixture of experts models the conditional density of a response variable using a mixture of regression models with covariate-dependent mixture weights. We extend the finite mixture of experts model by allowing the parameters in both the mixture components and the weights to evolve in time by following random walk processes. Inference for time-varying parameters in richly parameterized mixture of experts models is challenging. We propose a sequential Monte Carlo algorithm for online inference based on a tailored proposal distribution built on ideas from linear Bayes methods and the EM algorithm. The method gives a unified treatment for mixtures with time-varying parameters, including the special case of static parameters. We assess the properties of the method on simulated data and on industrial data where the aim is to predict software faults in a continuously upgraded large-scale software project. 

Objectives: In cats, although ultrasonography remains the preferred modality to evaluate the gastrointestinal tract, computed tomographic (CT) examination of the abdomen is commonly performed. However, a normal description of the gastrointestinal tract is lacking. This study describes the conspicuity and contrast enhancement pattern of the normal gastrointestinal tract in cats using dual-phase CT.

Materials and Methods: Pre- and dual-phase postcontrast (early scan at 30 seconds and late scan mean at 84 seconds) abdominal CT exams of 39 cats without history, clinical signs or diagnosis of gastrointestinal disease were reviewed. The gastrointestinal tract was examined for conspicuity and enhancement pattern using commercially available viewing software (Osirix, v.6.5.2), and diameters of 16 gastrointestinal segments were recorded and compared with published radiographic and ultrasonographic reference values.

Results: Of the 624 gastrointestinal segments, 530 (84.9%) were identified on precontrast studies and 545 (87.3%) segments on postcontrast studies. Of the gastrointestinal wall segments, 257 (41.2%) were identified on precontrast studies and 314 (50.3%) on postcontrast studies. Gastrointestinal segment diameters correlated well with published normal values, whereas wall thickness measurements usually were smaller compared with sonographic normal values. Early mucosal surface enhancement was frequently seen in the gastric cardia and fundus and ileocolic junction, and a mainly transmural wall enhancement in other gastrointestinal segments.

Clinical Significance: Dual-phase CT allows for the identification of gastrointestinal tract segments and walls in cats. Contrast enhancement improves conspicuity and demonstrates wall layering in the cardia, fundus and ileocolic junction.

In this article, four new families named as Weibull extended exponentiated-X (WEE-X), Lomax extended exponentiated-X (LEE-X), Logistic extended exponentiated-X (LGCEE-X), and Burr III extended exponentiated-X (BIIIEE-X) with their quantile functions are proposed. The expressions for distribution function and density function of BIIIEE-X family are written in terms of linear combinations of the exponentiated densities based to parent model. New models, i.e., Weibul extended exponentiated Weibull (WEEW), Lomax extended exponentiated Weibull (LEEW), Logistic extended exponentiated Weibull (LGCEEW), and Burr III extended exponentiated-Weibull (BIIIEEW) distributions are derived, were plotted for functions of probability density and hazard rate at different levels of parameters. Some mathematical properties of the BIIIEEW model are disclosed. The maximum likelihood method for the BIIIEEW model are described. Numerical applications of the BIIIEEW model to disease of cancer datasets are provided.

Objective: The positive effect of antidiabetic medication on cognitive decline has been given some support by, among others, Secnik et al (2021) and Secnik et al (2022). However, as they are observational studies, it is not clear whether the effect is causal.

Research design and methods: Using the Swedish Dementia Registry and supplementary Swedish registers/databases, we identified 1,873 patients (4,732 observations) with diagnosis of diabetes and Alzheimer’s disease or mixed-pathology dementia who were followed-up at least once after dementia diagnosis. The association of use of metformin with Mini-Mental State Examination scores in patients with diabetes and dementia was studied in two ways. 1) The difference between the last and the first score for each patient was compared with treatment (use of metformin) and subjected to a new sensitivity analysis. 2) The difference between scores for each patient at the points in time when there was a change in use of metformin (either start of use, or discontinuation of use) was studied.

Results: There is an association between cognitive decline and use of metformin. However, any conclusion of a causal relationship is tenuous. 

Conclusion: The present study offers no basis for causal conclusions, but given the association, further examination of cognitive effects of metformin is warranted.

Objective: The positive effect of antidiabetic medication on cognitive decline has been given some support by, among others, Secnik et al (2021) and Secnik et al (2022). However, as they are observational studies, it is not clear whether the effect is causal.

Research design and methods: Using the Swedish Dementia Registry and supplementary Swedish registers/databases, we identified 1,873 patients (4,732 observations) with diagnosis of diabetes and Alzheimer’s disease or mixed-pathology dementia who were followed-up at least once after dementia diagnosis. The association of use of metformin with Mini-Mental State Examination scores in patients with diabetes and dementia was studied in two ways. 1) The difference between the last and the first score for each patient was compared with treatment (use of metformin) and subjected to a new sensitivity analysis. 2) The difference between scores for each patient at the points in time when there was a change in use of metformin (either start of use, or discontinuation of use) was studied.

Results: There is an association between cognitive decline and use of metformin. However, any conclusion of a causal relationship is tenuous. 

Conclusion: The present study offers no basis for causal conclusions, but given the association, further examination of cognitive effects of metformin is warranted.

We consider a Gaussian variational approximation of the posterior density in high-dimensional state space models. The number of parameters in the covariance matrix of the variational approximation grows as the square of the number of model parameters, so it is necessary to find simple yet effective parametrisations of the covariance structure when the number of model parame-ters is large. We approximate the joint posterior density of the state vectors by a dynamic factor model, having Markovian time dependence and a factor covariance structure for the states. This gives a reduced description of the dependence struc-ture for the states, as well as a temporal conditional independence structure sim-ilar to that in the true posterior. We illustrate the methodology on two examples. The first is a spatio-temporal model for the spread of the Eurasian collared-dove across North America. Our approach compares favorably to a recently proposed ensemble Kalman filter method for approximate inference in high-dimensional hi-erarchical spatio-temporal models. Our second example is a Wishart-based multi-variate stochastic volatility model for financial returns, which is outside the class of models the ensemble Kalman filter method can handle.

We propose local prediction pools as a method for combining the predictive distributions of a set of experts conditional on a set of variables believed to be related to the predictive accuracy of the experts. This is done in a two-step process where we first estimate the conditional predictive accuracy of each expert given a vector of covariates-or pooling variables-and then combine the predictive distributions of the experts conditional on this local predictive accuracy. To estimate the local predictive accuracy of each expert, we introduce the simple, fast, and interpretable caliper method. Expert pooling weights from the local prediction pool approaches the equal weight solution whenever there is little data on local predictive performance, making the pools robust and adaptive. We also propose a local version of the widely used optimal prediction pools. Local prediction pools are shown to outperform the widely used optimal linear pools in a macroeconomic forecasting evaluation and in predicting daily bike usage for a bike rental company.

Stochastic actor-oriented models (SAOMs) are a modelling framework for analysing network dynamics using network panel data. This paper extends the SAOM to the analysis of multilevel network panels through a random coefficient model, estimated with a Bayesian approach. The proposed model allows testing theories about network dynamics, social influence, and interdependence of multiple networks. It is illustrated by a study of the dynamic interdependence of friendship networks and minor delinquency. Data were available for 126 classrooms in the first year of secondary school, of which 82 were used, containing relatively few missing data points and having not too much network turnover.

University-industry (U-I) collaboration takes on many forms, from research services, teaching and training, to curiosity-led research. In the chemical industries, academic chemists generate new knowledge, address novel problems faced by industry, and train the future workforce in cutting-edge methods. In this study, we examine the dynamic structures of collaborative research contracts and grants between academic and industry partners over a 5-year period within a research-intensive Australian university. We reconstruct internal contract data provided by a university research office as records of its collaborations into a complex relational database that links researchers to research projects. We then structure this complex relational data as a two-mode network of researcher-project collaborations for utilisation with Social Network Analysis (SNA)-a relational methodology ideally suited to relational data. Specifically, we use a stochastic actor-oriented model (SAOM), a statistical network model for longitudinal two-mode network data. Although the dataset is complicated, we manage to replicate it exactly using a very parsimonious and relatable network model. Results indicate that as academics gain experience, they become more involved in direct research contracts with industry, and in research projects more generally. Further, more senior academics are involved in projects involving both industry partners and other academic partners of any level. While more experienced academics are also less likely to repeat collaborations with the same colleagues, there is a more general tendency in these collaborations, regardless of academic seniority or industry engagement, for prior collaborations to predict future collaborations. We discuss implications for industry and academics.

Much of classical optimal design theory relies on specifying a model with only a small number of parameters. In many applications, such models will give reasonable approximations. However, they will often be found not to be entirely correct when enough data are at hand. A property of classical optimal design methodology is that the amount of data does not influence the design when a fixed model is used. However, it is reasonable that a low dimensional model is satisfactory only if limited data is available. With more data available, more aspects of the underlying relationship can be assessed. We consider a simple model that is not thought to be fully correct. The model misspecification, that is, the difference between the true mean and the simple model, is explicitly modeled with a stochastic process. This gives a unified approach to handle situations with both limited and rich data. Our objective is to estimate the combined model, which is the sum of the simple model and the assumed misspecification process. In our situation, the low-dimensional model can be viewed as a fixed effect and the misspecification term as a random effect in a mixed-effects model. Our aim is to predict within this model. We describe how we minimize the prediction error using an optimal design. We compute optimal designs for the full model in different cases. The results confirm that the optimal design depends strongly on the sample size. In low-information situations, traditional optimal designs for models with a small number of parameters are sufficient, while the inclusion of the misspecification term lead to very different designs in data-rich cases. 

The study of hydrological characteristics has a vital role in designing, planning, and managing water resources. The selection of appropriate probability distributions and methods of estimations are basic elements in hydrology analyses. In this article, a new family named the ‘exponentiated power alpha index generalized’ (EPAIG)-G is proposed to develop several new distributions. Using this proposed family, we developed a new model, called the EPAIG-exponential (EPAIG-E). A few structural properties of the EPAIG-G were obtained. The EPAIG-E parameters were estimated through the method of maximum likelihood (MML). The study of the Monte Carlo simulation (MCS) was produced for the EPAIG-E. The model performance is illustrated using real data.

As global average temperatures rise, so does the frequency and intensity of El Niño-induced droughts, which in turn threaten the reliability of hydropower. 1.4 billion people live in countries where hydropower constitutes more than a quarter of the electricity production and which have experienced El Niño droughts, meaning many more power outages can be expected around the world. Little research has been conducted on the impact of power outages on mental health. This study takes Zambia as its case study to examine the impact that El Niño droughts have had on the lives of householders connected to a highly hydropower-dependant electricity grid, and includes the impact it has had on their physical and self-reported mental health. Using 54 online responses to a survey, we found that the greatest impacts of outages spoiled food, compromised entertainment, compromised ability to work and limitation in cooking options. More than a fifth of respondents reported experiencing self-reported depression to a major degree or all of the time due to power outages, with individuals writing their own responses that they felt debilitated, experienced reduced communication and reduced activities, and stress. Using Bayesian inference, we found that changes in sleeping patterns arising from power outages was a statistically significant predictor of self-reported depression. 63% of surveyed households were willing to pay approximately USD 0.10/kWh as of the end of 2019, about double the tariff that they did, to ensure reliable electricity supply. Household income was a statistically significant predictor of willingness to pay more.

When teaching we usually not only demonstrate/discuss how a certain method works, but, not less important, why it works. In contrast, the Wald confidence interval for a binomial p constitutes an excellent example of a case where we might be interested in why a method does not work. It has been in use for many years and, sadly enough, it is still to be found in many textbooks in mathematical statistics/statistics. The reasons for not using this interval are plentiful and this fact gives us a good opportunity to discuss all of its deficiencies and draw conclusions which are of more general interest. We will mostly use already known results and bring them together in a manner appropriate to the teaching situation. The main purpose of this article is to show how to stimulate students to take a more critical view of simplifications and approximations. We primarily aim for master’s students who previously have been confronted with the Wilson (score) interval, but parts of the presentation may as well be suitable for bachelor’s students. 

Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art probabilistic latent network model to forecast multilayer dynamic graphs that are increasingly common in transportation and proposes a community-based extension to reduce the computational burden. Flexible time series analysis is obtained by modeling the probability of edges between vertices through latent Gaussian processes. The models and Bayesian inference are illustrated on a sample of 10-year data from four major airlines within the US air transportation system. Results show how the estimated latent parameters from the models are related to the airlines’ connectivity dynamics, and their ability to project the multilayer graph into the future for out-of-sample full network forecasts, while stochastic blockmodeling allows for the identification of relevant communities. Reliable network predictions would allow policy-makers to better understand the dynamics of the transport system, and help in their planning on e.g. route development, or the deployment of new regulations.

It is common practice to use Laplace approximations to decrease the computational burden when computing the marginal likelihoods in Bayesian versions of generalised linear models (GLM). Marginal likelihoods combined with model priors are then used in different search algorithms to compute the posterior marginal probabilities of models and individual covariates. This allows performing Bayesian model selection and model averaging. For large sample sizes, even the Laplace approximation becomes computationally challenging because the optimisation routine involved needs to evaluate the likelihood on the full dataset in multiple iterations. As a consequence, the algorithm is not scalable for large datasets. To address this problem, we suggest using stochastic optimisation approaches, which only use a subsample of the data for each iteration. We combine stochastic optimisation with Markov chain Monte Carlo (MCMC) based methods for Bayesian model selection and provide some theoretical results on the convergence of the estimates for the resulting time-inhomogeneous MCMC. Finally, we report results from experiments illustrating the performance of the proposed algorithm. 

The experiments where response of a treatment (direct effect) is affected by the treatment(s) applied in neighboring units, neighbor designs are used to balance the neighbor effects. Being the economical, minimal neighbor designs are preferred by the experimenters. Minimal circular neighbor designs could not be constructed for almost every case of v even, where v is number of treatments. For v even, minimal circular generalized neighbor designs are preferred. In this article, algorithms are developed to obtain minimal circular generalized neighbor designs in which (a) v/2 of the unordered pairs, and (b) 3v/2 of the unordered pairs, do not appear as neighbor whereas the remaining ones appear once. These algorithms are also coded with R-language.