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  1. Model‐based clustering of time‐evolving networks has emerged as one of the important research topics in statistical network analysis. It is a fundamental research question to model time‐varying network parameters. However, due to difficulties in modelling functional network parameters, there is little progress in the current literature to model time‐varying network parameters effectively. In this work, we model network parameters as univariate nonparametric functions instead of constants. We effectively estimate those functional network parameters in temporal exponential‐family random graph models using a kernel regression technique and a local likelihood approach. Furthermore, we propose a semiparametric finite mixture of temporal exponential‐family random graph models by adopting finite mixture models, which simultaneously allows both modelling and detecting groups in time‐evolving networks. Also, we use a conditional likelihood to construct an effective model selection criterion and network cross‐validation to choose an optimal bandwidth. The power of our method is demonstrated in simulation studies and real‐world applications to dynamic international trade networks and dynamic arm trade networks.

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  3. With recent improvements in high-volume hydraulic fracturing (HVHF, known to the public as fracking), vast new reservoirs of natural gas and oil are now being tapped. As HVHF has expanded into the populous northeastern USA, some residents have become concerned about impacts on water quality. Scientists have addressed this concern by investigating individual case studies or by statistically assessing the rate of problems. In general, however, lack of access to new or historical water quality data hinders the latter assessments. We introduce a new statistical approach to assess water quality datasets – especially sets that differ in data volume and variance – and apply the technique to one region of intense shale gas development in northeastern Pennsylvania (PA) and one with fewer shale gas wells in northwestern PA. The new analysis for the intensely developed region corroborates an earlier analysis based on a different statistical test: in that area, changes in groundwater chemistry show no degradation despite that area's dense development of shale gas. In contrast, in the region with fewer shale gas wells, we observe slight but statistically significant increases in concentrations in some solutes in groundwaters. One potential explanation for the slight changes in groundwater chemistry in that area (northwestern PA) is that it is the regional focus of the earliest commercial development of conventional oil and gas (O&G) in the USA. Alternate explanations include the use of brines from conventional O&G wells as well as other salt mixtures on roads in that area for dust abatement or de-icing, respectively. 
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