Exploring the Relationship between Membership Turnover and Productivity in Online Communities

One of the more disruptive reforms associated with the modern Internet is the emergence of online communities working together on knowledge artefacts such as Wikipedia and OpenStreetMap. Recently it has become clear that these initiatives are vulnerable because of problems with membership turnover. This study presents a longitudinal analysis of 891 WikiProjects where we model the impact of member turnover and social capital losses on project productivity. By examining social capital losses we attempt to provide a more nuanced analysis of member turnover. In this context social capital is modelled from a social network perspective where the loss of more central members has more impact. We find that only a small proportion of WikiProjects are in a relatively healthy state with low levels of membership turnover and social capital losses. The results show that the relationship between social capital losses and project performance is U-shaped, and that member withdrawal has significant negative effect on project outcomes. The results also support the mediation of turnover rate and network density on the curvilinear relationship

Novel Models for Multiple Dependent Heteroskedastic Time Series

Functional magnetic resonance imaging or functional MRI (fMRI) is a very popular tool used for differing brain regions by measuring brain activity. It is affected by physiological noise, such as head and brain movement in the scanner from breathing, heart beats, or the subject fidgeting. The purpose of this paper is to propose a novel approach to handling fMRI data for infants with high volatility caused by sudden head movements. Another purpose is to evaluate the volatility modelling performance of multiple dependent fMRI time series data. The models examined in this paper are AR and GARCH and the modelling performance is evaluated by several statistical performance measures. The conclusions of this paper are that multiple dependent fMRI series data can be fitted with AR + GARCH model if the multiple fMRI data have many sudden head movements. The GARCH model can capture the shared volatility clustering caused by head movements across brain regions. However, the multiple fMRI data without many head movements have fitted AR + GARCH model with different performance. The conclusions are supported by statistical tests and measures. This paper highlights the difference between the proposed approach from traditional approaches when estimating model parameters and modelling conditional variances on multiple dependent time series. In the future, the proposed approach can be applied to other research fields, such as financial economics, and signal processing. Code is available at \url{https://github.com/13204942/STAT40710}.Comment: 18 page

Variational Bayesian Inference for the Latent Position Cluster Model

Many recent approaches to modeling social networks have focussed on embedding the actors in a latent “social space”. Links are more likely for actors that are close in social space than for actors that are distant in social space. In particular, the Latent Position Cluster Model (LPCM) [1] allows for explicit modelling of the clustering that is exhibited in many network datasets. However, inference for the LPCM model via MCMC is cumbersome and scaling of this model to large or even medium size networks with many interacting nodes is a challenge. Variational Bayesian methods offer one solution to this problem. An approximate, closed form posterior is formed, with unknown variational parameters. These parameters are tuned to minimize the Kullback-Leibler divergence between the approximate variational posterior and the true posterior, which known only up to proportionality. The variational Bayesian approach is shown to give a computationally efficient way of fitting the LPCM. The approach is demonstrated on a number of data sets and it is shown to give a good fit

Modelling Temporal Uncertainty in Palaeoclimate Reconstructions

Abstract We present a method for reconstructing an aspect of climate over 13,000 years at Sluggan Moss, Northern Ireland. We extend the work of [1] to include calibrated radiocarbon ages. The required chronologies are obtained via a new monotone stochastic process. We also discuss the t 8 long-tailed random walk model and produce predictive climate estimates

On Bayesian Modelling of the Uncertainties in Palaeoclimate Reconstruction

We outline a model and algorithm to perform inference on the palaeoclimate and palaeoclimate volatility from pollen proxy data. We use a novel multivariate non-linear non-Gaussian state space model consisting of an observation equation linking climate to proxy data and an evolution equation driving climate change over time. The link from climate to proxy data is defined by a pre-calibrated forward model, as developed in Salter-Townshend and Haslett (2012) and Sweeney (2012). Climatic change is represented by a temporally-uncertain Normal-Inverse Gaussian Levy process, being able to capture large jumps in multivariate climate whilst remaining temporally consistent. The pre-calibrated nature of the forward model allows us to cut feedback between the observation and evolution equations and thus integrate out the state variable entirely whilst making minimal simplifying assumptions. A key part of this approach is the creation of mixtures of marginal data posteriors representing the information obtained about climate from each individual time point. Our approach allows for an extremely efficient MCMC algorithm, which we demonstrate with a pollen core from Sluggan Bog, County Antrim, Northern Ireland.Comment: 25 pages, 7 figure

Statistical Challenges in Estimating Past Climate Changes

We review the statistical methods currently in use to estimate past changes in climate. These methods encompass the full gamut of statistical modeling approaches, ranging from simple regression up to nonparametric spatiotemporal Bayesian models. Often the full inferential challenge is broken down into many submodels each of which may involve multiple stochastic components, and occasionally mechanistic or process‐based models too. We argue that many of the traditional approaches are simplistic in their structure, handling, and presentation of uncertainty, and that newer models (which incorporate mechanistic aspects alongside statistical models) provide an exciting research agenda for the next decade. We hope that policy‐makers and those charged with predicting future climate change will increasingly use probabilistic paleoclimate reconstructions to calibrate their forecasts, learn about key natural climatological parameters, and make appropriate decisions concerning future climate change. Remarkably few statisticians have involved themselves with paleoclimate reconstruction, and we hope that this article inspires more to take up the challenge

Bayesian Exponential Random Graph Models with Nodal Random Effects

We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Bayesian framework for ERGMs proposed by Caimo and Friel (2011) yields the basis of our modelling algorithm. A central question in network models is the question of model selection and following the Bayesian paradigm we focus on estimating Bayes factors. To do so we develop an approximate but feasible calculation of the Bayes factor which allows one to pursue model selection. Two data examples and a small simulation study illustrate our mixed model approach and the corresponding model selection.Comment: 23 pages, 9 figures, 3 table

Genomic insights into the population history and adaptive traits of Latin American Criollo cattle.

Criollo cattle, the descendants of animals brought by Iberian colonists to the Americas, have been the subject of natural and human-mediated selection in novel tropical agroecological zones for centuries. Consequently, these breeds have evolved distinct characteristics such as resistance to diseases and exceptional heat tolerance. In addition to European taurine (Bos taurus) ancestry, it has been proposed that gene flow from African taurine and Asian indicine (Bos indicus) cattle has shaped the ancestry of Criollo cattle. In this study, we analysed Criollo breeds from Colombia and Venezuela using whole-genome sequencing (WGS) and single-nucleotide polymorphism (SNP) array data to examine population structure and admixture at high resolution. Analysis of genetic structure and ancestry components provided evidence for African taurine and Asian indicine admixture in Criollo cattle. In addition, using WGS data, we detected selection signatures associated with a myriad of adaptive traits, revealing genes linked to thermotolerance, reproduction, fertility, immunity and distinct coat and skin coloration traits. This study underscores the remarkable adaptability of Criollo cattle and highlights the genetic richness and potential of these breeds in the face of climate change, habitat flux and disease challenges. Further research is warranted to leverage these findings for more effective and sustainable cattle breeding programmes

Bayesian exponential random graph modelling of interhospital patient referral networks

Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among health care organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well-known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described, can be reproduced with accuracy by specifying the system of local dependencies that produce – but at the same time are induced by – decentralised collaborative arrangements between hospitals