Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit

This paper presents the R package AdMit which provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Student-t distributions to the density of interest. Then, importance sampling or the independence chain Metropolis-Hastings algorithm is used to obtain quantities of interest for the target density, using the fitted mixture as the importance or candidate density. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The relevance of the package is shown in two examples. The first aims at illustrating in detail the use of the functions provided by the package in a bivariate bimodal distribution. The second shows the relevance of the adaptive mixture procedure through the Bayesian estimation of a mixture of ARCH model fitted to foreign exchange log-returns data. The methodology is compared to standard cases of importance sampling and the Metropolis-Hastings algorithm using a naive candidate and with the Griddy-Gibbs approach.

Leader-Follower Effects in Resource Dilemmas: The Roles of Leadership Selection and Social Responsibility

Previous research on the allocation of scarce resources shows that when people are assigned labels of leader or follower in their group, leaders allocate more of the scarce resources to themselves than followers do. In three laboratory studies, we examine the idea that how people are selected for the leader role (i.e. election or appointment) determines whether leaders take more or equal shares (relative to followers) from a common resource. In a first experiment, we show that participants were more accepting of norm violating behavior by an appointed versus elected leader. In a second experiment, we show that when participants were assigned to a leader or follower role, allocations of appointed leaders differed significantly from those of elected leaders and followers, whereas there was no difference between the two latter conditions. Moreover, elected leaders were shown to feel more social responsibility than both appointed leaders and followers. In a final experiment, we show that when participants were primed with the concept of social responsibility (relative to a neutral condition) no difference in allocations between appointed and elected leaders emerged

To Bridge, to Warp or to Wrap?

Important choices for efficient and accurate evaluation of marginal likelihoods by means of Monte Carlo simulation methods are studied for the case of highly non-elliptical posterior distributions. We focus on the situation where one makes use of importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is presented of possible advantages and limitations of different simulation techniques; of possible choices of candidate distributions and choices of target or warped target distributions; and finally of numerical standard errors. The importance of a robust and flexible estimation strategy is demonstrated where the complete posterior distribution is explored. In this respect, the adaptive mixture of Student-t distributions of Hoogerheide et al.(2007) works particularly well. Given an appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal likelihood in the cases investigated in this paper, which include a non-linear regression model of Ritter and Tanner (1992) and a conditional normal distribution of Gelman and Meng (1991). A poor choice of candidate density may lead to a huge loss of efficiency where the numerical standard error may be highly unreliable

Publication metrics and success on the academic job market

SummaryThe number of applicants vastly outnumbers the available academic faculty positions. What makes a successful academic job market candidate is the subject of much current discussion [1–4]. Yet, so far there has been no quantitative analysis of who becomes a principal investigator (PI). We here use a machine-learning approach to predict who becomes a PI, based on data from over 25,000 scientists in PubMed. We show that success in academia is predictable. It depends on the number of publications, the impact factor (IF) of the journals in which those papers are published, and the number of papers that receive more citations than average for the journal in which they were published (citations/IF). However, both the scientist’s gender and the rank of their university are also of importance, suggesting that non-publication features play a statistically significant role in the academic hiring process. Our model (www.pipredictor.com) allows anyone to calculate their likelihood of becoming a PI

Sample size, lag order and critical values of seasonal unit root tests

This paper presents a response surface analysis for the distributions of the popular tests for seasonal unit roots in quarterly observed time series variables developed by Hylleberg et al. (1990). Approximate asymptotic distributions are obtained, and response surface coefficients for 1%-, 5%- and 10%-level critical values are reported, permitting simple computation of accurate critical values for any sample size and lag order. Five test statistics are considered, along with five different specifications of the deterministic component in the test regression; allowance is also made for the lag order to be determined endogenously, using commonly applied selection methods. Dependence of the critical values and the probability density functions on the sample size and lag order is also investigated

Facilitating practice-led co-innovation for the improvement in animal welfare

Using the egg-laying-hen sector as a case study, the European Union-funded ‘Hennovation’ thematic network has been testing mechanisms to enable practice-led innovation through the establishment of 19 innovation networks of farmers and within the laying-hen processing industry, supported by existing science and market-driven actors. These networks were facilitated to proactively search for, share and use new ideas to improve hen welfare, efficiency and sustainability. This article provides insights into the tools used, including a framework for the facilitation of practice-led collaborative innovation processes. This framework was developed through participatory action research to monitor network performance and self-reflection by facilitators. Practice-led innovation processes are network specific and evolve as the actors within the network come together to share common problems, experiment with possible solutions and learn. The participatory and iterative nature of this process leads to uncertainty in process and end results. This raises methodological challenges in the management of such processes and requires a flexible and adaptive management approach focusing on learning and reflection. </jats:p