The purpose of this paper is to demonstrate and evaluate the use of Bayesian dynamic borrowing (Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case of a general framework of historical borrowing where the degree of borrowing depends on the heterogeneity among historical data and current data. A joint prior distribution over the historical and current data sets is specified with the degree of heterogeneity across the data sets controlled by the variance of the joint distribution. We apply Bayesian dynamic borrowing to both single-level and multilevel models and compare this approach to other historical borrowing methods such as complete pooling, Bayesian synthesis, and power priors. Two case studies using data from the Program for International Student Assessment reveal the utility of Bayesian dynamic borrowing in terms of predictive accuracy. This is followed by two simulation studies that reveal the utility of Bayesian dynamic borrowing over simple pooling and power priors in cases where the historical data is heterogeneous compared to the current data based on bias, mean squared error, and predictive accuracy. In cases of homogeneous historical data, Bayesian dynamic borrowing performs similarly to data pooling, Bayesian synthesis, and power priors. In contrast, for heterogeneous historical data, Bayesian dynamic borrowing performed at least as well, if not better, than other methods of borrowing with respect to mean squared error, percent bias, and leave-one-out cross-validation.

A general latent variable model is given which includes the specification of a missing data mechanism. This framework allows for an elucidating discussion of existing general multivariate theory bearing on maximum likelihood estimation with missing data. Here, missing completely at random is not a prerequisite for unbiased estimation in large samples, as when using the traditional listwise or pairwise present data approaches. The theory is connected with old and new results in the area of selection and factorial invariance. It is pointed out that in many applications, maximum likelihood estimation with missing data may be carried out by existing structural equation modeling software, such as LISREL and LISCOMP. Several sets of artifical data are generated within the general model framework. The proposed estimator is compared to the two traditional ones and found superior.

When conducting robustness research where the focus of attention is on the impact of non-normality, the marginal skewness and kurtosis are often used to set the degree of non-normality. Monte Carlo methods are commonly applied to conduct this type of research by simulating data from distributions with skewness and kurtosis constrained to pre-specified values. Although several procedures have been proposed to simulate data from distributions with these constraints, no corresponding procedures have been applied for discrete distributions. In this paper, we present two procedures based on the principles of maximum entropy and minimum cross-entropy to estimate the multivariate observed ordinal distributions with constraints on skewness and kurtosis. For these procedures, the correlation matrix of the observed variables is not specified but depends on the relationships between the latent response variables. With the estimated distributions, researchers can study robustness not only focusing on the levels of non-normality but also on the variations in the distribution shapes. A simulation study demonstrates that these procedures yield excellent agreement between specified parameters and those of estimated distributions. A robustness study concerning the effect of distribution shape in the context of confirmatory factor analysis shows that shape can affect the robust ${\chi }^2$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\chi ^2$$\end{document} and robust fit indices, especially when the sample size is small, the data are severely non-normal, and the fitted model is complex.

Issues of model selection have dominated the theoretical and applied statistical literature for decades. Model selection methods such as ridge regression, the lasso, and the elastic net have replaced ad hoc methods such as stepwise regression as a means of model selection. In the end, however, these methods lead to a single final model that is often taken to be the model considered ahead of time, thus ignoring the uncertainty inherent in the search for a final model. One method that has enjoyed a long history of theoretical developments and substantive applications, and that accounts directly for uncertainty in model selection, is Bayesian model averaging (BMA). BMA addresses the problem of model selection by not selecting a final model, but rather by averaging over a space of possible models that could have generated the data. The purpose of this paper is to provide a detailed and up-to-date review of BMA with a focus on its foundations in Bayesian decision theory and Bayesian predictive modeling. We consider the selection of parameter and model priors as well as methods for evaluating predictions based on BMA. We also consider important assumptions regarding BMA and extensions of model averaging methods to address these assumptions, particularly the method of Bayesian stacking. Simple empirical examples are provided and directions for future research relevant to psychometrics are discussed.

Considering that causal mechanisms unfold over time, it is important to investigate the mechanisms over time, taking into account the time-varying features of treatments and mediators. However, identification of the average causal mediation effect in the presence of time-varying treatments and mediators is often complicated by time-varying confounding. This article aims to provide a novel approach to uncovering causal mechanisms in time-varying treatments and mediators in the presence of time-varying confounding. We provide different strategies for identification and sensitivity analysis under homogeneous and heterogeneous effects. Homogeneous effects are those in which each individual experiences the same effect, and heterogeneous effects are those in which the effects vary over individuals. Most importantly, we provide an alternative definition of average causal mediation effects that evaluates a partial mediation effect; the effect that is mediated by paths other than through an intermediate confounding variable. We argue that this alternative definition allows us to better assess at least a part of the mediated effect and provides meaningful and unique interpretations. A case study using ECLS-K data that evaluates kindergarten retention policy is offered to illustrate our proposed approach.

A two-step Bayesian propensity score approach is introduced that incorporates prior information in the propensity score equation and outcome equation without the problems associated with simultaneous Bayesian propensity score approaches. The corresponding variance estimators are also provided. The two-step Bayesian propensity score is provided for three methods of implementation: propensity score stratification, weighting, and optimal full matching. Three simulation studies and one case study are presented to elaborate the proposed two-step Bayesian propensity score approach. Results of the simulation studies reveal that greater precision in the propensity score equation yields better recovery of the frequentist-based treatment effect. A slight advantage is shown for the Bayesian approach in small samples. Results also reveal that greater precision around the wrong treatment effect can lead to seriously distorted results. However, greater precision around the correct treatment effect parameter yields quite good results, with slight improvement seen with greater precision in the propensity score equation. A comparison of coverage rates for the conventional frequentist approach and proposed Bayesian approach is also provided. The case study reveals that credible intervals are wider than frequentist confidence intervals when priors are non-informative.

We present the Sydney Radio Star Catalogue, a new catalogue of stars detected at megahertz to gigahertz radio frequencies. It consists of 839 unique stars with 3 405 radio detections, more than doubling the previously known number of radio stars. We have included stars from large area searches for radio stars found using circular polarisation searches, cross-matching, variability searches, and proper motion searches as well as presenting hundreds of newly detected stars from our search of Australian SKA Pathfinder observations. The focus of this first version of the catalogue is on objects detected in surveys using SKA precursor and pathfinder instruments; however, we will expand this scope in future versions. The 839 objects in the Sydney Radio Star Catalogue are distributed across the whole sky and range from ultracool dwarfs to Wolf-Rayet stars. We demonstrate that the radio luminosities of cool dwarfs are lower than the radio luminosities of more evolved sub-giant and giant stars. We use X-ray detections of 530 radio stars by the eROSITA soft X-ray instrument onboard the Spectrum Roentgen Gamma spacecraft to show that almost all of the radio stars in the catalogue are over-luminous in the radio, indicating that the majority of stars at these radio frequencies are coherent radio emitters. The Sydney Radio Star Catalogue can be found in Vizier or at https://radiostars.org.

We argue that proxy failure contributes to poor measurement practices in psychological science and that a tradeoff exists between the legibility and fidelity of proxies whereby increasing legibility can result in decreased fidelity.

We present a systematic search for radio counterparts of novae using the Australian Square Kilometer Array Pathfinder (ASKAP). Our search used the Rapid ASKAP Continuum Survey, which covered the entire sky south of declination $+41^{\circ}$ ($\sim$$34000$ square degrees) at a central frequency of 887.5 MHz, the Variables and Slow Transients Pilot Survey, which covered $\sim$$5000$ square degrees per epoch (887.5 MHz), and other ASKAP pilot surveys, which covered $\sim$200–2000 square degrees with 2–12 h integration times. We crossmatched radio sources found in these surveys over a two–year period, from 2019 April to 2021 August, with 440 previously identified optical novae, and found radio counterparts for four novae: V5668 Sgr, V1369 Cen, YZ Ret, and RR Tel. Follow-up observations with the Australian Telescope Compact Array confirm the ejecta thinning across all observed bands with spectral analysis indicative of synchrotron emission in V1369 Cen and YZ Ret. Our light-curve fit with the Hubble Flow model yields a value of $1.65\pm 0.17 \times 10^{-4} \rm \:M_\odot$ for the mass ejected in V1369 Cen. We also derive a peak surface brightness temperature of $250\pm80$ K for YZ Ret. Using Hubble Flow model simulated radio lightcurves for novae, we demonstrate that with a 5$\sigma$ sensitivity limit of 1.5 mJy in 15-min survey observations, we can detect radio emission up to a distance of 4 kpc if ejecta mass is in the range $10^{-3}\rm \:M_\odot$, and upto 1 kpc if ejecta mass is in the range $10^{-5}$–$10^{-3}\rm \:M_\odot$. Our study highlights ASKAP’s ability to contribute to future radio observations for novae within a distance of 1 kpc hosted on white dwarfs with masses $0.4$–$1.25\:\rm M_\odot$, and within a distance of 4 kpc hosted on white dwarfs with masses $0.4$–$1.0\:\rm M_\odot$.

We assessed patterns of enteric infections caused by 14 pathogens, in a longitudinal cohort study of sequelae in British Columbia (BC) Canada, 2005–2014. Our population cohort of 5.8 million individuals was followed for an average of 7.5 years/person; during this time, 40 523 individuals experienced 42 308 incident laboratory-confirmed, provincially reported enteric infections (96.4 incident infections per 100 000 person-years). Most individuals (38 882/40 523; 96%) had only one, but 4% had multiple concurrent infections or more than one infection across the study. Among individuals with more than one infection, the pathogens and combinations occurring most frequently per individual matched the pathogens occurring most frequently in the BC population. An additional 298 557 new fee-for-service physician visits and hospitalisations for enteric infections, that did not coincide with a reported enteric infection, also occurred, and some may be potentially unreported enteric infections. Our findings demonstrate that sequelae risk analyses should explore the possible impacts of multiple infections, and that estimating risk for individuals who may have had a potentially unreported enteric infection is warranted.

The Variables and Slow Transients Survey (VAST) on the Australian Square Kilometre Array Pathfinder (ASKAP) is designed to detect highly variable and transient radio sources on timescales from 5 s to $\sim\!5$ yr. In this paper, we present the survey description, observation strategy and initial results from the VAST Phase I Pilot Survey. This pilot survey consists of $\sim\!162$ h of observations conducted at a central frequency of 888 MHz between 2019 August and 2020 August, with a typical rms sensitivity of $0.24\ \mathrm{mJy\ beam}^{-1}$ and angular resolution of $12-20$ arcseconds. There are 113 fields, each of which was observed for 12 min integration time, with between 5 and 13 repeats, with cadences between 1 day and 8 months. The total area of the pilot survey footprint is 5 131 square degrees, covering six distinct regions of the sky. An initial search of two of these regions, totalling 1 646 square degrees, revealed 28 highly variable and/or transient sources. Seven of these are known pulsars, including the millisecond pulsar J2039–5617. Another seven are stars, four of which have no previously reported radio detection (SCR J0533–4257, LEHPM 2-783, UCAC3 89–412162 and 2MASS J22414436–6119311). Of the remaining 14 sources, two are active galactic nuclei, six are associated with galaxies and the other six have no multi-wavelength counterparts and are yet to be identified.

This article contends that an important driver of turnout is the national stories embraced by citizens. We suggest the notion of ‘story incentive,’ whereby adopting a group’s story components – those that connect the past, the future, and prominent national characters – motivates individuals to participate in that group’s political activities. Leaning on narrative theories and studies on voter turnout, we develop and test hypotheses regarding the effect of story components on the likelihood of voting. Our measurements of story incentives are based on election surveys and encompass Denmark, Israel, the Netherlands, the UK, and the US. The results support the main story-incentive hypothesis. We discuss the theoretical ramifications of the connection between adherence to national stories and voter turnout.

We discuss observational strategies to detect prompt bursts associated with gravitational wave (GW) events using the Australian Square Kilometre Array Pathfinder (ASKAP). Many theoretical models of binary neutron stars mergers predict that bright, prompt radio emission would accompany the merger. The detection of such prompt emission would greatly improve our knowledge of the physical conditions, environment, and location of the merger. However, searches for prompt emission are complicated by the relatively poor localisation for GW events, with the 90% credible region reaching hundreds or even thousands of square degrees. Operating in fly’s eye mode, the ASKAP field of view can reach $\sim1\,000$ deg$^2$ at $\sim$$888\,{\rm MHz}$. This potentially allows observers to cover most of the 90% credible region quickly enough to detect prompt emission. We use skymaps for GW170817 and GW190814 from LIGO/Virgo’s third observing run to simulate the probability of detecting prompt emission for GW events in the upcoming fourth observing run. With only alerts released after merger, we find it difficult to slew the telescope sufficiently quickly as to capture any prompt emission. However, with the addition of alerts released before merger by negative-latency pipelines, we find that it should be possible to search for nearby, bright prompt fast radio burst-like emission from GW events. Nonetheless, the rates are low: we would expect to observe $\sim$0.012 events during the fourth observing run, assuming that the prompt emission is emitted microseconds around the merger.

We show that there exists a cubic threefold defined by an invertible polynomial that, when quotiented by the maximal diagonal symmetry group, has a derived category that does not have a full exceptional collection consisting of line bundles. This provides a counterexample to a conjecture of Lekili and Ueda.