Reliable population estimates are one of the most elementary needs for the management of wildlife, particularly for introduced ungulates on oceanic islands. We aimed to produce accurate and precise density estimates of Philippine deer (Rusa marianna) and wild pigs (Sus scrofa) on Guam using motion-triggered cameras combined with distance sampling to estimate densities from observations of unmarked animals while accounting for imperfect detection. We used an automated digital data processing pipeline for species recognition and to estimate the distance to detected species. Our density estimates were slightly lower than published estimates, consistent with management to reduce populations. We estimated the number of camera traps needed to obtain a 0.1 coefficient of variation was substantial, requiring > ten-fold increase in camera traps, while estimates with precision of 0.2 or 0.3 were more achievable, requiring doubling to quadrupling the number of camera traps. We provide best practices for establishing and conducting distance sampling with camera trap surveys for density estimation based on lessons learned during this study. Future studies should consider distance sampling with camera traps to efficiently survey and monitor unmarked animals, particularly medium-sized ungulates, in tropical, oceanic island ecosystems.

Glacier ice flux is a key indicator of mass balance; therefore, accurate monitoring of ice dynamics is essential. Satellite-based methods are widely used for glacier velocity measurements but are limited by satellite revisit frequency. This study explores using seismic station internal GPS data to track glacier movement. While less accurate than differential GPS, this method offers high-temporal resolution as a by-product where seismic stations are deployed. Using a seismic station on Borebreen, Svalbard, we show that internal GPS provides reliable surface velocity measurements. When compared with satellite-inferred velocities, the results show a strong correlation, suggesting that the internal GPS, despite its inherent uncertainty, can serve as an efficient tool for glacier velocity monitoring. The high-temporal sampling reveals short-term dynamics of speed-up events and underscores the role of meltwater in driving these processes. This approach augments glacier observation networks at no additional cost.

This article investigates the use of standard econometric models for quantal choice to study equilibria of extensive form games. Players make choices based on a quantal-choice model and assume other players do so as well. We define an agent quantal response equilibrium (AQRE), which applies QRE to the agent normal form of an extensive form game and imposes a statistical version of sequential rationality. We also define a parametric specification, called logit-AQRE, in which quantal-choice probabilities are given by logit response functions. AQRE makes predictions that contradict the invariance principle in systematic ways. We show that these predictions match up with some experimental findings by Schotter et al. (1994) about the play of games that differ only with respect to inessential transformations of the extensive form. The logit-AQRE also implies a unique selection from the set of sequential equilibria in generic extensive form games. We examine data from signaling game experiments by Banks et al. (1994) and Brandts and Holt(1993). We find that the logit-AQRE selection applied to these games succeeds in predicting patterns of behavior observed in these experiments, even when our prediction conflicts with more standard equilibrium refinements, such as the intuitive criterion. We also reexamine data from the McKelvey and Palfrey (1992) centipede experiment and find that the AQRE model can account for behavior that had previously been explained in terms of altruistic behavior.

We investigate, in an experimental setting, the effect of private information on the Coase theorem's predictions of efficiency and allocative neutrality. For a two-person bargaining game, we find significantly more inefficiency and allocative bias in the case of private information compared with the case of complete information. We also find substantial bargaining breakdown, which is not predicted by the Coase theorem. For the case of private information, we reject the Coase theorem in favor of the alternative of a generalized version of the Myerson- Satterthwaite theorem, which predicts inefficiency, allocative bias in the direction of the disagreement point, and some bargaining breakdown.

Converting knowledge from basic research into innovations that improve clinical care requires a specialized workforce that converts a laboratory invention into a product that can be developed and tested for clinical use. As the mandate to demonstrate more real-world impact from the national investment in research continues to grow, the demand for staff that specialize in product development and clinical trials continues to outpace supply. In this study, two academic medical institutions in the greater Houston–Galveston region termed this population the “bridge and clinical research professional” (B + CRP) workforce and assessed its turnover before and after the onset of the COVID-19 pandemic . Both institutions realized growth (1.2 vs 2.3-fold increase) in B + CRP-specific jobs from 2017 to 2022. Turnover increased 1.5–2-fold after the onset of the pandemic but unlike turnover in the larger clinical and translational research academic workforce, the instability did not resolve by 2022. These results are a baseline measurement of the instability of our regional B + CRP workforce and have informed the development of a regional alliance of universities, academic medical centers, and economic development organizations in the greater Houston–Galveston region to increase this highly specialized and skilled candidate pool.

Psychologists and other behavioral scientists are frequently interested in whether a questionnaire measures a latent construct. Attempts to address this issue are referred to as construct validation. We describe and extend nonparametric hypothesis testing procedures to assess matrix structures, which can be used for construct validation. These methods are based on a quadratic assignment framework and can be used either by themselves or to check the robustness of other methods. We investigate the performance of these matrix structure tests through simulations and demonstrate their use by analyzing a big five personality traits questionnaire administered as part of the Health and Retirement Study. We also derive rates of convergence for our overall test to better understand its behavior.

We present a cognitive process model of response choice and response time performance data that has excellent psychometric properties and may be used in a wide variety of contexts. In the model there is an accumulator associated with each response option. These accumulators have bounds, and the first accumulator to reach its bound determines the response time and response choice. The times at which accumulator reaches its bound is assumed to be lognormally distributed, hence the model is race or minima process among lognormal variables. A key property of the model is that it is relatively straightforward to place a wide variety of models on the logarithm of these finishing times including linear models, structural equation models, autoregressive models, growth-curve models, etc. Consequently, the model has excellent statistical and psychometric properties and can be used in a wide range of contexts, from laboratory experiments to high-stakes testing, to assess performance. We provide a Bayesian hierarchical analysis of the model, and illustrate its flexibility with an application in testing and one in lexical decision making, a reading skill.

The subject of factor indeterminacy has a vast history in factor analysis (Guttman, 1955; Lederman, 1938; Wilson, 1928). It has lead to strong differences in opinion (Steiger, 1979). The current paper gives necessary and sufficient conditions for observability of factors in terms of the parameter matrices and a finite number of variables. Five conditions are given which rigorously define indeterminacy. It is shown that (un)observable factors are (in)determinate. Specifically, the indeterminacy proof by Guttman is extended to Heywood cases. The results are illustrated by two examples and implications for indeterminacy are discussed.

Human abilities in perceptual domains have conventionally been described with reference to a threshold that may be defined as the maximum amount of stimulation which leads to baseline performance. Traditional psychometric links, such as the probit, logit, and t, are incompatible with a threshold as there are no true scores corresponding to baseline performance. We introduce a truncated probit link for modeling thresholds and develop a two-parameter IRT model based on this link. The model is Bayesian and analysis is performed with MCMC sampling. Through simulation, we show that the model provides for accurate measurement of performance with thresholds. The model is applied to a digit-classification experiment in which digits are briefly flashed and then subsequently masked. Using parameter estimates from the model, individuals’ thresholds for flashed-digit discrimination is estimated.

Experiments in engineering are typically conducted in controlled environments where parameters can be set to any desired value. This assumes that the same applies in a real-world setting, which is often incorrect as many experiments are influenced by uncontrollable environmental conditions such as temperature, humidity, and wind speed. When optimizing such experiments, the focus should be on finding optimal values conditionally on these uncontrollable variables. This article extends Bayesian optimization to the optimization of systems in changing environments that include controllable and uncontrollable parameters. The extension fits a global surrogate model over all controllable and environmental variables but optimizes only the controllable parameters conditional on measurements of the uncontrollable variables. The method is validated on two synthetic test functions, and the effects of the noise level, the number of environmental parameters, the parameter fluctuation, the variability of the uncontrollable parameters, and the effective domain size are investigated. ENVBO, the proposed algorithm from this investigation, is applied to a wind farm simulator with eight controllable and one environmental parameter. ENVBO finds solutions for the entire domain of the environmental variable that outperform results from optimization algorithms that only focus on a fixed environmental value in all but one case while using a fraction of their evaluation budget. This makes the proposed approach very sample-efficient and cost-effective. An off-the-shelf open-source version of ENVBO is available via the NUBO Python package.