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ItemMitigating spatial confounding by explicitly correlating Gaussian random fields(Wiley, 2022-08-01)Abstract Spatial models are used in a variety of research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in such spatial regression models is spatial confounding. This phenomenon is observed when spatially indexed covariates modeling the mean of the response are correlated with a spatial random effect included in the model, for example, as a proxy of unobserved spatial confounders. As a result, estimates for regression coefficients of the covariates can be severely biased and interpretation of these is no longer valid. Recent literature has shown that typical solutions for reducing spatial confounding can lead to misleading and counterintuitive results. In this article, we develop a computationally efficient spatial model that explicitly correlates a Gaussian random field for the covariate of interest with the Gaussian random field in the main model equation and integrates novel prior structures to reduce spatial confounding. Starting from the univariate case, we extend our prior structure also to the case of multiple spatially confounded covariates. In simulation studies, we show that our novel model flexibly detects and reduces spatial confounding in spatial datasets, and it performs better than typically used methods such as restricted spatial regression. These results are promising for any applied researcher who wishes to interpret covariate effects in spatial regression models. As a real data illustration, we study the effect of elevation and temperature on the mean of monthly precipitation in Germany.
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ItemThe P-Word: Power aversion and responsibility aversion as explanations for the avoidance of power(WILEY, 2022-02-12)Abstract Though we typically think that power is desirable, individuals will sometimes avoid power. One explanation for this behavior is some individuals are averse to the responsibility associated with power and will therefore avoid positions of power. However, people may also avoid power because they perceive it as being inherently negative. This is supported by research on lay theories of power, which suggests that those who endorse the coercive lay theory perceive powerful people as manipulative and deceitful. In this paper, we propose a new theory of power aversion that expands upon the coercive lay theory to more thoroughly explain how negative perceptions of power cause some individuals to avoid it. We draw from previous research to identify specific negative traits associated with power. Based on this, we propose that some power‐averse individuals believe that possessing power will turn them into immoral, cold, selfish, and unjust people. For this reason, they avoid power. We also consider the relationship between power aversion and responsibility aversion and suggest a convergence between research on responsibility aversion and lay theories of power.
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ItemVariational inference and sparsity in high-dimensional deep Gaussian mixture models(SPRINGER, 2022-10)Abstract Gaussian mixture models are a popular tool for model-based clustering, and mixtures of factor analyzers are Gaussian mixture models having parsimonious factor covariance structure for mixture components. There are several recent extensions of mixture of factor analyzers to deep mixtures, where the Gaussian model for the latent factors is replaced by a mixture of factor analyzers. This construction can be iterated to obtain a model with many layers. These deep models are challenging to fit, and we consider Bayesian inference using sparsity priors to further regularize the estimation. A scalable natural gradient variational inference algorithm is developed for fitting the model, and we suggest computationally efficient approaches to the architecture choice using overfitted mixtures where unnecessary components drop out in the estimation. In a number of simulated and two real examples, we demonstrate the versatility of our approach for high-dimensional problems, and demonstrate that the use of sparsity inducing priors can be helpful for obtaining improved clustering results.
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ItemA non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes(Springer Science and Business Media LLC, 2022-10-01)Abstract Circular data can be found across many areas of science, for instance meteorology (e.g., wind directions), ecology (e.g., animal movement directions), or medicine (e.g., seasonality in disease onset). The special nature of these data means that conventional methods for non-periodic data are no longer valid. In this paper, we consider wrapped Gaussian processes and introduce a spatial model for circular data that allow for non-stationarity in the mean and the covariance structure of Gaussian random fields. We use the empirical equivalence between Gaussian random fields and Gaussian Markov random fields which allows us to considerably reduce computational complexity by exploiting the sparseness of the precision matrix of the associated Gaussian Markov random field. Furthermore, we develop tunable priors, inspired by the penalized complexity prior framework, that shrink the model toward a less flexible base model with stationary mean and covariance function. Posterior estimation is done via Markov chain Monte Carlo simulation. The performance of the model is evaluated in a simulation study. Finally, the model is applied to analyzing wind directions in Germany.
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ItemThe rise of 'smart' solutions in Africa: a review of the socio-environmental cost of the transportation and employment benefits of ride-hailing technology in Ghana.(Springer Science and Business Media LLC, 2022)Governments in Africa are licensing major global ride-hailing firms to launch operations in the continent. This is often presented as a refreshing development for the continent to leverage technology to address its twin problems of inefficient urban transport and rising youth unemployment. Interviews with ride-hailing adopters (drivers, riders, and car owners) and researchers in Ghana suggest, however, that whereas the technology is driving up the standards of road transport experience, the benefits are accessible to a select few (largely, the younger, highly educated and relatively high income-earning class). The lopsided power relations underlying the ride-hailing industry have also meant that the economic opportunities it avails disproportionately benefit a few powerful players (e.g. ride-hailing firms and car owners) while stimulating 'turf wars' among online and traditional taxi drivers; deepening existing gender inequalities in access to income-earning opportunities in the commercial passenger transport sector; encouraging unhealthy driving practices, shifts from shared public transport, and inundation of the roads with more private cars. While it will be imprecise to say that the private gains of ride-hailing outstrip the public costs and, therefore, the technology is detrimental to Ghana's development, the considered evidence raises the need for sustained scrutiny of the hailing of technological interventions as though they are the magic bullets for socio-economic transformation in Africa. Overall, the paper argues that dismantling the power structures underlying Africa's urban challenges will require more than splashing 'smart' apps and other tech wizardries around. Indeed, the lessons from Ghana's ride-hailing industry suggest that such exclusively technical solutions could easily take root and pattern after existing strictures of unjust power structures in ways that could exacerbate the social and environmental problems they are supposed to address.
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ItemIs age at menopause decreasing? - The consequences of not completing the generational cohort.(Springer Science and Business Media LLC, 2022-07-11)BACKGROUND: Due to contradictory results in current research, whether age at menopause is increasing or decreasing in Western countries remains an open question, yet worth studying as later ages at menopause are likely to be related to an increased risk of breast cancer. Using data from breast cancer screening programs to study the temporal trend of age at menopause is difficult since especially younger women in the same generational cohort have often not yet reached menopause. Deleting these younger women in a breast cancer risk analyses may bias the results. The aim of this study is therefore to recover missing menopause ages as a covariate by comparing methods for handling missing data. Additionally, the study makes a contribution to understanding the evolution of age at menopause for several generations born in Portugal between 1920 and 1970. METHODS: Data from a breast cancer screening program in Portugal including 278,282 women aged 45-69 and collected between 1990 and 2010 are used to compare two approaches of imputing age at menopause: (i) a multiple imputation methodology based on a truncated distribution but ignoring the mechanism of missingness; (ii) a copula-based multiple imputation method that simultaneously handles the age at menopause and the missing mechanism. The linear predictors considered in both cases have a semiparametric additive structure accommodating linear and non-linear effects defined via splines or Markov random fields smoothers in the case of spatial variables. RESULTS: Both imputation methods unveiled an increasing trend of age at menopause when viewed as a function of the birth year for the youngest generation. This trend is hidden if we model only women with an observed age at menopause. CONCLUSION: When studying age at menopause, missing ages must be recovered with an adequate procedure for incomplete data. Imputing these missing ages avoids excluding the younger generation cohort of the screening program in breast cancer risk analyses and hence reduces the bias stemming from this exclusion. In addition, imputing the not yet observed ages of menopause for mostly younger women is also crucial when studying the time trend of age at menopause otherwise the analysis will be biased.
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ItemUsing Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study(MDPI, 2022-06)There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.
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ItemOnline community's recognition and continued participation in idea competitions(Wiley, 2022-07-20)This study examines the effect of online community's recognition on continued participation in idea competitions, and how personal winning record moderates such an influence. We reason that the motivating role of community recognition might either be reinforced or substituted by personal winning record, depending upon whether relational motives (psychological and social bonding) or individualistic motives (personal benefits, such as status and career enhancement) are the primary behavior driver. Through an event history analysis of data obtained from a platform of creative design contests, we find that although community recognition exerts a positive effect on the rate of continued participation for designers who are yet to win any competitions, this effect increasingly turns negative for designers who have won. Such findings indicate that the motivating role of community recognition might be substituted instead of reinforced by personal winning record, lending support to the individualistic view while rejecting the relational view. Although virtual social spaces represent an important means for modern competition platforms to attract and motivate participants, our study informs practitioners about online community's limitation in retaining “star” participants.
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ItemNo Preview Availablebamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)(JOURNAL STATISTICAL SOFTWARE, 2021-11)
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ItemBehavioural Agency and Firm Productivity: Revisiting the Incentive Alignment Qualities of Stock Options(WILEY, 2022-11-01)Drawing on behavioural agency theory, we revisit the incentive alignment qualities of stock options. Using behavioural agency’s logic, we theorize that chief executive officers (CEOs) are likely to perceive efforts directed at firm productivity as a means of protecting their option wealth (the value of previously awarded stock options). Our reasoning suggests that CEO option wealth positively influences firm productivity and that productivity mediates the relationship between CEO option wealth and firm value. Our theory also points to boundary conditions at the CEO level and the firm level. Our study advances research on the utility of stock options by focusing on effort and productivity as the mechanism through which option incentives affect CEO behaviours. We demonstrate that option risk bearing can align CEO–shareholder interests.
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