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End date: 2021 × Author: Klein, Nadja × Start date: 2021 ×

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    bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
    Umlauf, N ; Klein, N ; Simon, T ; Zeileis, A (JOURNAL STATISTICAL SOFTWARE, 2021-11)
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    Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification
    Hafermann, L ; Becher, H ; Herrmann, C ; Klein, N ; Heinze, G ; Rauch, G (BMC, 2021-09-29)
    BACKGROUND: Statistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies. METHODS: We conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a "known" predictor if a predefined number of preceding studies identified it as relevant. RESULTS: Even if several preceding studies identified a variable as a "true" predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection. CONCLUSIONS: The source of "background knowledge" should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.
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    Bayesian Effect Selection in Structured Additive Distributional Regression Models
    Klein, N ; Carlan, M ; Kneib, T ; Lang, S ; Wagner, H (INT SOC BAYESIAN ANALYSIS, 2021-06)
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    Bayesian variable selection for non-Gaussian responses: a marginally calibrated copula approach
    Klein, N ; Smith, MS (WILEY, 2021-09)
    We propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non-Gaussian regression models. It uses a copula decomposition for the joint distribution of observations on the dependent variable. This allows the marginal distribution of the dependent variable to be calibrated accurately using a nonparametric or other estimator. The family of copulas employed are "implicit copulas" that are constructed from existing hierarchical Bayesian models widely used for variable selection, and we establish some of their properties. Even though the copulas are high dimensional, they can be estimated efficiently and quickly using Markov chain Monte Carlo. A simulation study shows that when the responses are non-Gaussian, the approach selects variables more accurately than contemporary benchmarks. A real data example in the Web Appendix illustrates that accounting for even mild deviations from normality can lead to a substantial increase in accuracy. To illustrate the full potential of our approach, we extend it to spatial variable selection for fMRI. Using real data, we show our method allows for voxel-specific marginal calibration of the magnetic resonance signal at over 6000 voxels, leading to an increase in the quality of the activation maps.