This paper introduces the Bradley-Terry regression trunk model, a novel probabilistic approach for the analysis of preference data expressed through paired comparison rankings. In some cases, it... Show moreThis paper introduces the Bradley-Terry regression trunk model, a novel probabilistic approach for the analysis of preference data expressed through paired comparison rankings. In some cases, it may be reasonable to assume that the preferences expressed by individuals depend on their characteristics. Within the framework of tree-based partitioning, we specify a tree-based model estimating the joint effects of subject-specific covariates over and above their main effects. We, therefore, combine a tree-based model and the log-linear Bradley-Terry model using the outcome of the comparisons as response variable. The proposed model provides a solution to discover interaction effects when no a-priori hypotheses are available. It produces a small tree, called trunk, that represents a fair compromise between a simple interpretation of the interaction effects and an easy to read partition of judges based on their characteristics and the preferences they have expressed. We present an application on a real dataset following two different approaches, and a simulation study to test the model's performance. Simulations showed that the quality of the model performance increases when the number of rankings and objects increases. In addition, the performance is considerably amplified when the judges' characteristics have a high impact on their choices. Show less
Renewed calls for decolonizing anthropology in the 21st century raise the question of what work earlier waves of decolonization since the 1960s have left undone. Some of this work should focus on... Show moreRenewed calls for decolonizing anthropology in the 21st century raise the question of what work earlier waves of decolonization since the 1960s have left undone. Some of this work should focus on the classification of human differences, which figured prominently in all phases of the discipline’s history: as a methodology in its racist phases, as an object of study during its late colonial phase of professionalization, as self-critical reflexivity in the 1980s and 1990s, and as a renewed critique in the 21st century. Can a universal methodology of studying classifications of human kinds arise from the discipline’s past of colonial stereotyping? I argue affirmatively, through an approach that recognizes time as the epistemic condition that connects past and present positions to present and future methodologies. Firstly, my analysis distinguishes the parochial embedding in colonial culture of Durkheim and Mauss’ ideas about classification from their more universal intentions. This is then developed into a threefold reflexive and timeful methodology of studying classification’s nominal-descriptive, constructive, and interventionist dimensions—a process of adding temporality to the study of classification. Subsequently, Antenor Firmin’s 19th-century critique of racial classifications, and W. E. B. Du Bois’s theory of double consciousness help to show how this threefold methodology addresses the insufficiently theorized process of being classified and discriminated against through racial categories wielded by the powers that be. These arguments radicalize the essay’s timeful perspective by concluding that we need to avoid modernist uses of time as classification and adopt the aforementioned threefold methodology in order to put time in classifications of human kinds. This reverses modern positivism’s subordination to methodological rules of the epistemic conditions posed by contingent history and shows instead that the universal goals of methodology should be understood as a future ideal. Show less
Vos, F. de; Koini, M.; Schouten, T.M.; Seiler, S.; Van der Grond, J.; Lechner, A.; ... ; Rombouts, S.A.R.B. 2017
Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI... Show moreAlzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly. Show less
Schouten, T.M.; Koini, M.; Vos, F. de; Seiler, S.; Rooij, M.J. de; Lechner, A.; ... ; Rombouts, S.A.R.B. 2017
