This paper proposes a method for assessing differential item functioning (DIF) in item response theory (IRT) models. The method does not require pre-specification of anchor items, which is its main virtue. It is developed in two main steps: first by showing how DIF can be re-formulated as a problem of outlier detection in IRT-based scaling and then tackling the latter using methods from robust statistics. The proposal is a redescending M-estimator of IRT scaling parameters that is tuned to flag items with DIF at the desired asymptotic type I error rate. Theoretical results describe the efficiency of the estimator in the absence of DIF and its robustness in the presence of DIF. Simulation studies show that the proposed method compares favorably to currently available approaches for DIF detection, and a real data example illustrates its application in a research context where pre-specification of anchor items is infeasible. The focus of the paper is the two-parameter logistic model in two independent groups, with extensions to other settings considered in the conclusion.

The incorporation of sex and gender (S/G) related factors is commonly acknowledged as a necessary step to advance towards more personalized diagnoses and treatments for somatic, psychiatric, and neurological diseases. Until now, most attempts to integrate S/G-related factors have been reduced to identifying average differences between females and males in behavioral/ biological variables. The present commentary questions this traditional approach by highlighting three main sets of limitations: 1) Issues stemming from the use of classic parametric methods to compare means; 2) challenges related to the ability of means to accurately represent the data within groups and differences between groups; 3) mean comparisons impose a results\' binarization and a binary theoretical framework that precludes advancing towards precision medicine. Alternative methods free of these limitations are also discussed. We hope these arguments will contribute to reflecting on how research on S/G factors is conducted and could be improved.

In this work, we present the water quality assessment of an urban river, the San Luis River, located in San Luis Province, Argentina. The San Luis River flows through two developing cities; hence, urban anthropic activities affect its water quality. The river was sampled spatially and temporally, evaluating ten physicochemical variables on each water sample. These data were used to calculate a Simplified Index of Water Quality in order to estimate river water quality and infer possible contamination sources. Data were statistically analyzed with the opensource software R, 4.1.0 version. Principal component analysis, cluster analysis, correlation matrices, and heatmap analysis were performed. Results indicated that water quality decreases in areas where anthropogenic activities take place. Robust inferential statistical analysis was performed, employing an alternative of multivariate analysis of variance (MANOVA), MANOVA.wide function. The most statistically relevant physicochemical variables associated with water quality decrease were used to develop a multiple linear regression model to estimate organic matter, reducing the variables necessary for continuous monitoring of the river and, hence, reducing costs. Given the limited information available in the region about the characteristics and recovery of this specific river category, the model developed is of vital importance since it can quickly detect anthropic alterations and contribute to the environmental management of the rivers. This model was also used to estimate organic matter at sites located in other similar rivers, obtaining satisfactory results.

Data augmentation is an effective technique for automatically expanding training data in deep learning. Brain-inspired methods are approaches that draw inspiration from the functionality and structure of the human brain and apply these mechanisms and principles to artificial intelligence and computer science. When there is a large style difference between training data and testing data, common data augmentation methods cannot effectively enhance the generalization performance of the deep model. To solve this problem, we improve modeling Domain Shifts with Uncertainty (DSU) and propose a new brain-inspired computer vision image data augmentation method which consists of two key components, namely, using Robust statistics and controlling the Coefficient of variance for DSU (RCDSU) and Feature Data Augmentation (FeatureDA). RCDSU calculates feature statistics (mean and standard deviation) with robust statistics to weaken the influence of outliers, making the statistics close to the real values and improving the robustness of deep learning models. By controlling the coefficient of variance, RCDSU makes the feature statistics shift with semantic preservation and increases shift range. FeatureDA controls the coefficient of variance similarly to generate the augmented features with semantics unchanged and increase the coverage of augmented features. RCDSU and FeatureDA are proposed to perform style transfer and content transfer in the feature space, and improve the generalization ability of the model at the style and content level respectively. On Photo, Art Painting, Cartoon, and Sketch (PACS) multi-style classification task, RCDSU plus FeatureDA achieves competitive accuracy. After adding Gaussian noise to PACS dataset, RCDSU plus FeatureDA shows strong robustness against outliers. FeatureDA achieves excellent results on CIFAR-100 image classification task. RCDSU plus FeatureDA can be applied as a novel brain-inspired semantic data augmentation method with implicit robot automation which is suitable for datasets with large style differences between training and testing data.

Different algorithms, such as the Savitzky-Golay filter and Whittaker smoother, have been proposed to improve the quality of experimental chromatograms. These approaches avoid excessive noise from hampering data analysis and as such allow an accurate detection and quantification of analytes. These algorithms require fine-tuning of their hyperparameters to regulate their roughness and flexibility. Traditionally, this fine-tuning is done manually until a signal is obtained that removes the noise while conserving valuable peak information. More objective and automated approaches are available, but these are usually method specific and/or require previous knowledge. In this work, the L-and V-curve, k-fold cross-validation, autocorrelation function and residual variance estimation approach are introduced as alternative automated and generally applicable parameter tuning methods. These methods do not require any previous information and are compatible with a multitude of denoising methods. Additionally, for k-fold cross-validation, autocorrelation function and residual variance estimation, a novel implementation based on median estimators is proposed to handle the specific shape of chromatograms, typically composed of alternating flat baselines and sharp peaks. These tuning methods are investigated in combination with four denoising methods; the Savitsky-Golay filter, Whittaker smoother, sparsity assisted signal smoother and baseline estimation and denoising using sparsity approach. It is demonstrated that the median estimators approach significantly improves the denoising and information conservation performance of relevant smoother-tuner combinations up to a factor 4 for simulated datasets and even up to a factor 10 for an experimental chromatogram. Moreover, the parameter tuning methods relying on residual variance estimation, k-fold cross-validation and autocorrelation function lead to similar small root-mean squared errors on the different simulated datasets and experimental chromatograms. The sparsity assisted signal smoother and baseline estimation and denoising using sparsity approach, which both rely on the use of sparsity, systematically outperform the two other methods and are hence most appropriate for chromatograms.

The term robustness in statistics refers to methods that are generally insensitive to deviations from model assumptions. In other words, robust methods are able to preserve their accuracy even when the data do not perfectly fit the statistical models. Robust statistical analyses are particularly effective when analysing mixtures of probability distributions. Therefore, these methods enable the discretization of X-ray serial crystallography data into two probability distributions: a group comprising true data points (for example the background intensities) and another group comprising outliers (for example Bragg peaks or bad pixels on an X-ray detector). These characteristics of robust statistical analysis are beneficial for the ever-increasing volume of serial crystallography (SX) data sets produced at synchrotron and X-ray free-electron laser (XFEL) sources. The key advantage of the use of robust statistics for some applications in SX data analysis is that it requires minimal parameter tuning because of its insensitivity to the input parameters. In this paper, a software package called Robust Gaussian Fitting library (RGFlib) is introduced that is based on the concept of robust statistics. Two methods are presented based on the concept of robust statistics and RGFlib for two SX data-analysis tasks: (i) a robust peak-finding algorithm and (ii) an automated robust method to detect bad pixels on X-ray pixel detectors.

Viable methods for the identification of item misfit or Differential Item Functioning (DIF) are central to scale construction and sound measurement. Many approaches rely on the derivation of a limiting distribution under the assumption that a certain model fits the data perfectly. Typical DIF assumptions such as the monotonicity and population independence of item functions are present even in classical test theory but are more explicitly stated when using item response theory or other latent variable models for the assessment of item fit. The work presented here provides a robust approach for DIF detection that does not assume perfect model data fit, but rather uses Tukey\'s concept of contaminated distributions. The approach uses robust outlier detection to flag items for which adequate model data fit cannot be established.

The space of possible human cultures is vast, but some cultural configurations are more consistent with cognitive and social constraints than others. This leads to a \"landscape\" of possibilities that our species has explored over millennia of cultural evolution. However, what does this fitness landscape, which constrains and guides cultural evolution, look like? The machine-learning algorithms that can answer these questions are typically developed for large-scale datasets. Applications to the sparse, inconsistent, and incomplete data found in the historical record have received less attention, and standard recommendations can lead to bias against marginalized, under-studied, or minority cultures. We show how to adapt the minimum probability flow algorithm and the Inverse Ising model, a physics-inspired workhorse of machine learning, to the challenge. A series of natural extensions-including dynamical estimation of missing data, and cross-validation with regularization-enables reliable reconstruction of the underlying constraints. We demonstrate our methods on a curated subset of the Database of Religious History: records from 407 religious groups throughout human history, ranging from the Bronze Age to the present day. This reveals a complex, rugged, landscape, with both sharp, well-defined peaks where state-endorsed religions tend to concentrate, and diffuse cultural floodplains where evangelical religions, non-state spiritual practices, and mystery religions can be found.

We evaluated the error detection performance of the DetectDeviatingCells (DDC) algorithm which flags data anomalies at observation (casewise) and variable (cellwise) level in continuous variables. We compared its performance to other approaches in a simulated dataset.
We simulated height and weight data for hypothetical individuals aged 2-20 years. We changed a proportion of height values according to predetermined error patterns. We applied the DDC algorithm and other error-detection approaches (descriptive statistics, plots, fixed-threshold rules, classic, and robust Mahalanobis distance) and we compared error detection performance with sensitivity, specificity, likelihood ratios, predictive values, and receiver operating characteristic (ROC) curves.
At our chosen thresholds error detection specificity was excellent across all scenarios for all methods and sensitivity was higher for multivariable and robust methods. The DDC algorithm performance was similar to other robust multivariable methods. Analysis of ROC curves suggested that all methods had comparable performance for gross errors (e.g., wrong measurement unit), but the DDC algorithm outperformed the others for more complex error patterns (e.g., transcription errors that are still plausible, although extreme).
The DDC algorithm has the potential to improve error detection processes for observational data.

UNASSIGNED: Postpartum depression can take many forms. Different symptom patterns could have divergent implications for how we screen, diagnose, and treat postpartum depression. We sought to utilise a recently developed robust estimation algorithm to automatically identify differential patterns in depressive symptoms and subsequently characterise the individuals who exhibit different patterns.
UNASSIGNED: Depressive symptom data (N = 548) were collected from women with neuropsychiatric illnesses at two U.S. urban sites participating in a longitudinal observational study of stress across the perinatal period. Data were collected from Emory University between 1994 and 2012 and from the University of Arkansas for Medical Sciences between 2012 and 2017. We conducted an exploratory factor analysis of Beck Depression Inventory (BDI) items using a robust expectation-maximization algorithm, rather than a conventional expectation-maximization algorithm. This recently developed method enabled automatic detection of differential symptom patterns. We described differences in symptom patterns and conducted unadjusted and adjusted analyses of associations of symptom patterns with demographics and psychiatric histories.
UNASSIGNED: 53 (9.7%) participants were identified by the algorithm as having a different pattern of reported symptoms compared to other participants. This group had more severe symptoms across all items-especially items related to thoughts of self-harm and self-judgement-and differed in how their symptoms related to underlying psychological constructs. History of social anxiety disorder (OR: 4.0; 95% CI [1.9, 8.1]) and history of childhood trauma (for each 5-point increase, OR: 1.2; 95% CI [1.1, 1.3]) were significantly associated with this differential pattern after adjustment for other covariates.
UNASSIGNED: Social anxiety disorder and childhood trauma are associated with differential patterns of severe postpartum depressive symptoms, which might warrant tailored strategies for screening, diagnosis, and treatment to address these comorbid conditions.
UNASSIGNED: There are no funding sources to declare.