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Objective Discrete but ordered covariates are quite common in applied statistics, and some regularized fitting procedures have been proposed for proper handling of ordinal predictors in statistical models. Motivated by a study from neonatal medicine on Bronchopulmonary Dysplasia (BPD), we show how quadratic penalties on adjacent dummy coefficients of ordinal factors proposed in the literature can be incorporated in the framework of generalized additive models, making tools for statistical inference developed there available for ordinal predictors as well. Results The approach presented allows to exploit the scale level of ordinally scaled factors in a sound statistical framework. Furthermore, several ordinal factors can be considered jointly without the need to collapse levels even if the number of observations per level is small. By doing so, results obtained earlier on the BPD data analyzed could be confirmed.

Structural health monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an “input–output” and an “output-only” method.

Shrinking methods in regression analysis are usually designed for metric predictors. In this article, however, shrinkage methods for categorial predictors are proposed. As an application we consider data from the Munich rent standard, where, for example, urban districts are treated as a categorial predictor. If independent variables are categorial, some modifications to usual shrinking procedures are necessary. Two L₁-penalty based methods for factor selection and clustering of categories are presented and investigated. The first approach is designed for nominal scale levels, the second one for ordinal predictors. Besides applying them to the Munich rent standard, methods are illustrated and compared in simulation studies.

Aberrant microbial colonization of premature infants is increasingly recognized as a risk factor for severe acute morbidities. The aim of this study was to evaluate the correlation of bacterial upper airway colonization within the first 6 weeks of life in preterm infants <1000g and risk of moderate/severe bronchopulmonary dysplasia (BPD). In this retrospective two-center cohort study postnatal upper airway bacterial colonization of premature infants with a birth weight <1000g was analyzed. Bacteria were categorized into facultative- and highly pathogenic. Within 242 infants, a birth weight cutoff of 800g prevailed as the most relevant discriminator for risk of BPD. Furthermore, center, male sex, duration of antibiotic therapy, and delayed detection of facultative pathogenic bacteria after week 4 was associated with the development of BPD. Using classification tree analyses for the binary outcome, antibiotic therapy was more importance in infants <800g, whereas in those with a birth weight ≥800g, delayed colonization with facultative pathogenic bacteria was more relevant than antibiotic exposure. We add delayed colonization of the upper airway with facultative pathogenic bacteria to the risks for BPD. The variations of microbial colonization should be considered in future studies on the pathogenesis of BPD and new treatment modalities.

While recent studies suggest an influence of noise on olfactory performance, it is unclear as to what extent the influence varies between subjects who are accustomed to noise and those who are not. Two groups of panelists were selected: a University panel usually working under silent conditions and an abattoir panel usually working on the slaughter line with abattoir noise. Odor discrimination, odor identification, and odor detection thresholds were studied. Furthermore, a sensory quality control task using 40 boar samples was performed. All tests were accomplished both with and without extraneous noise recorded at an abattoir (70 dB) using headphones. Contrary to the researchers' expectations, abattoir noise hardly affected the olfactory tests nor was the quality control task impaired. Abattoir noise did not influence the perceived intensity of boar taint and the classification results of the testers, regardless of whether they were accustomed to such noise or not. The results indicate that sensory quality control can be conducted in a manufacturing environment with constant noise without diminishing the assessors' performance.

[This corrects the article DOI: 10.1371/journal.pone.0174697.].

The so-called proportional odds assumption is popular in cumulative, ordinal regression. In practice, however, such an assumption is sometimes too restrictive. For instance, when modeling the perception of boar taint on an individual level, it turns out that, at least for some subjects, the effects of predictors (androstenone and skatole) vary between response categories. For more flexible modeling, we consider the use of a ‘smooth-effects-on-response penalty’ (SERP) as a connecting link between proportional and fully non-proportional odds models, assuming that parameters of the latter vary smoothly over response categories. The usefulness of SERP is further demonstrated through a simulation study. Besides flexible and accurate modeling, SERP also enables fitting of parameters in cases where the pure, unpenalized non-proportional odds model fails to converge.

We consider nonparametric prediction with multiple covariates, in particular categorical or functional predictors, or a mixture of both. The method proposed bases on an extension of the Nadaraya-Watson estimator where a kernel function is applied on a linear combination of distance measures each calculated on single covariates, with weights being estimated from the training data. The dependent variable can be categorical (binary or multi-class) or continuous, thus we consider both classification and regression problems. The methodology presented is illustrated and evaluated on artificial and real world data. Particularly it is observed that prediction accuracy can be increased, and irrelevant, noise variables can be identified/removed by ‘downgrading’ the corresponding distance measures in a completely data-driven way.

The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.

In its simplest case, ANOVA can be seen as a generalization of the t-test for comparing the means of a continuous variable in more than two groups defined by the levels of a discrete covariate, a so-called factor. Testing is then typically done by using the standard F-test. Here, we consider the special but frequent case of factor levels that are ordered. We propose an alternative test using mixed models methodology. The new test often outperforms the standard F-test when factor levels are ordered. We illustrate the proposed testing procedure in simulation studies and three typical applications: nonparametric dose response analysis in agriculture, associations between rating scales and a continuous outcome, and testing differentially expressed genes with ordinal phenotypes.