Title Participants "Independent risk factors for the development of skin erosion due to incontinence (incontinence-associated dermatitis category 2) in nursing home residents : results from a multivariate binary regression analysis" "Nele Van Damme, Karen Van den Bussche, Dorien De Meyer, Ann Van Hecke, Dimitri Beeckman" "Association of BMI category change with TB treatment mortality in HIV-positive smear-negative and extrapulmonary TB patients in Myanmar and Zimbabwe" "Lenka Benova, Katherine Fielding, Jane Greig, Bern-Thomas Nyang'wa, Esther Carrillo Casas, Marcio Silveira da Fonseca, Philipp du Cros" "OBJECTIVE: The HIV epidemic has increased the proportion of patients with smear-negative and extrapulmonary tuberculosis (TB) diagnoses, with related higher rates of poor TB treatment outcomes. Unlike in smear-positive pulmonary TB, no interim markers of TB treatment progress are systematically used to identify individuals most at risk of mortality. The objective of this study was to assess the association of body mass index (BMI) change at 1 month (±15 days) from TB treatment start with mortality among HIV-positive individuals with smear-negative and extrapulmonary TB.METHODS AND FINDINGS: A retrospective cohort study of adult HIV-positive new TB patients in Médecins Sans Frontières (MSF) treatment programmes in Myanmar and Zimbabwe was conducted using Cox proportional hazards regression to estimate the association between BMI category change and mortality. A cohort of 1090 TB patients (605 smear-negative and 485 extrapulmonary) was followed during TB treatment with mortality rate of 28.9 per 100 person-years. In multivariable analyses, remaining severely underweight or moving to a lower BMI category increased mortality (adjusted hazard ratio 4.05, 95% confidence interval 2.77-5.91, p" "Lexical category acquisition is facilitated by uncertainty in distributional co-occurrences" "Giovanni Cassani, Robert Grimm" "This paper analyzes distributional properties that facilitate the categorization of words into lexical categories. First, word-context co-occurrence counts were collected using corpora of transcribed English child-directed speech. Then, an unsupervised k-nearest neighbor algorithm was used to categorize words into lexical categories. The categorization outcome was regressed over three main distributional predictors computed for each word, including frequency, contextual diversity, and average conditional probability given all the co-occurring contexts. Results show that both contextual diversity and frequency have a positive effect while the average conditional probability has a negative effect. This indicates that words are easier to categorize in the face of uncertainty: categorization works best for words which are frequent, diverse, and hard to predict given the co-occurring contexts. This shows how, in order for the learner to see an opportunity to form a category, there needs to be a certain degree of uncertainty in the co-occurrence pattern." "The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions" "Paul De Boeck" "The Graded Response Model (GRM; Samejima, Estimation of ability using a response pattern of graded scores, Psychometric Monograph No. 17, Richmond, VA: The Psychometric Society, 1969) can be derived by assuming a linear regression of a continuous variable, Z, on the trait, θ, to underlie the ordinal item scores (Takane & de Leeuw in Psychometrika, 52:393-408, 1987). Traditionally, a normal distribution is specified for Z implying homoscedastic error variances and a normally distributed θ. In this paper, we present the Heteroscedastic GRM with Skewed Latent Trait, which extends the traditional GRM by incorporation of heteroscedastic error variances and a skew-normal latent trait. An appealing property of the extended GRM is that it includes the traditional GRM as a special case. This enables specific tests on the normality assumption of Z. We show how violations of normality in Z can lead to asymmetrical category response functions. The ability to test this normality assumption is beneficial from both a statistical and substantive perspective. In a simulation study, we show the viability of the model and investigate the specificity of the effects. We apply the model to a dataset on affect and a dataset on alexithymia." "Advertising for extensions: Moderating effects of extension type, advertising strategy, and product category involvement on extension evaluation" "N Dens, Patrick De Pelsmacker" "This paper investigates how advertisements for extensions contribute to consumers' attitudes towards new line and brand extensions of highly familiar brands. We investigate the relative importance of attitude toward the advertisement (Aad), parent brand quality, and fit between the extension and the parent brand for extension evaluations with a sample of 754 Belgians. Hierarchical regressions showed that Aad is the major influencer of extension evaluation. The importance of Aad, quality, and fit on extension evaluation is moderated by extension type (line or brand extension), advertising strategy (informational, positive emotional, negative emotional), and product involvement (low or high involvement). Quality transfer from the parent brand was more outspoken for line than for brand extensions; Aad was relatively more important for low product involvement and fit for high involvement conditions. Informational appeals, compared to emotional appeals, reduced the effects of parent brand quality and fit, but Aad was all the more important." "Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities" "Akpona Okujeni, Frank Canters, Sam D. Cooper, Jeroen Degerickx, Uta Heiden, Patrick Hostert, Frederik Priem, Dar A. Roberts, Ben Somers, Sebastian van der Linden" "Forthcoming spaceborne imaging spectrometers will provide novel opportunities for mapping urban composition globally. To move from case studies for single cities towards comparative and more operational analyses, generalized models that may be transferred throughout space are desired. In this study, we investigated how single regression models can be spatially generalized for vegetation-impervious-soil (VIS) mapping across multiple cities. The combination of support vector regression (SVR) with synthetically mixed training data generated from spectral libraries was used for fraction mapping. We developed three local models based on separate spectral libraries from Berlin (Germany), Brussels (Belgium), and Santa Barbara (U.S.), and a generalized model based on a combined multi-site spectral library. To examine the performance and transferability of the generalized model compared to local models, we first applied all model variants to simulated Environmental Mapping and Analysis Program (EnMAP) data from the three cities that were represented in the models, i.e., known sites. Next, we transferred the models to two unknown sites not represented in the models, San Francisco Bay Area (U.S.) and Munich (Germany). In the first mapping constellation, results demonstrated that the generalized model was capable of accurately mapping VIS fractions across all three known sites. Average mean absolute errors (AV-MAEs) were 8.5, 12.2, and 11.0% for Berlin, Brussels, and Santa Barbara. The performance of the generalized model was very similar to the local models, with ∆AV-MAEs falling within a range of ±0.7%. A detailed assessment of fraction maps and class-wise accuracies confirmed that modeling errors related to remaining limitations of urban mapping based on optical remote sensing data rather than to the choice between a local or generalized model. For the second mapping constellation, the generalized model proved to be useful for mapping vegetation and impervious fractions in the unknown sites. MAEs for both cover types were 5.4 and 10.9% for the San Francisco Bay Area, and 6.3 and 15.4% for Munich. In contrast, the three local models were only found to have similar accuracies as the generalized model for one of the two sites or for individual VIS categories. Despite the enhanced transferability of the generalized model to the unknown sites, deficiencies remained for accurate soil mapping. MAEs were 22.4 and 12.3%, and high over - and underestimations were observed at the low and high end of the fraction range. These shortcomings indicated possible limitations of the spectral libraries to account for the spectral characteristics of soils in the unknown sites. Overall, we conclude that the combination of SVR and synthetically mixed training data generated from multi-site libraries constitutes a flexible modeling approach for generalized urban mapping across multiple cities." "Quantile Regression in Space-Time Varying Coefficient Model of Upper Respiratory Tract Infections Data" "Bertho Tantular, Budi Nurani Ruchjana, Yudhie ANDRIYANA, Anneleen VERHASSELT" "Space-time varying coefficient models, which are used to identify the effects of covariates that change over time and spatial location, have been widely studied in recent years. One such model, called the quantile regression model, is particularly useful when dealing with outliers or non-standard conditional distributions in the data. However, when the functions of the covariates are not easily specified in a parametric manner, a nonparametric regression technique is often employed. One such technique is the use of B-splines, a nonparametric approach used to estimate the parameters of the unspecified functions in the model. B-splines smoothing has potential to overfit when the number of knots is increased, and thus, a penalty is added to the quantile objective function known as P-splines. The estimation procedure involves minimizing the quantile loss function using an LP-Problem technique. This method was applied to upper respiratory tract infection data in the city of Bandung, Indonesia, which were measured monthly across 30 districts. The results of the study indicate that there are differences in the effect of covariates between quantile levels for both space and time coefficients. The quantile curve estimates also demonstrate robustness with respect to outliers. However, the simultaneous estimation of the quantile curves produced estimates that were relatively close to one another, meaning that some quantile curves did not depict the actual data pattern as precisely. This suggests that each district in Bandung City not only has different categories of incidence rates but also has a heterogeneous incidence rate based on three quantile levels, due to the difference in the effects of covariates over time and space." "Quantifying image distortion based on Gabor filter bank and multiple regression analysis" "Benhur Ortiz Jaramillo, Julio Cesar Garcia Alvarez, Hartmut Führ, Sergio Alejandro Orjuela Vargas, German Castellanos Dominguez, Wilfried Philips" "Image quality assessment is indispensable for image-based applications. The approaches towards image quality assessment fall into two main categories: subjective and objective methods. Subjective assessment has been widely used. However, careful subjective assessments are experimentally difficult and lengthy, and the results obtained may vary depending on the test conditions. On the other hand, objective image quality assessment would not only alleviate the difficulties described above but would also help to expand the application field. Therefore, several works have been developed for quantifying the distortion presented on a image achieving goodness of fit between subjective and objective scores up to 92%. Nevertheless, current methodologies are designed assuming that the nature of the distortion is known. Generally, this is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. Therefore, we believe that the current methods of image quality assessment should be adapted in order to identify and quantify the distortion of images at the same time. That combination can improve processes such as enhancement, restoration, compression, transmission, among others. We present an approach based on the power of the experimental design and the joint localization of the Gabor filters for studying the influence of the spatial/frequencies on image quality assessment. Therefore, we achieve a correct identification and quantification of the distortion affecting images. This method provides accurate scores and differentiability between distortions." "On regression modelling with dummy variables versus separate regressions per group: Comment on Holgersson et al." "Jan Schepers" "© 2015 Taylor & Francis. In a recent issue of this journal, Holgersson et al. [Dummy variables vs. category-wise models, J. Appl. Stat. 41(2) (2014), pp. 233–241, doi:10.1080/02664763.2013.838665] compared the use of dummy coding in regression analysis to the use of category-wise models (i.e. estimating separate regression models for each group) with respect to estimating and testing group differences in intercept and in slope. They presented three objections against the use of dummy variables in a single regression equation, which could be overcome by the category-wise approach. In this note, I first comment on each of these three objections and next draw attention to some other issues in comparing these two approaches. This commentary further clarifies the differences and similarities between dummy variable and category-wise approaches." "Support vector regression and synthetically mixed training data for quantifying urban land cover" "Laurent Tits, Ben Somers" "Exploiting imaging spectrometer data with machine learning algorithms has been demonstrated to be an excellent choice for mapping ecologically meaningful land cover categories in spectrally complex urban environments. However, the potential of kernel-based regression techniques for quantitatively analyzing urban composition has not yet been fully explored. To a great extent, this can be explained by difficulties in deriving quantitative training information that reliably represents pairs of spectral signatures with associated land cover fractions needed for empirical modeling. In this paper we present an approach to circumvent this limitation by combining support vector regression (SVR) with synthetically mixed training data to map sub-pixel fractions of single urban land cover categories of interest. This approach was tested on Hyperspectral Mapper (HyMap) data acquired over Berlin, Germany. Fraction estimates were validated with extensive manual mappings and compared to fractions derived from multiple endmember spectral mixture analysis (MESMA). Our regression results demonstrate that the sets of multiple mixtures yielded high accuracies for quantitative estimates for four spectrally complex urban land cover types, i.e., fractions of impervious rooftops and pavements, as well as grass- and tree-covered areas. Despite the extrapolation uncertainty of SVR, which resulted in fraction values below 0% and above 100%, physically meaningful model outputs were reported for a clear majority of pixels, and visual inspection underpinned the quality of produced fraction maps. Statistical accuracy assessment with detailed reference information for 92 urban blocks showed linear relations with R2 values of 0.86, 0.58, 0.81 and 0.85 for the four categories, respectively. Mean absolute errors (MAE) ranged from 6.4 to 12.8% and block-wise sums of the four individually modeled category fractions were always around 100%. Results of MESMA followed similar trends, but with slightly lower accuracies. Our findings demonstrate that the combination of SVR and synthetically mixed training data enable the use of empirical regression for sub-pixel mapping. Thus, the strengths of kernel-based approaches for quantifying urban land cover from imaging spectrometer data can be well utilized. Remaining uncertainties and limitations were related to the known phenomena of spectral similarity or ambiguity of urban materials, the spectral deficiencies in shaded areas, or the dependency on comprehensive and representative spectral libraries. Therefore, the suggested workflow constitutes a new flexible and extendable universal modeling approach to map land cover fractions."