Model-based Methods of Classification: Using the mclust Software in Chemometrics

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance.

Model-based Methods of Classification: Using the mclust Software in Chemometrics

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance

Equivalence of Narcissistic Personality Inventory constructs and correlates across scoring approaches and response formats

The prevalent scoring practice for the Narcissistic Personality Inventory (NPI) ignores the forced-choice nature of the items. The aim of this study was to investigate whether findings based on NPI scores reported in previous research can be confirmed when the forced-choice nature of the NPI’s original response format is appropriately modeled, and when NPI items are presented in different response formats (true/false or rating scale). The relationships between NPI facets and various criteria were robust across scoring approaches (mean score vs. model-based), but were only partly robust across response formats. In addition, the scoring approaches and response formats achieved equivalent measurements of the vanity facet and in part of the leadership facet, but differed with respect to the entitlement facet

Least angle and ℓ1\ell_1 penalized regression: A review

Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO ($\ell_1$-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has caught on rapidly, and sparked a great deal of research interest. In this paper, we give an overview of Least Angle Regression and the current state of related research.Comment: Published in at http://dx.doi.org/10.1214/08-SS035 the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical Statistics (http://www.imstat.org

Probabilistic Weather Forecasting in R

This article describes two R packages for probabilistic weather forecasting, ensembleBMA, which offers ensemble post-processing via Bayesian model averaging (BMA), and Prob ForecastGOP, which implements the geostatistical output perturbation (GOP) method. BMA forecasting models use mixture distributions, in which each component corresponds to an ensemble member, and the form of the component distribution depends on the weather parameter (temperature, quantitative precipitation or wind speed). The model parameters are estimated from training data. The GOP technique uses geostatistical methods to produce probabilistic fore casts of entire weather fields for temperature or pressure, based on a single numerical forecast on a spatial grid. Both packages include functions for evaluating predictive performance, in addition to model fitting and forecasting

Attachment and language use in donor-conceived adults' self-narratives

Assisted reproduction with donor gametes (i.e., eggs, sperm) entails the formation of new kinds of families, giving rise to new concepts such as "donor,""social father," and "social mother." These ideas can be understood within an attachment theoretical framework. The present study examined whether individual differences in attachment predict language use in donor-conceived adults' self- narratives. In particular, we focused on meaning-making (McAdams & McLean 2013), in addition to three other aspects of written text: Relational words (i.e., father, dad), non-relational words (i.e., donor, sperm), and "social" parent words (e.g., "my social father always picked me up from school") that participants used to describe their donor conception experience. Data were collected from 488 donor-conceived people from the Donor Sibling Registry (DSR). Results indicated no association between attachment and meaning-making, nor relational and non-relational words. However, we found that people who were anxiously attached (with respect to their close relationships in general) were more likely to endorse the term "social" parent; those who were avoidant were less likely to use this terminology when writing about their donor conception experience. These results, combined with other exploratory findings, suggests that insecurely attached DC adults may construct their narrative identities differently than their secure counterparts. Keywords: attachment, donor conception, LIWCOpe

Region-Of-Interest Selection and Statistical Analysis of Dynamic Breast Magnetic Resonance Imaging Data

Magnetic Resonance Imaging (MRI) is emerging as a powerful tool for the diagnosis of breast abnormalities. Early investigation of contrast-enhanced MRI of the breast demonstrated that breast carcinoma consistently enhanced after the intravenous administration of contrast material (Gadolinium). However, many benign lesions also enhance reducing as a result MRI specificity- . Further characterization is necessary to differentiate benign from malignant lesions and analysis of the temporal pattern of contrast uptake has been applied to improve specificity. Selecting a Region-Of-Interest (ROI) is an almost universal step in the process of examining the contrast uptake characteristics of a breast lesion. A limitation of most ROI analysis procedures is that only subjectively selected regions are examined. We propose a ROI selection method based on techniques from multivariate data analysis and statistical image segmentation. In addition to the enhancement rate of the contrast material, we use other characteristics of the signal intensity to group areas of the breast with similar characteristics and then select the group which corresponds to a fast and strong enhancement. We then investigate tools for subsequent analysis of signal intensity time course data in the selected region in order to determine if the enhancing lesion is likely to be benign or malignant. We tested our procedure on cases for which the diagnosis was available. The results are promising and are consistent with the diagnosed nature of the lesions