Global Sensitivity Analysis Applied to a Contamination Assessment Model of Listeria monocytogenes in Cold Smoked Salmon at Consumption

In this study, a variance‐based global sensitivity analysis method was first applied to a contamination assessment model of Listeria monocytogenes in cold smoked vacuum packed salmon at consumption. The impact of the choice of the modeling approach (populational or cellular) of the primary and secondary models as well as the effect of their associated input factors on the final contamination level was investigated. Results provided a subset of important factors, including the food water activity, its storage temperature, and duration in the domestic refrigerator. A refined sensitivity analysis was then performed to rank the important factors, tested over narrower ranges of variation corresponding to their current distributions, using three techniques: ANOVA, Spearman correlation coefficient, and partial least squares regression. Finally, the refined sensitivity analysis was used to rank the important factors.     

Bayesian regression under combinations of constraints

A Bayesian method for regression under several types of constraints is proposed. The constraints can be range-restricted and include shape restrictions, constraints on the value of the regression function, smoothness conditions and combinations of these types of constraints. The support of the prior distribution is included in the set of piecewise linear functions. It is shown that the proposed prior can be arbitrarily close to the distribution induced by the addition of a polynomial plus an (m−1)-fold integrated Brownian motion. Hence, despite its piecewise linearity, the regression function behaves (approximately) like an m−1 times continuously differentiable random function. Furthermore, thanks to the piecewise linear property, many combinations of constraints can easily be considered. The regression function is estimated by the posterior mode computed by a simulated annealing algorithm. The constraints on the shape and the values of the regression function are taken into account thanks to the proposal distribution, while the smoothness condition is handled by the acceptation step. Simulations from the posterior distribution are obtained by a Gibbs sampling algorithm.     

Infants' Acquisition of Grammatical Gender Dependencies

To successfully understand spoken language, listeners need to determine how words within sentences relate to one another. Although the ability to compute relationships between word categories is known to develop early in life, little research has been conducted on infants' early sensitivity to subcategorical dependencies, such as those evoked by grammatical gender (where the article form is dictated by the noun's gender). This study therefore examines whether French‐learning 18‐month‐olds track such relationships. Using the Visual Fixation Procedure, infants were presented with article–noun sequences in which the gender‐marked article either matched (e.g., la_FEM poussette_FEM “the stroller”) or mismatched (e.g., le_MASC poussette_FEM) the gender of the noun. A clear preference for correct over incorrect co‐occurrences was observed, suggesting that by 18 months of age, children's storage and access of words is sufficiently sophisticated to include the means to track subcategorical dependencies. This early sensitivity to gender information may be greatly beneficial for constraining lexical access during online language processing.     

Logistic discrimination using robust estimators: An influence function approach

Logistic regression is frequently used for classifying observations into two groups. Unfortunately there are often outlying observations in a data set and these might affect the estimated model and the associated classification error rate. In this paper, the authors study the effect of observations in the training sample on the error rate by deriving influence functions. They obtain a general expression for the influence function of the error rate, and they compute it for the maximum likelihood estimator as well as for several robust logistic discrimination procedures. Besides being of interest in their own right, the influence functions are also used to derive asymptotic classification efficiencies of different logistic discrimination rules. The authors also show how influential points can be detected by means of a diagnostic plot based on the values of the influence function     

A robust test of exogeneity based on quantile regressions

In this paper, we propose a robust test of exogeneity. The test statistics is constructed from quantile regression estimators, which are robust to heavy tails of errors. We derive the asymptotic distribution of the test statistic under the null hypothesis of exogeneity at a given quantile. The finite sample properties of the test are investigated through Monte Carlo simulations that exhibit not only good size and power properties, but also good robustness to outliers.     

Theoretical L-moments and TL-moments using combinatorial identities and finite operators

Moments have been traditionally used to characterize a probability distribution. Recently, linear moments (L-moments) and trimmed L-moments (TL-moments) are appealing alternatives to the conventional moments. This paper focuses on the computation of theoretical L-moments and TL-moments and emphasizes the use of combinatorial identities. We are able to derive new closed-form formulas of L-moments and TL-moments for continuous probability distributions. Finally, closed-form formulas for the L-moments for the exponential distribution and the uniform distribution are also obtained.     

On temperance and risk spreading

Theory and Decision - On page 599 in the original publication of the article.     

14‐month‐olds exploit verbs’ syntactic contexts to build expectations about novel words

During their second year of life, infants develop a rudimentary understanding of grammatical categories based on their knowledge and use of frequent function words. The current study inquired whether, at only 14 months of age, infants can track co‐occurrence patterns between function words and content words (e.g., determiners can precede nouns, and pronouns can precede verbs), and use these previously encountered syntactic contexts to build expectations about which function words can co‐occur with novel words. Using a habituation paradigm, French‐learning 14‐month‐olds were presented with utterances containing two novel words preceded by function words (either two determiners in the Novel Nouns condition or two pronouns in the Novel Verbs conditions). We found that at test, infants looked longer during trials in which the novel words occurred in an unexpected syntactic context (following a pronoun for infants in the Novel Nouns condition and following a determiner for infants in the pooled analysis of the three Novel Verbs conditions). Hence, our results confirm previous findings on infants’ sensitivity to noun contexts and most importantly demonstrate that their sensitivity to the co‐occurrence of verbs with pronouns begins much earlier than previously understood.     

The Probabilistic Final Standing Calculator: a fair stochastic tool to handle abruptly stopped football seasons

AStA Advances in Statistical Analysis - The COVID-19 pandemic has left its marks in the sports world, forcing the full stop of all sports-related activities in the first half of 2020. Football...