Acceptance of two liquid vitamin D₃ formulations among mothers with newborn infants: a randomized, single-blind trial

In Switzerland, children are prescribed 7.5-12.5 μg per day of vitamin D(3) dissolved in alcohol, but many families do not adhere to the recommendation. The aim of the trial was to compare the acceptance of vitamin D(3) dissolved in alcohol or in medium-chain triglycerides among mothers of Swiss newborn infants. The acceptance was tested in 42 healthy newborn infants (20 girls and 22 boys) aged between 2 and 7 days. Their neonatal body weight ranged between 2.225 and 4.150 kg, and the gestational age between 36 1/7 and 41 3/7 weeks. The blinded mothers rated the facial reaction of their children by pointing on a facial hedonic scale. Thirty eight of the 41 mothers, who brought the comparison to completion, assigned a better score to the oily preparation with no difference in the remaining three cases (P < 0.0001). The acceptance for the oily preparation was significantly better both among mothers whose babies were initially presented the alcoholic preparation and among mothers whose babies were initially presented the oily preparation. Furthermore, the acceptance for the oily preparation was better irrespective of gender of the infant or parity of the mother. In conclusion, from the perspective of mothers, Swiss newborn infants prefer the taste of the oily vitamin D(3) preparation over the alcoholic preparation

A new estimator of Zumbo\u2019s Ordinal Alpha: a copula approach

We aim to propose a new estimator of the reliability index for polytomous ordinal items, introduced by Zumbo et al. (J Mod Appl Stat Methods 6:21\u201329, 2007), who suggested a modification of the classical Cronbach\u2019s $$alpha$$\u3b1 indicator to be used in presence of ordinal variables. Zumbo et al. introduced an underlying variable conceptualization of the Cronbach\u2019s reliability index and suggested that one can estimate this underlying variable index, Zumbo\u2019s Ordinal Alpha, using ML estimation of the polychoric correlations. Our proposal relaxes the assumption made by Zumbo et al., that the ordinal variables have an underlying multinormal distribution, by using a copula framework. The proposed estimator builds upon the Spearman grade correlation coefficient on a transformation of the ordinal variables, calculated by the copula function. An empirical version of our proposal, defined by means of the empirical copula, is also presented. Some examples and simulation studies are presented

Bankruptcy prediction of small and medium enterprises using a flexible binary generalized extreme value model

We introduce a binary regression accounting-based model for bankruptcy prediction of small and medium enterprises (SMEs). The main advantage of the model lies in its predictive performance in identifying defaulted SMEs. Another advantage, which is especially relevant for banks, is that the relationship between the accounting characteristics of SMEs and response is not assumed a priori (eg, linear, quadratic or cubic) and can be determined from the data. The proposed approach uses the quantile function of the generalized extreme value distribution as link function as well as smooth functions of accounting characteristics to flexibly model covariate effects. Therefore, the usual assumptions in scoring models of symmetric link function and linear or pre-specified covariate-response relationships are relaxed. Out-of-sample and out-of-time validation on Italian data shows that our proposal outperforms the commonly used (logistic) scoring model for different default horizons

Modelling small and medium enterprise loan defaults as rare events: the generalized extreme value regression model

A pivotal characteristic of credit defaults that is ignored by most credit scoring models is the rarity of the event. The most widely used model to estimate the probability of default is the logistic regression model. Since the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks, for example, underestimation of the default probability, which could be very risky for banks. In order to overcome these drawbacks, we propose the generalized extreme value regression model. In particular, in a generalized linear model (GLM) with the binary-dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure used is the maximum-likelihood method. This model accommodates skewness and it presents a generalisation of GLMs with complementary log\u2013log link function. We analyse its performance by simulation studies. Finally, we apply the proposed model to empirical data on Italian small and medium enterprises