Search for relevant subsets of binary predictors in high dimensional regression for discovering the lead molecule

One of the main problems that the drug discovery research field confronts is to identify small molecules, modulators of protein function, which are likely to be therapeutically useful. Common practices rely on the screening of vast libraries of small molecules (often 1–2 million molecules) in order to identify a molecule, known as a lead molecule, which specifically inhibits or activates the protein function. To search for the lead molecule, we investigate the molecular structure, which generally consists of an extremely large number of fragments. Presence or absence of particular fragments, or groups of fragments, can strongly affect molecular properties. We study the relationship between molecular properties and its fragment composition by building a regression model, in which predictors, represented by binary variables indicating the presence or absence of fragments, are grouped in subsets and a bi-level penalization term is introduced for the high dimensionality of the problem. We evaluate the performance of this model in two simulation studies, comparing different penalization terms and different clustering techniques to derive the best predictor subsets structure. Both studies are characterized by small sets of data relative to the number of predictors under consideration. From the results of these simulation studies, we show that our approach can generate models able to identify key features and provide accurate predictions. The good performance of these models is then exhibited with real data about the MMP–12 enzyme.     

Voice Cues Influence Children’s Assessment of Adults’ Occupational Competence

Journal of Nonverbal Behavior - The adult voice is a strong bio-social marker for masculinity and femininity. In this study we investigated whether children make gender stereotypical judgments...     

A Generalization of the Skew-Normal Distribution: The Beta Skew-Normal

We consider a new generalization of the skew-normal distribution introduced by Azzalini (1985 Azzalini , A. ( 1985 ). A class of distributions which includes the normal ones . Scand. J. Statis. 12 ( 2 ): 171 – 178 .[Web of Science ®] , [Google Scholar]). We denote this distribution Beta skew-normal (BSN) since it is a special case of the Beta generated distribution (Jones, 2004 Jones , M. C. ( 2004 ). Families of distributions of order statistics . Test 13 ( 1 ): 1 – 43 .[Crossref], [Web of Science ®] , [Google Scholar]). Some properties of the BSN are studied. We pay attention to some generalizations of the skew-normal distribution (Bahrami et al., 2009 Bahrami , W. , Agahi , H. , Rangin , H. ( 2009 ). A two-parameter Balakrishnan skew-normal distribution . J. Statist. Res. Iran 6 : 231 – 242 . [Google Scholar]; Sharafi and Behboodian, 2008 Sharafi , M. , Behboodian , J. ( 2008 ). The Balakrishnan skew-normal density . Statist. Pap. 49 : 769 – 778 .[Crossref], [Web of Science ®] , [Google Scholar]; Yadegari et al., 2008 Yadegari , I. , Gerami , A. , Khaledi , M. J. ( 2008 ). A generalization of the Balakrishnan skew-normal distribution . Statist. Probab. Lett. 78 : 1165 – 1167 .[Crossref], [Web of Science ®] , [Google Scholar]) and to their relations with the BSN.     

Large sample confidence intervals for the skewness parameter of the skew-normal distribution based on Fisher's transformation

The skew-normal model is a class of distributions that extends the Gaussian family by including a skewness parameter. This model presents some inferential problems linked to the estimation of the skewness parameter. In particular its maximum likelihood estimator can be infinite especially for moderate sample sizes and is not clear how to calculate confidence intervals for this parameter. In this work, we show how these inferential problems can be solved if we are interested in the distribution of extreme statistics of two random variables with joint normal distribution. Such situations are not uncommon in applications, especially in medical and environmental contexts, where it can be relevant to estimate the distribution of extreme statistics. A theoretical result, found by Loperfido [7 Loperfido, N. 2002. Statistical implications of selectively reported inferential results. Statist. Probab. Lett., 56: 13–22. [Crossref], [Web of Science ®] , [Google Scholar]], proves that such extreme statistics have a skew-normal distribution with skewness parameter that can be expressed as a function of the correlation coefficient between the two initial variables. It is then possible, using some theoretical results involving the correlation coefficient, to find approximate confidence intervals for the parameter of skewness. These theoretical intervals are then compared with parametric bootstrap intervals by means of a simulation study. Two applications are given using real data.     

Motivations and Family Functioning of Foster Families in Italy

ABSTRACT

The aim of this paper was to investigate foster family care to better understand this unique care-giving context. The research objective was to understand motivation, family functioning, and individual attachment styles in foster families. Participants were 33 foster parents who completed a questionnaire that included the motivations for choosing foster care, the relationship with the birth family and with social service workers, a measure of family functioning and adult attachment. The motivations for becoming a foster family were very different between families with a birth child and families without a birth child. The results showed that for the foster mother, the relationship with the birth family constituted a very critical element of the fostering experience. The same trend emerged in the relationship with the minor. The results showed that foster families were cohesive and expressive. With respect to attachment style, foster parents had lower scores than a normative sample on the discomfort with closeness, the tendency to consider relationships as secondary and the need for approval subscales. These results have implications for the practices of social work for foster family support and developing a shared commitment to the needs of the foster family in terms of both resources and weaknesses.     

A review of the CTP distribution: a comparison with other over- and underdispersed count data models

The complex triparametric Pearson (CTP) distribution is a flexible model belonging to the Gaussian hypergeometric family that can account for over- and underdispersion. However, despite its good properties, not much attention has been paid to it. So, we revive the CTP comparing it with some well-known distributions that cope with overdispersion (negative binomial, generalized Poisson and univariate generalized Waring) as well as underdispersion (Conway–Maxwell–Poisson (CMP) and hyper-Poisson (HP)). We make a simulation study that reveals the performance of the CTP and shows that it has its own space among count data models. In this sense, we also explore some overdispersed datasets which seem to be more appropriately modelled by the CTP than by other usual models. Moreover, we include two underdispersed examples to illustrate that the CTP can provide similar fits to the CMP or HP (sometimes even more accurate) without the computational problems of these models.     

Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model

We propose a test for state dependence in binary panel data with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a Conditional Maximum Likelihood (CML) approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. Therefore, it is possible to implement a t-test for this hypothesis which may be very simply performed and attains the nominal significance level under several structures of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state dependence. The proposed approach is illustrated by two empirical applications: the first is based on data coming from the Panel Study of Income Dynamics and concerns employment and fertility; the second is based on the Health and Retirement Study and concerns the self reported health status.     

Fear of childbirth in primiparous Italian pregnant women: The role of anxiety,depression, and couple adjustment

Background

The prevalence of fear of childbirth in pregnant women is described to be about 20–25%, while 6–10% of expectant mothers report a severe fear that impairs their daily activities as well as their ability to cope with labour and childbirth. Research on fear of childbirth risk factors has produced heterogeneous results while being mostly done with expectant mothers from northern Europe, northern America, and Australia.