On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection

Wavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning.     

The State of Research in Latino Academic Attainment

In this essay, I present an interdisciplinary examination of the state of research in Latino education, addressing not only the cultural forces influencing Latinos’ educational attainment, but also the structural conditions faced by them—as a racialized minority group—throughout their schooling and that have a cumulative effect on their academic attainment. Overall, I suggest that by engaging in a critical review of the literature on Latinos’ educational attainment, the reader will acknowledge that the racially driven hierarchical structural arrangements are highly responsible for the patterns observed.     

A Note on Selection and Gender Unemployment Gaps

Previous research has documented the impact of positive selection into employment on the ranking of countries by gender wage gaps. This note focuses on the impact of selection into labor force on cross-country differences in gender unemployment gaps. We construct the Manski bounds for the selection-free gender unemployment gaps in 26 EU countries and show that - without additional assumptions - the observed gender unemployment gaps carry little information about the selection-free gender differences in unemployment. Contrary to the common assumption of positive selection into labor force (similar to positive selection into employment documented in the gender wage gap research), we also point at an example of negative-selection bias. We show that labor force withdrawal of mothers on job-protected family leaves may lead to an overestimation of the selection-free gender unemployment gaps by as much as 1 p.p.     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.     

Trend smoothness achieved by penalized least squares with the smoothing parameter chosen by optimality criteria

This work presents a study about the smoothness attained by the methods more frequently used to choose the smoothing parameter in the context of splines: Cross Validation, Generalized Cross Validation, and corrected Akaike and Bayesian Information Criteria, implemented with Penalized Least Squares. It is concluded that the amount of smoothness strongly depends on the length of the series and on the type of underlying trend, while the presence of seasonality even though statistically significant is less relevant. The intrinsic variability of the series is not statistically significant and its effect is taken into account only through the smoothing parameter.     

Semi-polynomial copulas

In this paper, we characterise a family of bivariate copulas whose sections between the main diagonal and the border of the unit square are polynomial, generalising several families of copulas, including those with quadratic and cubic sections. We also study a measure of association and the tail dependence for this class, illustrating our results with several examples.     

Community Wise: The Development of an Anti-Oppression Model to Promote Individual and Community Health

Communities with histories of oppression have shown great resilience, yet few health interventions focus on structural oppression as a contributor to health problems in these communities. This article describes the development and active ingredients of Community Wise, a unique behavioral health intervention designed to reduce substance use frequency, related health risk behaviors, and recidivism among individuals with a history of incarceration and substance abuse residing in distressed and predominantly African American communities. Community Wise, developed through the collaborative efforts of a board of service providers, researchers, consumers, and government officials, is a 12-week group intervention that aims to address behavioral health problems by raising critical consciousness in distressed communities.     

Long-term care in Spain: Difficulties in professionalizing services

The aim of this article is to analyze the difficulties in professionalizing the long-term care system in Spain. Since 2006, the new Spanish law has recognized care as a subjective right, and regulations are being designed to create a framework for its professionalization. Nowadays, family remains the most important group of providers who care for their elders, and women remain the main informal caregivers. Why do families resist using public long-term care services and professional carers included in the new law? The hypothesis highlights sociocultural factors as an obstacle to professionalization of long-term care services in addition to political and economic factors. The results show qualitative data about expectations, preferences, and discourses that women caregivers have in relation to their responsibility. The empirical material includes 25 interviews with different profiles of caregivers and six focus groups with family caregivers. The article suggests that the Spanish ideal of care is a problem for the professionalization of services because the family remains as the main provider of care—without specific skills, knowledge, and abilities.