Quantile regression with group lasso for classification

Applications of regression models for binary response are very common and models specific to these problems are widely used. Quantile regression for binary response data has recently attracted attention and regularized quantile regression methods have been proposed for high dimensional problems. When the predictors have a natural group structure, such as in the case of categorical predictors converted into dummy variables, then a group lasso penalty is used in regularized methods. In this paper, we present a Bayesian Gibbs sampling procedure to estimate the parameters of a quantile regression model under a group lasso penalty for classification problems with a binary response. Simulated and real data show a good performance of the proposed method in comparison to mean-based approaches and to quantile-based approaches which do not exploit the group structure of the predictors

An efficient algorithm for structured sparse quantile regression

An efficient algorithm is derived for solving the quantile regression problem combined with a group sparsity promoting penalty. The group sparsity of the regression parameters is achieved by using a $ell_{1,infty}$-norm penalty (or constraint) on the regression parameters. The algorithm is efficient in the sense that it obtains the regression parameters for a wide range of penalty parameters, thus enabling easy application of a model selection criteria afterwards. A Matlab implementation of the proposed algorithm is provided and some applications of the methods are studied.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Role of common mental and physical disorders in partial disability around the world

Background Mental and physical disorders are associated with total disability, but their effects on days with partial disability (i.e. the ability to perform some, but not full-role, functioning in daily life) are not well understood. Aims To estimate individual (i.e. the consequences for an individual with a disorder) and societal effects (i.e. the avoidable partial disability in the society due to disorders) of mental and physical disorders on days with partial disability around the world. Method Respondents from 26 nationally representative samples (n=61 259, age 18+) were interviewed regarding mental and physical disorders, and day-to-day functioning. The Composite International Diagnostic Interview, version 3.0 (CIDI 3.0) was used to assess mental disorders; partial disability (expressed in full day equivalents) was assessed with the World Health Organization Disability Assessment Schedule in the CIDI 3.0. Results Respondents with disorders reported about 1.58 additional disability days per month compared with respondents without disorders. At the individual level, mental disorders (especially post-traumatic stress disorder, depression and bipolar disorder) yielded a higher number of days with disability than physical disorders. At the societal level, the population attributable risk proportion due to physical and mental disorders was 49% and 15% respectively. Conclusions Mental and physical disorders have a considerable impact on partial disability, at both the individual and at the societal level. Physical disorders yielded higher effects on partial disability than mental disorders