Prevalence of and Risk Factors for Sagittal Posture Abnormalities in Children Born With Esophageal Atresia: A Prospective Cohort Study

Introduction: Scoliosis is a well-described complication of esophageal atresia (EA) caused by the associated spine malformations and/or thoracotomy. However, the sagittal posture abnormalities in patients with EA have not been described. The aim of this study was to evaluate the prevalence of and risk factors for sagittal posture abnormalities at the age of 6 years in patients operated on for EA.Methods: A prospective cohort of 123 patients with EA was examined by the same rehabilitation doctor at the time of a multidisciplinary visit scheduled at the age of 6 years. Children presenting with scoliosis (n = 4) or who missed the consultation (n = 33) were excluded. Univariate and multivariate logistic regression models with Firth's penalized-likelihood approach were used to identify risk factors associated with sagittal posture anomalies. Candidate risk factors included neonatal characteristics, associated malformations, atresia type, postoperative complications, psychomotor development retardation, orthopedic abnormalities, and neurological hypotonia.Results: The prevalence rates of sagittal posture abnormalities were 25.6% (n = 22; 95% CI, 16.7–36.1%). Multivariate analysis showed that minor orthopedic abnormalities (OR: 4.02, 95% CI: 1.29–13.43, P = 0.021), and VACTERL (OR: 3.35, 95% CI: 1.09–10.71, P = 0.042) were significant risk factors for sagittal posture abnormalities.Conclusion: This study shows that sagittal posture anomalies occur frequently in children operated on at birth for EA and are not directly linked to the surgical repair. These children should be screened and treated using postural physiotherapy, especially those with VACTERL and minor orthopedic abnormalities.

A multi-state Markov model using notification data to estimate HIV incidence, number of undiagnosed individuals living with HIV, and delay between infection and diagnosis: Illustration in France, 2008–2018

Thirty-five years since the discovery of the human immunodeficiency virus (HIV), the epidemic is still ongoing in France. To guide HIV prevention strategies and monitor their impact, it is essential to understand the dynamics of the HIV epidemic. The indicator for reporting the progress of new infections is the HIV incidence. Given that HIV is mainly transmitted by undiagnosed individuals and that earlier treatment leads to less HIV transmission, it is essential to know the number of infected people unaware of their HIV-positive status as well as the time between infection and diagnosis. Our approach is based on a non-homogeneous multi-state Markov model describing the progression of the HIV disease. We propose a penalized likelihood approach to estimate the HIV incidence curve as well as the diagnosis rates. The HIV incidence curve was approximated using cubic M-splines, while an approximation of the cross-validation criterion was used to estimate the smoothing parameter. In a simulation study, we evaluate the performance of the model for reconstructing the HIV incidence curve and diagnosis rates. The method is illustrated in the population of men who have sex with men using HIV surveillance data collected by the French Institute for Public Health Surveillance since 2004.

A regularized hidden Markov model for analyzing the ‘hot shoe’ in football

We propose a penalized likelihood approach in hidden Markov models (HMMs) to perform automated variable selection. To account for a potential large number of covariates, which also may be substantially correlated, we consider the elastic net penalty containing LASSO and ridge as special cases. By quadratically approximating the non-differentiable penalty, we ensure that the likelihood can be maximized numerically. The feasibility of our approach is assessed in simulation experiments. As a case study, we examine the ‘hot hand’ effect, whose existence is highly debated in different fields, such as psychology and economics. In the present work, we investigate a potential ‘hot shoe’ effect for the performance of penalty takers in (association) football, where the (latent) states of the HMM serve for the underlying form of a player.

Selective Linear Segmentation for Detecting Relevant Parameter Changes*

Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

WAVELET-BASED FEATURE EXTRACTION FOR MORTALITY PROJECTION

Wavelet theory is known to be a powerful tool for compressing and processing time series or images. It consists in projecting a signal on an orthonormal basis of functions that are chosen in order to provide a sparse representation of the data. The first part of this article focuses on smoothing mortality curves by wavelets shrinkage. A chi-square test and a penalized likelihood approach are applied to determine the optimal degree of smoothing. The second part of this article is devoted to mortality forecasting. Wavelet coefficients exhibit clear trends for the Belgian population from 1965 to 2015, they are easy to forecast resulting in predicted future mortality rates. The wavelet-based approach is then compared with some popular actuarial models of Lee–Carter type estimated fitted to Belgian, UK, and US populations. The wavelet model outperforms all of them.

Direction of Arrival Estimation in Elliptical Models via Sparse Penalized Likelihood Approach

In this paper, an l 1 -penalized maximum likelihood (ML) approach is developed for estimating the directions of arrival (DOAs) of source signals from the complex elliptically symmetric (CES) array outputs. This approach employs the l 1 -norm penalty to exploit the sparsity of the gridded directions, and the CES distribution setting has a merit of robustness to the uncertainty of the distribution of array output. To solve the constructed non-convex penalized ML optimization for spatially either uniform or non-uniform sensor noise, two majorization-minimization (MM) algorithms based on different majorizing functions are developed. The computational complexities of the above two algorithms are analyzed. A modified Bayesian information criterion (BIC) is provided for selecting an appropriate penalty parameter. The effectiveness and superiority of the proposed methods in producing high DOA estimation accuracy are shown in numerical experiments.