BACKGROUND Dropout (DO) is common in the treatment of eating disorders (EDs), but the reasons for this phenomenon remain unclear. This study is an extensive review of the literature regarding DO predictors in EDs. METHODS All papers in PubMed, PsycINFO and Cochrane Library (1980-2009) were considered. Methodological issues and detailed results were analysed for each paper. After selection acc...

One of the Millennium Development Goals is to ensure universal access to primary education by 2015. However, primary school dropout remains a challenge in many developing countries. While official statistics in China report aggregated primary school dropout of only 0.2 %, almost no independent, survey-based studies have sought to verify these dropout rates in rural areas. The primary objective ...

BACKGROUND Clinical trials with longitudinally measured outcomes are often plagued by missing data due to patients withdrawing or dropping out from the trial before completing the measurement schedule. The reasons for dropout are sometimes clearly known and recorded during the trial, but in many instances these reasons are unknown or unclear. Often such reasons for dropout are non-ignorable. Ho...

BACKGROUND High school dropout is of major concern in the western world. Our aims were to estimate the risk of school dropout in adolescents following chronic somatic disease, somatic symptoms, psychological distress, concentration difficulties, insomnia or overweight and to assess to which extent the family contributes to the association between health and school dropout. METHODS A populatio...

Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analy...

Dropout is a simple yet effective algorithm for regularizing neural networks by randomly dropping out units through Bernoulli multiplicative noise, and for some restricted problem classes, such as linear or logistic regression, several theoretical studies have demonstrated the equivalence between dropout and a fully deterministic optimization problem with data-dependent Tikhonov regularization....

There is inconsistency in how we operationalise dropout in clinical research, with studies defining it based on time, number of sessions, therapeutic goals, an identified ‘end’ of therapy, or the therapist’s judgement. The effect of different definitions is poorly understood, but metaanalyses have indicated that they may produce varying rates of dropout and relate to heterogenous client groups....

This article focuses on the high dropout rate in the Danish vocational education system. The article seeks to analyse the dropout problem using the concept of trust. By taking this perspective on the dropout problem, the article seeks to replace the hitherto-dominant deficit of understanding within the Danish dropout research, which has focused on the so-called crisis of motivation in which stu...

Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dropout, where measurement times can be irregular and dropout can occur at any point in continuous time (not just at observation times) together with administr...

We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of ...