The aim was to investigate predictors of treatment dropout among 182 children (aged 8-15 years) participating in an effectiveness trial of manual-based 10-session individual and group cognitive behavior therapy (CBT) for anxiety disorders in community clinics. The dropout rate was 14.4%, with no significant difference between the two treatment conditions. We examined predictors for overall drop...

BACKGROUND Rates of treatment dropout in outpatient Dialectical Behavior Therapy (DBT) in the community can be as high as 24 % to 58 %, making dropout a great concern. The primary purpose of this article was to examine predictors of dropout from DBT in a community mental health setting. METHODS Participants were 56 consumers with borderline personality disorder (BPD) who were psychiatrically ...

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However, the independent sampling for dropout could be suboptimal for the sake of convergence. In this paper, we propose to use multinomial sampling for dropout, i.e...

C to the common view that analysts are important information agents, intraday returns evidence shows that announcements of analysts’ forecast revisions release little new information, on average. Further cross-sectional evidence from returns around the announcements confirms that revisions are virtually information free. Daily announcement returns used in the literature appear to overstate the ...

Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also enco...

In this paper, multivariate skew-normal distribution is em- ployed for analyzing an outcome based dropout model for repeated mea- surements with non-random dropout in skew regression data sets. A probit regression is considered as the conditional probability of an ob- servation to be missing given outcomes. A simulation study of using the proposed methodology and comparing it with a semi-parame...

We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introduc...

The linear mixed model with an added integrated Ornstein-Uhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of available software. We implemented the linear mixed IOU model in Stata and using simulations we assessed the feasibility of fitting the model by restricted maximum...

We introduce Information Dropout, a generalization of dropout that is motivated by the Information Bottleneck principle and highlights the way in which injecting noise in the activations can help in learning optimal representations of the data. Information Dropout is rooted in information theoretic principles, it includes as special cases several existing dropout methods, like Gaussian Dropout ...

BACKGROUND Dropout rates in binge eating disorder (BED) treatment are high (17-30%), and predictors of dropout are unknown. METHOD Participants were 376 patients following an intensive outpatient cognitive behavioural therapy programme for BED, 82 of whom (21.8%) dropped out of treatment. An exploratory logistic regression was performed using eating disorder variables, general psychopathology...