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We study regularized estimation in high‐dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an efficient algorithm for computing such estimates, based on proximal gradient descent. We apply our proposed technique to a longitudinal data set on Alzheimer's disease from the Cardiovascular Health Study Cognition Study. Using data analysis and a simulation study, we motivate and demonstrate several practical considerations such as the selection of tuning parameters and the assessment of model stability. While race, gender, vascular and heart disease, lack of caregivers, and deterioration of learning and memory are all important predictors of dementia, we also find that these risk factors become more relevant in the later stages of life. [ABSTRACT FROM AUTHOR]

An exploratory analysis of the Suicide Intent Scale was performed on a sample of 98 psychiatric inpatients who had made suicide attempts. The factor analysis was performed using a method for polychotomous data, and resulted in a two-factor solution. The Lethal Intent factor contained items pertaining to the subjective level of lethal intent, while the Planning factor contained items largely related to objective planning for the attempt. Preliminary analysis of these factors suggest that the Suicide Intent Scale can be used to evaluate two separate aspects of suicidal behavior. [ABSTRACT FROM AUTHOR]