This paper explores the implications of asset return predictability on longterm portfolio choice when return forecasting variables exhibit long memory. We model long memory using the class of fractionally integrated time series models. Important predictor variables for U.S. data, like the dividend-price ratio and nominal and real interest rates, are non-stationary with orders of integration aro...

A variety of anthropometric and training characteristics have been identified as predictor variables for race performance in endurance and ultra-endurance athletes. Anthropometric characteristics such as skin-fold thicknesses, body fat, circumferences and length of limbs, body mass, body height, and body mass index were bi-variately related to race performance in endurance athletes such as swim...

BACKGROUND We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variab...

Objectives: Aging is associated with cognitive decline, including visuomotor and memory concerns, and with motor system changes, including gait slowing and stooped posture. We investigated the associations of visuomotor performance and episodic memory with motor system characteristics in healthy older adults. Methods: Neurologically healthy older adults (N = 160, aged 50-89) completed a battery...

In antibiotics industry, the titre in bioreactors is the most important process variable both for process supervision and scheduling. It is therefore of great significance to develop a software sensor to predict the product formation. In this contribution, a pseudo dynamic product predictor based on artificial neural network is designed. The input process variables of the predictor include subs...

We develop a Bayesian “sum-of-trees” model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by en...