On Parametric Bootstrapping and Bayesian Prediction

Abstract.  We investigate bootstrapping and Bayesian methods for prediction. The observations and the variable being predicted are distributed according to different distributions. Many important problems can be formulated in this setting. This type of prediction problem appears when we deal with a Poisson process. Regression problems can also be formulated in this setting. First, we show that bootstrap predictive distributions are equivalent to Bayesian predictive distributions in the second-order expansion when some conditions are satisfied. Next, the performance of predictive distributions is compared with that of a plug-in distribution with an estimator. The accuracy of prediction is evaluated by using the Kullback–Leibler divergence. Finally, we give some examples.     

体验“监狱”生活

正听起来显得讽刺和悲伤——"监狱"成了现代人体验自由的下一站。这是发生在韩国的事情。在洪川郡坐落着一座名为"内心牢笼"(Prison Inside Me)的"监狱",不过这里并非对刑事罪犯开放,而是提供给对生活有束缚感的上班族、大学生、公司老板和全职妈妈。外观上看来,这里更像一个工业园区。此处的     

Effects of strength and power training on neuromuscular variables in older adults

The purpose of this study was to compare the neuromuscular adaptations produced by strength-training (ST) and power-training (PT) regimens in older individuals. Participants were balanced by quadriceps cross-sectional area (CSA) and leg-press 1-repetition maximum and randomly assigned to an ST group (n = 14; 63.6 ± 4.0 yr, 79.7 ± 17.2 kg, and 163.9 ± 9.8 cm), a PT group (n = 16; 64.9 ± 3.9 yr, 63.9 ± 11.9 kg, and 157.4 ± 7.7 cm), or a control group (n = 13; 63.0 ± 4.0 yr, 67.2 ± 10.8 kg, and 159.8 ± 6.8 cm). ST and PT were equally effective in increasing (a) maximum dynamic and isometric strength (p < .05), (b) increasing quadriceps muscle CSA (p < .05), and (c) decreasing electrical mechanical delay of the vastus lateralis muscle (p < .05). There were no significant changes in neuromuscular activation after training. The novel finding of the current study is that PT seems to be an attractive alternative to regular ST to maintain and improve muscle mass.