In recent years, calibration estimation has become an important field of research in survey sampling. This paper proposes a new calibration estimator for the population mean in the presence of two auxiliary variables in stratified sampling. The theory of new calibration estimator is given and optimum calibration weights are derived. A simulation study is carried out to performance of the proposed calibration estimator over other existing calibration estimators. The results reveal that the proposed calibration estimators are more efficient than other existing calibration estimators in stratified sampling. 相似文献
Background: Sarcopenia is a pathophysiological condition diffused in elderly people; it represents a social issue due to the longer life expectancy and the growing aging population. It affects negatively quality of life and it represents a risk factor for other pathologies, such as diabetes, cardiovascular disease, and obesity. No silver bullet exists to hinder sarcopenia, but it may be counteracted by physical exercise, nutrition, and a proper endocrine milieu. Indeed, we aim to analyze the scientific literature to give to clinician effective advices to counteract sarcopenia.
Main text: Physical exercise, proper nutrition, optimized hormonal homeostasis represent the three pillars to fight sarcopenia. Physical exercise represents the most effective remedy to face sarcopenia, in particular if it is combined with a proper diet and with an adequate endocrine milieu. Consistency in training, adequate daily protein intake and eugonadism seems to be the keys to fight sarcopenia. The combination of these three pillars might act synergistically.
Conclusions: Optimization of these factors may increase their efficiency; however, scientific data may be sometimes confusing so far. Therefore, we aim to give practical advices to clinician to identify and to highlight the most important aspects in each of these three factors that should be addressed. 相似文献
In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure. 相似文献