Johnson Timothy D
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, USA.
Biometrics. 2003 Sep;59(3):650-60. doi: 10.1111/1541-0420.00075.
Many hormones are secreted into the circulatory system in a pulsatile manner and are cleared exponentially. The most common method of analyzing these systems is to deconvolve the hormone concentration into a secretion function and a clearance function. Accurate estimation of the model parameters depends on the number and location of the secretion pulses. To date, deconvolution analysis assumes the number and approximate location of these pulses are known a priori. In this article, we present a novel Bayesian approach to deconvolution that jointly models the number of pulses along with all other model parameters. Our method stochastically searches for the secretion pulses. This is accomplished by viewing the set of parameters that define the pulses as a point process. Pulses are determined by a birth-death process which is embedded in Markov chain Monte Carlo algorithm. This idea originated with Stephens (2000, Annals of Statistics 28, 40-74) in the context of finite mixture model density estimation, where the number of mixture components is unknown. There are several advantages that our model enjoys over the traditional frequentist approaches. These advantages are highlighted with four datasets consisting of serum concentration levels of luteinizing hormone obtained from ovariectomized ewes.
许多激素以脉冲方式分泌到循环系统中，并呈指数清除。分析这些系统最常用的方法是将激素浓度反卷积为分泌函数和清除函数。模型参数的准确估计取决于分泌脉冲的数量和位置。迄今为止，反卷积分析假定这些脉冲的数量和大致位置是先验已知的。在本文中，我们提出了一种新颖的贝叶斯反卷积方法，该方法将脉冲数量与所有其他模型参数联合建模。我们的方法随机搜索分泌脉冲。这是通过将定义脉冲的参数集视为一个点过程来实现的。脉冲由嵌入马尔可夫链蒙特卡罗算法的生死过程确定。这个想法起源于斯蒂芬斯（2000年，《统计学年鉴》28卷，40 - 74页）在有限混合模型密度估计的背景下，其中混合成分的数量是未知的。我们的模型相对于传统的频率主义方法有几个优点。通过四个由去卵巢母羊的促黄体生成素血清浓度水平组成的数据集突出了这些优点。
