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利用MCMC对时间相关数据进行多元受体建模AI 解读

Multivariate receptor modeling aims to estimate pollution source profiles and the amounts of pollution based on a series of ambient concentrations of multiple chemical species over time. Air pollution data often show temporal dependence due to meteorology and/or background sources. Previous approaches to receptor modeling do not incorporate this dependence. We model dependence in the data using a time series approach so that we can incorporate extra sources of variability in…

连续比例向量的时间序列模型AI 解读

SUMMARY A vector of continuous proportions consists of the proportions of some total accounted for by its constituent components. An example is the proportions of world motor vehicle production by Japan, the USA and all other countries. We consider the situation where time series data are available and where interest focuses on the proportions rather than the actual amounts. Reasons for analysing such times series include estimation of the underlying trend, estimation of the…

抽样计数过程强度的非参数估计AI 解读

SUMMARY Sampled counting processes are often studied using methods for the analysis of stationary time series. We express some time series parameters as smoothed versions of corresponding point process parameters and use these relations to suggest estimates of the point process parameters. In addition, we propose a reconstructive approach to the estimation problem. We derive some properties of the estimators and apply them to data from meteorology, entomology and particle…

部分观测随机游走中的一致估计AI 解读

If a discrete-time random walk, consisting of the sum of independent increments from an unknown underlying distribution, is observed at every time instant then it is clear that the underlying distribution can be consistently estimated. If, however, we are restricted to observing only a subset of the times, and if this subset is too sparse, then a central limit effect takes over and only two moments can be consistently estimated. We show that divergence of the sum of the…