Expectation-maximization analysis of spatial time series

Expectation-maximization analysis of spatial time series

Expectation maximization (EM) is used to estimate the parameters of a Gaussian Mixture Model for spatial time series data. The method is presented as an alternative and complement to Empirical Orthogonal Function (EOF) analysis. The resulting weights, associating time points with component distributions, are used to distinguish physical regimes. The method is applied to equatorial Pacific sea s...

a time-series analysis of the demand for life insurance in iran

با توجه به تجزیه و تحلیل داده ها ما دریافتیم که سطح درامد و تعداد نمایندگیها باتقاضای بیمه عمر رابطه مستقیم دارند و نرخ بهره و بار تکفل با تقاضای بیمه عمر رابطه عکس دارند

Expectation-based scan statistics for monitoring spatial time series data

We consider the simultaneous monitoring of a large number of spatially localized time series in order to detect emerging spatial patterns. For example, in disease surveillance, we detect emerging outbreaks by monitoring electronically available public health data, e.g. aggregate daily counts of Emergency Department visits. We propose a two-step approach based on the expectation-based scan stati...

Expectation Maximization

Expectation Maximization

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...