Statistics – Computation
Scientific paper
2010-09-05
Statistics
Computation
20 pages, 3 figures, added explicit treatment of a death-immigration model and analytic derivations of conditional expectation
Scientific paper
Estimating parameters of continuous-time linear birth-death-immigration processes, observed discretely at unevenly spaced time points, is a recurring theme in statistical analyses of population dynamics. Viewing this task as a missing data problem, we develop two novel expectation-maximization (EM) algorithms. When birth rate is zero or immigration rate is either zero or proportional to the birth rate, we use Kendall's generating function method to reduce the E-step of the EM algorithm, as well as calculation of the Fisher information, to one dimensional integration. This reduction results in a simple and fast implementation of the EM algorithm. To tackle the unconstrained birth and immigration rates, we extend a direct sampler for finite-state Markov chains and use this sampling procedure to develop a Monte Carlo EM algorithm. We test our algorithms on simulated data and then use our new methods to explore the birth and death rates of a transposable element in the genome of Mycobacterium tuberculosis, the causative agent of tuberculosis.
Doss Charles R.
Holmes Ian
Kato-Maeda Midori
Minin Vladimir N.
Suchard Marc A.
No associations
LandOfFree
Great Expectations: EM Algorithms for Discretely Observed Linear Birth-Death-Immigration Processes does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Great Expectations: EM Algorithms for Discretely Observed Linear Birth-Death-Immigration Processes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Great Expectations: EM Algorithms for Discretely Observed Linear Birth-Death-Immigration Processes will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-473711