Department of Mathematics
A maximum likelihood procedure is given for estimating parameters in a germination-growth process, based on germination times only or on both times and locations. The process is assumed to be driven by a Poisson process whose intensity is of known analytical form. The procedure is shown to perform well on simulated data with unnormalised gamma intensity and is also applied to data on release of neurotransmitter at a synapse.
Germination-growth, Maximum Likelihood Estimation, Neurobiology, Poisson, Synapse
Source Publication Title
Journal of Statistical Computation and Simulation
Taylor & Francis
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Statistical Computation and Simulation in October 2003, available online: http://www.tandfonline.com/10.1080/0094965031000078855.
SNC and ISM were supported by the UK/Hong Kong Joint Research Scheme, and by a Visiting Fellowship grant from the UK Engineering and Physical Sciences Research Council. SNC and MPQ were supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Project no. HKBU/2075/98P) and an FRG grant of the Hong Kong Baptist University.
Link to Publisher's Edition
Chiu, S., Molchanov, I., & Quine, M. (2003). Maximum likelihood estimation for germination-growth processes with application to neurotransmitters data. Journal of Statistical Computation and Simulation, 73 (10), 725-732. https://doi.org/10.1080/0094965031000078855