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STATISTICAL INFERENCE FOR A POISSON PROCESS MODEL OF SCANNING FLUORESCENCE CORRELATION SPECTROSCOPY
Author(s) -
BENN A. G.,
KULPERGER R. J.
Publication year - 1996
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/(sici)1099-095x(199603)7:2<167::aid-env203>3.0.co;2-c
Subject(s) - estimator , point process , consistency (knowledge bases) , statistical inference , poisson distribution , confidence interval , mathematics , statistics , data set , statistical model , point estimation , fluorescence correlation spectroscopy , fluorescence spectroscopy , biological system , algorithm , physics , fluorescence , optics , geometry , biology
Scanning fluorescence correlation spectroscopy (S‐FCS) is a method of indirectly observing biological cell masses. This paper proposes a filtered Poisson process model of S‐FCS, and based on this model the statistical properties of certain parameter estimators are obtained. In addition to the consistency of these point estimators, it is shown that limiting variances can be estimated, giving rise to confidence intervals of the parameters, based on a single set of experimental data. Previously only point estimates, obtained by averaging over several data sets, could be calculated. The estimation methods are then applied to several S‐FCS data sets.