Document Type
Report
Source Publication Title
Technical Report 302
Abstract
Transition probabilities provide a convenient summary of changes in a categorical trait over time in a population. The difficulties of estimating such probabilities based on only aggregate data from repeated sampling are well known. We give here a method for augmenting aggregate data with haphazard recapture data, which can dramatically improve the estimation precision of transition probabilities. The method requires a rather high sampling fraction to provide sufficient numbers of recaptures. It is based on a generalized nonlinear least squares strategy which yields transition probability estimates preserving their natural parameter space, and which are asymptotically efficient. The asymptotic theory is given under finite population sampling assumptions which are typical in practice.
Disciplines
Mathematics | Physical Sciences and Mathematics
Publication Date
1-1-1995
Language
English
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Hawkins, D. L.; Eisenfeld, Jerome; and Han, C. P., "Estimating Transition Probabilities from Aggregate Samples Augmented by Haphazard Recaptures" (1995). Mathematics Technical Papers. 18.
https://mavmatrix.uta.edu/math_technicalpapers/18