Affiliation:
1. Biomathematics Unit, Department of Zoology, Faculty of Life Sciences, Tel Aviv University
2. Woods Hole Oceanographic Institution, Woods Hole
Abstract
Existing methods for fitting a population model to time series data typically assume that the time series is complete. When there are missing values, it is common practice to substitute interpolated values. When the proportion of values that are missing is large, this can lead to bias in model-fitting. Here, we describe a maximum likelihood approach that allows explicitly for missing values. The approach is applied to a long weekly time series of the dinoflagellate Peridinium gatunense in Lake Kinneret, Israel, in which around 35% of the values are missing.
Subject
Animal Science and Zoology,Ecology, Evolution, Behavior and Systematics
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