Probability density functions useful for parametrization of heterogeneity in growth and allometry data

This paper addresses the problem of modelling heterogeneous individual characteristics in a population. A flexible unified approach for stochastic parametrization dynamics of the distribution in population data is proposed. To approximate data with multiple observations per individual, models based on Markov processes are constructed. The method can be applied to scalar or multivariate characteristics, and its application to growth and allometry data is considered. Different stochastic versions of known growth and allometry functions are developed, which enable wide applicability. Simple informative growth indices are calculated as the moments of distribution. The three-parameter Gompertz growth model for size-at-age data was reparametrized to a size-increment data model with two parameters.