Literature DB >> 31175548

A Novel Unification Method to Characterize a Broad Class of Growth Curve Models Using Relative Growth Rate.

Biman Chakraborty1, Amiya Ranjan Bhowmick2, Joydev Chattopadhyay3, Sabyasachi Bhattacharya4.   

Abstract

Growth curve models serve as the mathematical framework for the quantitative studies of growth in many areas of applied science. The evolution of novel growth curves can be categorized in two notable directions, namely generalization and unification. In case of generalization, a modeler starts with a simple mathematical form to describe the behavior of the data and increases the complexity of the equation by incorporating more parameters to obtain a more flexible shape. The unification refers to the process of obtaining a compact representation of a large number of growth equations. An enormous number of growth equations are made available in the literature by means of the generalization of existing growth laws. However, the unification of growth equations has received relatively less attention from the researchers. Two significant unification functions are available in the literature, namely the Box-Cox transformation by Garcia (For Biometry Model Inf Sci 1:63-68, 2005) and generalized logarithmic and exponential functions by Martinez et al. (Phys A 387:5679-5687, 2008; Phys A 388:2922-2930, 2009). Existing unification approaches are found to have limited applications if the growth equation is characterized by the relative growth rate (RGR). RGR has immense practical value in biological growth curve analysis, which has been amplified by the construction of size and time covariate models, in which; RGR is represented either as a function of size or time or both. The present study offers a unification function for the RGR growth curves. The proposed function combines a broad class of the growth curves and possesses a greater generality than the existing unification functions. We also propose the notion of generalized RGR, which is capable of making interrelations among the unifying functions. Our proposed method is expected to enhance the generality of software and may aid in choosing an optimal model from a set of competitor growth equations.

Keywords:  Allometry; Box–Cox transform; Extended logistic; Generalized logarithm; Generalized relative growth rate

Year:  2019        PMID: 31175548     DOI: 10.1007/s11538-019-00617-w

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  2 in total

1.  Instantaneous maturity rate: a novel and compact characterization of biological growth curve models.

Authors:  Biman Chakraborty; Amiya Ranjan Bhowmick; Joydev Chattopadhyay; Sabyasachi Bhattacharya
Journal:  J Biol Phys       Date:  2022-07-02       Impact factor: 1.560

2.  Statistical analysis and first-passage-time applications of a lognormal diffusion process with multi-sigmoidal logistic mean.

Authors:  Antonio Di Crescenzo; Paola Paraggio; Patricia Román-Román; Francisco Torres-Ruiz
Journal:  Stat Pap (Berl)       Date:  2022-08-27       Impact factor: 1.523

  2 in total

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