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Empirical detection of parameter variation in growth curve models using interval specific estimators

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  • Karim, Md Aktar Ul
  • Bhagat, Supriya Ramdas
  • Bhowmick, Amiya Ranjan

Abstract

Quantitative assessment of the growth of biological organisms has produced many mathematical equations, and over time, it has become an independent research area. Many efforts have been given on statistical identification of the correct growth model from a given data set, and have generated many model selection criteria as well. Every growth equation is unique in terms of mathematical structure; however, one model may serve as a close approximation of another equation by some appropriate choice of the parameter(s). It is still an interesting problem to select the best estimating model from a set of models whose shapes are similar in nature. In this manuscript, we utilize an existing model selection criterion which reduces the number of model fitting exercises substantially. By using continuous transformation of parameters, interconnections between many existing equations can be made. We consider four basic models, namely, exponential, logistic, confined exponential, and theta-logistic, as a starting point. Starting with these basic models, we utilize the idea of interval-specific rate parameter (ISRP), proposed by Bhowmick et al. (J. Biol. Phys., Vol 40, pp. 71–95, 2014) to obtain the best model for real data sets. The ISRP profiles of the parameters of simpler models indicate the nature of variation in parameters as a function of time, enabling the experimenter to extrapolate the inference to more complex models. Our proposed methodology significantly reduces the efforts involved in model fitting exercises. Connections have been built amongst many growth equations, which were studied independently to date by researchers. We believe that this work will be helpful for practitioners in the field of growth study. The proposed idea is verified by using simulated, and real data sets.

Suggested Citation

  • Karim, Md Aktar Ul & Bhagat, Supriya Ramdas & Bhowmick, Amiya Ranjan, 2022. "Empirical detection of parameter variation in growth curve models using interval specific estimators," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
  • Handle: RePEc:eee:chsofr:v:157:y:2022:i:c:s0960077922001126
    DOI: 10.1016/j.chaos.2022.111902
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    References listed on IDEAS

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    1. Francisco Louzada & Paulo H. Ferreira & Carlos A.R. Diniz, 2014. "Skew-normal distribution for growth curve models in presence of a heteroscedasticity structure," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1785-1798, August.
    2. Bhowmick, Amiya Ranjan & Saha, Bapi & Chattopadhyay, Joydev & Ray, Santanu & Bhattacharya, Sabyasachi, 2015. "Cooperation in species: Interplay of population regulation and extinction through global population dynamics database," Ecological Modelling, Elsevier, vol. 312(C), pages 150-165.
    3. Soetaert, Karline & Petzoldt, Thomas & Setzer, R. Woodrow, 2010. "Solving Differential Equations in R: Package deSolve," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i09).
    4. Michael G. Kenward, 1987. "A Method for Comparing Profiles of Repeated Measurements," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 296-308, November.
    5. Chakraborty, Biman & Bhowmick, Amiya Ranjan & Chattopadhyay, Joydev & Bhattacharya, Sabyasachi, 2017. "Physiological responses of fish under environmental stress and extension of growth (curve) models," Ecological Modelling, Elsevier, vol. 363(C), pages 172-186.
    6. Alexander Shapiro & Jos Berge, 2002. "Statistical inference of minimum rank factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 79-94, March.
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    Cited by:

    1. Karim, Md Aktar Ul & Aithal, Vikram & Bhowmick, Amiya Ranjan, 2023. "Random variation in model parameters: A comprehensive review of stochastic logistic growth equation," Ecological Modelling, Elsevier, vol. 484(C).

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