Prediction for Individual Growth in a Random Environment

Nonlinear growth models are widely employed in Animal sciences for describing growth of various species ofanimals. Nonlinear estimation procedures are generally employed for estimation of parameters. However, onelimitation of these models is that they are applicable only when the data are available at equidistant epochs. Another limitation is that the fluctuations in the system cannot be satisfactorily explained simply by adding an error term to the deterministic formulation. The purpose of this article is to bring to the notice of Animal scientists the new approach of Stochastic differential equation modelling, which is capable of incorporating both the above aspects. The methodology is discussed by considering Gompertz growth model. Relevant SAS codes for fitting the model are developed. Finally, the methodology is illustrated on secondary monthly pig weight data, collected at thepiggery farm of Indian Veterinary Research Institute, Izatnagar, Bareilly, India.

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“…Following along similar lines as Filipe et al (2013), analogous Gompertz SDE model with constant diffusion coefficient is given by…”

Section: Methodsmentioning

confidence: 99%

A new approach for fitting growth models in random environment

PRAJNESHU

Ghosh

2018

Indian J of Anim Sci

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“…So, differential equation for growth process l t should be considered as stochastic in nature or equivalently the model for changes δl t = l t+δt -l t , rather than l t should be considered. Therefore, following along similar lines as Filipe et al (2013), analogous VBSDE model with constant diffusion coefficient is given by:…”

Section: Methodsmentioning

confidence: 99%

Fitting of von Bertalanffy growth model: Stochastic differential equation approach

Gupta¹,

Ghosh²,

Pandey³

2017

Indian J. Fish.

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In this paper, the well-known von Bertalanffy growth (VBG) model for estimating age-length relationship in fisheries is considered. It is emphasised that nonlinear estimation procedures should be adopted for fitting the von Bertalanffy nonlinear statistical (VBNS) model rather than the age-old Ford-Walford plot. Some limitations of employing VBNS modelling approach are highlighted. Employment of stochastic differential equation (SDE) approach, which does not suffer from these limitations, is advocated for fitting the VBG model. The methodology for fitting the von Bertalanffy SDE (VBSDE) model is described. Relevant computer code for fitting this model is written in SAS package and the same is included as an Appendix. Finally, as an illustration, superiority of VBSDE model over VBNS model for fitting and forecasting purposes is shown for rainbow trout (Onchorhynchus mykiss) age-length data.

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Optimum Fitting of Richards Growth Model in Random Environment

Ghosh

Prajneshu

2018

J Stat Theory Pract

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Prediction for Individual Growth in a Random Environment

Cited by 4 publications

References 2 publications

A new approach for fitting growth models in random environment

A new approach for fitting growth models in random environment

Fitting of von Bertalanffy growth model: Stochastic differential equation approach

Optimum Fitting of Richards Growth Model in Random Environment

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