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Bull Math Biol. 2019 Jul;81(7):2529-2552. doi: 10.1007/s11538-019-00617-w. Epub 2019 Jun 7.
2
Critical thresholds for eventual extinction in randomly disturbed population growth models.随机扰动种群增长模型中最终灭绝的临界阈值。
J Math Biol. 2018 Aug;77(2):495-525. doi: 10.1007/s00285-018-1217-y. Epub 2018 Feb 16.
3
Evolution of model specific relative growth rate: Its genesis and performance over Fisher's growth rates.特定模型相对生长率的演变:其起源及与费希尔生长率相比的表现。
J Theor Biol. 2018 May 7;444:11-27. doi: 10.1016/j.jtbi.2018.02.012. Epub 2018 Feb 13.
4
Deciphering death: a commentary on Gompertz (1825) 'On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies'.解读死亡:对戈姆珀茨(1825年)《论表达人类死亡率规律的函数的性质,以及确定生命意外事件价值的一种新模式》的评论
Philos Trans R Soc Lond B Biol Sci. 2015 Apr 19;370(1666). doi: 10.1098/rstb.2014.0379.
5
A new growth curve model for biological growth: some inferential studies on the growth of Cirrhinus mrigala.
Math Biosci. 2014 Aug;254:28-41. doi: 10.1016/j.mbs.2014.06.004. Epub 2014 Jun 14.
6
Simultaneous identification of growth law and estimation of its rate parameter for biological growth data: a new approach.生物生长数据生长规律的同时识别及其速率参数估计:一种新方法。
J Biol Phys. 2014 Jan;40(1):71-95. doi: 10.1007/s10867-013-9336-6. Epub 2014 Jan 10.
7
Goodness-of-fit methods for generalized linear mixed models.广义线性混合模型的拟合优度方法。
Biometrics. 2005 Dec;61(4):1000-9. doi: 10.1111/j.1541-0420.2005.00365.x.
8
On the regulation of populations of mammals, birds, fish, and insects.论哺乳动物、鸟类、鱼类和昆虫种群的调控
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9
The modelled growth of mycorrhizal and non-mycorrhizal plants under constant versus variable soil nutrient concentration.在恒定与可变土壤养分浓度下菌根植物和非菌根植物的模拟生长情况。
Mycorrhiza. 2002 Oct;12(5):257-61. doi: 10.1007/s00572-002-0178-5. Epub 2002 Jun 15.
10
Analysis of logistic growth models.逻辑斯蒂增长模型分析
Math Biosci. 2002 Jul-Aug;179(1):21-55. doi: 10.1016/s0025-5564(02)00096-2.

瞬时成熟率:一种新颖紧凑的生物生长曲线模型特征描述。

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

机构信息

Department of Mathematics and Statistics, Aliah University, IIA/27, New Town, Kolkata, 700160, India.

Department of Mathematics, Institute of Chemical Technology, Mumbai, 400019, India.

出版信息

J Biol Phys. 2022 Sep;48(3):295-319. doi: 10.1007/s10867-022-09609-9. Epub 2022 Jul 2.

DOI:10.1007/s10867-022-09609-9
PMID:35779141
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9411411/
Abstract

Modeling and analysis of biological growth curves are an age-old study area in which much effort has been dedicated to developing new growth equations. Recent efforts focus on identifying the correct model from a large number of equations. The relative growth rate (RGR), developed by Fisher (1921), has largely been used in the statistical inference of biological growth curve models. It is convenient to express growth equations using RGR, where RGR can be expressed as functions of size or time. Even though RGR is model invariant, it has limitations when it comes to identifying actual growth patterns. By proposing interval-specific rate parameters (ISRPs), Pal et al. (2018) appeared to solve this problem. The ISRP is based on the mathematical structure of the growth equations. Therefore, it is not model invariant. The current effort is to develop a measure of growth that is model invariant like RGR and shares the advantages of ISRP. We propose a new measure of growth, which we call instantaneous maturity rate (IMR). IMR is model invariant, which allows it to distinguish growth patterns more clearly than RGR. IMR is also scale-invariant and can take several forms including increasing, decreasing, constant, sigmoidal, bell-shaped, and bathtub. A wide range of possible IMR shapes makes it possible to identify different growth curves. The estimation procedure of IMR under a stochastic setup has been developed. Statistical properties of empirical IMR estimators have also been investigated in detail. In addition to extensive simulation studies, real data sets have been analyzed to prove the utility of IMR.

摘要

生物生长曲线的建模与分析是一个由来已久的研究领域,人们投入了大量精力来开发新的生长方程。最近的研究重点是从大量方程中确定正确的模型。相对生长率(RGR)由 Fisher(1921)提出,在生物生长曲线模型的统计推断中得到了广泛应用。使用 RGR 来表达生长方程很方便,其中 RGR 可以表示为大小或时间的函数。尽管 RGR 是模型不变的,但在识别实际生长模式时存在局限性。Pal 等人(2018)通过提出特定区间的速率参数(ISRP)似乎解决了这个问题。ISRP 基于生长方程的数学结构,因此不是模型不变的。目前的工作是开发一种与 RGR 一样具有模型不变性并具有 ISRP 优势的生长度量。我们提出了一种新的生长度量,称为瞬时成熟率(IMR)。IMR 是模型不变的,这使得它比 RGR 更能清晰地区分生长模式。IMR 也是尺度不变的,可以采用多种形式,包括递增、递减、常数、S 形、钟形和浴盆形。IMR 可能具有多种形状,这使得识别不同的生长曲线成为可能。我们已经开发了在随机设置下估计 IMR 的程序,并详细研究了经验 IMR 估计量的统计性质。除了广泛的模拟研究外,还分析了真实数据集以证明 IMR 的实用性。