基于马尔科夫链蒙特卡罗估计的Logistic增长模型

Q3 Mathematics

Journal of Modern Applied Statistical Methods Pub Date : 2020-01-01 DOI:10.22237/JMASM/1556669820

Jaehwa Choi, Jinsong Chen, Jeffrey R. Harring

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引用次数: 2

摘要

提出了一种新的增长建模方法，可以在没有约束和参数化的情况下拟合固有非线性(即逻辑)函数。通过仿真研究，探讨了在贝叶斯估计框架下马尔可夫链蒙特卡罗方法在测量场合的数量和时间以及样本量等操纵条件下估计logistic增长曲线模型的完全随机版本的可行性和性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Logistic Growth Modeling with Markov Chain Monte Carlo Estimation

A new growth modeling approach is proposed to can fit inherently nonlinear (i.e., logistic) function without constraint nor reparameterization. A simulation study is employed to investigate the feasibility and performance of a Markov chain Monte Carlo method within Bayesian estimation framework to estimate a fully random version of a logistic growth curve model under manipulated conditions such as the number and timing of measurement occasions and sample sizes.

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来源期刊

Journal of Modern Applied Statistical Methods STATISTICS & PROBABILITY-

CiteScore

0.50

自引率

0.00%

发文量

期刊介绍： The Journal of Modern Applied Statistical Methods is an independent, peer-reviewed, open access journal designed to provide an outlet for the scholarly works of applied nonparametric or parametric statisticians, data analysts, researchers, classical or modern psychometricians, and quantitative or qualitative methodologists/evaluators.