Closed-form likelihoods for stochastic differential equation growth models

被引:2
作者
Paige, Robert [1 ]
Allen, Edward [2 ]
机构
[1] Missouri Univ Sci & Technol, Dept Math & Stat, Rolla, MO 65409 USA
[2] Texas Tech Univ, Dept Math & Stat, Lubbock, TX 79409 USA
来源
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2010年 / 38卷 / 03期
关键词
Growth modelling; likelihood estimation; pseudo-likelihoods; stochastic differential equations; MAXIMUM-LIKELIHOOD; PARAMETERS; DIFFUSIONS;
D O I
10.1002/cjs.10071
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The authors derive closed-form expressions for the full, profile, conditional and modified profile likelihood functions for a class of random growth parameter models they develop as well as Garcia's additive model. These expressions facilitate the determination of parameter estimates for both types of models. The profile, conditional and modified profile likelihood functions are maximized over few parameters to yield a complete set of parameter estimates. In the development of their random growth parameter models the authors specify the drift and diffusion coefficients of the growth parameter process in a natural way which gives interpretive meaning to these coefficients while yielding highly tractable models. They fit several of their random growth parameter models and Garcia's additive model to stock market data, and discuss the results. The Canadian Journal of Statistics 38: 474-487; 2010 (C) 2010 Statistical Society of Canada
引用
收藏
页码:474 / 487
页数:14
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