Yao Q, Tong H, Finkenstädt B, Stenseth N C
Department of Statistics, London School of Economics, UK.
Proc Biol Sci. 2000 Dec 7;267(1460):2459-67. doi: 10.1098/rspb.2000.1306.
Typically, in many studies in ecology, epidemiology, biomedicine and others, we are confronted with panels of short time-series of which we are interested in obtaining a biologically meaningful grouping. Here, we propose a bootstrap approach to test whether the regression functions or the variances of the error terms in a family of stochastic regression models are the same. Our general setting includes panels of time-series models as a special case. We rigorously justify the use of the test by investigating its asymptotic properties, both theoretically and through simulations. The latter confirm that for finite sample size, bootstrap provides a better approximation than classical asymptotic theory. We then apply the proposed tests to the mink-muskrat data across 81 trapping regions in Canada. Ecologically interpretable groupings are obtained, which serve as a necessary first step before a fuller biological and statistical analysis of the food chain interaction.
通常,在生态学、流行病学、生物医学等众多研究中,我们会遇到一系列短时间序列,我们希望对其进行具有生物学意义的分组。在此,我们提出一种自助法来检验一族随机回归模型中回归函数或误差项的方差是否相同。我们的一般设定将时间序列模型的面板作为一种特殊情况包含在内。我们通过理论研究和模拟来考察其渐近性质,从而严格证明该检验方法的合理性。模拟结果证实,对于有限样本量,自助法比经典渐近理论能提供更好的近似。然后,我们将所提出的检验方法应用于加拿大81个捕获区域的水貂 - 麝鼠数据。得到了具有生态学可解释性的分组,这是对食物链相互作用进行更全面的生物学和统计分析之前必要的第一步。