Discrimination Between Two Species ofMicrotususing both Classified and Unclassified Observations
Discrimination Between Two Species ofMicrotususing both Classified and Unclassified Observations
复制标题
使用分类和非分类观察来区分两种田鼠
DOI：
10.1006/jtbi.1995.0242
复制
发表时间：
1995
影响因子：
2
通讯作者：
M. Salvioni
中科院分区：
文献类型：
--
作者：
J. Airoldi;Bernard D. Flury;M. Salvioni
本文分析了288例亚高山田鼠（Microtus submergyeus）和蒙古田鼠（M.多路复用对89份标本的染色体进行了分析，以确定物种;其余199份标本的物种未知。在这种情况下，可以使用分类的观察来估计判别函数（这是判别分析的传统方法），或者也可以尝试使用未分类的观察来改进参数估计。后一种情况，我们称之为Discrimix是判别分析和有限混合分析的组合，这在生物学家中似乎基本上是未知的。然而，该方法的统计理论是相当发达的名称下“歧视分析与部分分类的数据”，有可能大大提高估计的分类规则，如我们所示，使用Microtus数据。与有限混合分析一样，Discrimix需要迭代计算来估计参数，但其优点是充分利用分类和未分类观测中包含的信息来构建分类规则。我们说明了传统的判别分析和Discrimix在单变量和多变量的情况下，并使用自举方法表明，从Discrimix获得的估计是更稳定的（即有较少的变异性）比从判别分析。
Abstract We analyse a set of morphometric data obtained from the skulls of 288 specimens of Microtus subterraneus and M. multiplex . The chromosomes of 89 specimens were analyzed to identify the species; species is unknown for the remaining 199 specimens. In this situation one may either use the classified observations to estimate a discriminant function (this is the traditional approach of discriminant analysis), or one may attempt to use the unclassified observations as well to improve parameter estimation. The latter case, which we refer to as Discrimix is a combination of discriminant analysis and finite mixture analysis which appears to be essentially unknown among biologists. Yet the method, the statistical theory of which is fairly well developed under the name “discriminate analysis with partially classified data“, has the potential to greatly improve the estimation of classification rules, as we illustrate using the Microtus data. Like finite mixture analysis, Discrimix requires iterative computations to estimate the parameters, but has the advantage of fully using the information contained in both the classified and the unclassified observations to construct the classification rule. We illustrate both traditional discriminant analysis and Discrimix in the univariate and the multivariate case, and use a bootstrap method to show that the estimates obtained from Discrimix are more stable (that is, have less variability) than those obtained from discriminant analysis.