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基于对数似然比分布的离子通道门控模型的统计评估。

Statistical evaluation of ion-channel gating models based on distributions of log-likelihood ratios.

作者信息

Csanády László

机构信息

Department of Medical Biochemistry, Semmelweis University, and Neurochemical Group of the Hungarian Academy of Sciences, Budapest, Hungary.

出版信息

Biophys J. 2006 May 15;90(10):3523-45. doi: 10.1529/biophysj.105.075135. Epub 2006 Feb 3.

Abstract

The distributions of log-likelihood ratios (DeltaLL) obtained from fitting ion-channel dwell-time distributions with nested pairs of gating models (Xi, full model; Xi(R), submodel) were studied both theoretically and using simulated data. When Xi is true, DeltaLL is asymptotically normally distributed with predictable mean and variance that increase linearly with data length (n). When Xi(R) is true and corresponds to a distinct point in full parameter space, DeltaLL is Gamma-distributed (2DeltaLL is chi-square). However, when data generated by an l-component multiexponential distribution are fitted by l+1 components, Xi(R) corresponds to an infinite set of points in parameter space. The distribution of DeltaLL is a mixture of two components, one identically zero, the other approximated by a Gamma-distribution. This empirical distribution of DeltaLL, assuming Xi(R), allows construction of a valid log-likelihood ratio test. The log-likelihood ratio test, the Akaike information criterion, and the Schwarz criterion all produce asymmetrical Type I and II errors and inefficiently recognize Xi, when true, from short datasets. A new decision strategy, which considers both the parameter estimates and DeltaLL, yields more symmetrical errors and a larger discrimination power for small n. These observations are explained by the distributions of DeltaLL when Xi or Xi(R) is true.

摘要

通过使用嵌套的门控模型对(Xi,完整模型;Xi(R),子模型)拟合离子通道驻留时间分布获得的对数似然比(DeltaLL)的分布,我们进行了理论研究并使用模拟数据进行了分析。当Xi为真时,DeltaLL渐近正态分布,其均值和方差可预测,且随数据长度(n)线性增加。当Xi(R)为真且对应于完整参数空间中的一个不同点时,DeltaLL呈伽马分布(2DeltaLL服从卡方分布)。然而,当由l分量多指数分布生成的数据用l + 1个分量拟合时,Xi(R)对应于参数空间中的一组无限点。DeltaLL的分布是两个分量的混合,一个恒为零,另一个近似为伽马分布。假设Xi(R)时,DeltaLL的这种经验分布允许构建有效的对数似然比检验。对数似然比检验、赤池信息准则和施瓦茨准则在处理短数据集时,都会产生不对称的I型和II型错误,并且在Xi为真时不能有效地识别它。一种新的决策策略,同时考虑参数估计和DeltaLL,对于小样本量n能产生更对称的错误和更大的辨别力。这些观察结果通过当Xi或Xi(R)为真时DeltaLL的分布来解释。

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