Stricker C, Redman S, Daley D
Division of Neuroscience, John Curtin School of Medical Research, Australian National University, Canberra.
Biophys J. 1994 Aug;67(2):532-47. doi: 10.1016/S0006-3495(94)80513-2.
Procedures for discriminating between competing statistical models of synaptic transmission, and for providing confidence limits on the parameters of these models, have been developed. These procedures were tested against simulated data and were used to analyze the fluctuations in synaptic currents evoked in hippocampal neurones. All models were fitted to data using the Expectation-Maximization algorithm and a maximum likelihood criterion. Competing models were evaluated using the log-likelihood ratio (Wilks statistic). When the competing models were not nested, Monte Carlo sampling of the model used as the null hypothesis (H0) provided density functions against which H0 and the alternate model (H1) were tested. The statistic for the log-likelihood ratio was determined from the fit of H0 and H1 to these probability densities. This statistic was used to determine the significance level at which H0 could be rejected for the original data. When the competing models were nested, log-likelihood ratios and the chi 2 statistic were used to determine the confidence level for rejection. Once the model that provided the best statistical fit to the data was identified, many estimates for the model parameters were calculated by resampling the original data. Bootstrap techniques were then used to obtain the confidence limits of these parameters.
已经开发出用于区分突触传递竞争统计模型以及为这些模型的参数提供置信限的程序。这些程序针对模拟数据进行了测试，并用于分析海马神经元中诱发的突触电流波动。所有模型均使用期望最大化算法和最大似然准则拟合数据。使用对数似然比（威尔克斯统计量）评估竞争模型。当竞争模型不嵌套时，用作原假设（H0）的模型的蒙特卡罗抽样提供了密度函数，据此对H0和备择模型（H1）进行检验。对数似然比的统计量由H0和H1对这些概率密度的拟合确定。该统计量用于确定在何种显著性水平下可以拒绝原数据的H0。当竞争模型嵌套时，使用对数似然比和卡方统计量来确定拒绝的置信水平。一旦确定了对数据提供最佳统计拟合的模型，就通过对原始数据重新采样来计算该模型参数的许多估计值。然后使用自助法技术获得这些参数的置信限。
