论文标题
逻辑回归简介
Introduction to logistic regression
论文作者
论文摘要
对于基于脑成像中基于多重比较校正的随机场理论，通常有必要计算随机场的至高无上的分布。不幸的是，计算随机字段至上的分布并不容易，并且需要满足实际数据中可能不正确的许多分布假设。因此，有必要提出一个不同的框架，该框架不使用需要计算P值的传统统计假设测试范式。以此为动机，我们可以使用一种称为逻辑回归的方法，该方法不需要计算P值，并且仍然能够定位大脑网络差异的区域。与试图对预选为特征向量进行分类的其他判别和分类技术不同，此处的方法不需要任何预选的特征向量，并且可以在每个边缘级别执行分类。
For random field theory based multiple comparison corrections In brain imaging, it is often necessary to compute the distribution of the supremum of a random field. Unfortunately, computing the distribution of the supremum of the random field is not easy and requires satisfying many distributional assumptions that may not be true in real data. Thus, there is a need to come up with a different framework that does not use the traditional statistical hypothesis testing paradigm that requires to compute p-values. With this as a motivation, we can use a different approach called the logistic regression that does not require computing the p-value and still be able to localize the regions of brain network differences. Unlike other discriminant and classification techniques that tried to classify preselected feature vectors, the method here does not require any preselected feature vectors and performs the classification at each edge level.
