Maximum Likelihood Estimation of Multiple Change Points
研究了在独立分类随机变量序列中，当变点间子序列长度有已知下界时，如何用最大似然估计定位变点，并讨论了变段数量估计和边界分布差异问题，应用于蛋白质螺旋区预测。
Maximum likelihood estimation of the locations of changes in sequences of independent categorical random variables when there are known lower bounds on the lengths of the sub-sequences between the change points is discussed. A method is developed which finds one of the maximum likelihood solutions. The problem of estimating the number of changed segments is discussed and some numerical results about the precision of the estimates of the locations of changes are presented. The method also allows the boundary distributions for the changed segments to be different from the distribution for the central region of the changed segments. An application to the prediction of protein helical regions is presented.