We study a likelihood ratio test for detecting multiple {\it weak} changes in the mean of a class of CHARN models. The locally asymptotically normal (LAN) structure of the family of likelihoods under study is established. It results that the test is asymptotically optimal, and an explicit form of its asymptotic local power is given as a function of candidates change locations and changes magnitudes. Strategies for weak change-points detection and their locations estimates are described. The estimates are obtained as the time indices maximizing an estimate of the local power. A simulation study shows the good performance of our methods compared to some existing approaches. Our results are also applied to three sets of real data.

翻译：我们研究一种可能性比率测试,以检测某类CHARN模型平均值的多重(薄弱)变化。正在研究的概率家庭在当地的零星正常(局域网)结构已经确立。它的结果是,该测试在瞬间是最佳的,其无症状地方力量的明确形式是候选人改变地点和变化幅度的函数。描述了薄弱变化点探测战略及其地点估计。这些估计数是作为时间指数获得的,以最大限度地估计当地力量。模拟研究显示,与某些现有方法相比,我们的方法表现良好。我们的结果也适用于三套真实数据。