偵測曲線峰谷位置是個常見問題，本論文中提出一種偵測方法作為部分節點選取方式，先透過統計檢定方法初步抓取峰谷位置，再利用保序迴歸(isotonic regression)計算殘差平方和評估偵測位置的優劣並進行優化改善偵測點。最後再使用樣條函數配適曲線。

在週期函數的資料下，表現會比其他偵測峰谷演算法來配適樣條函數表現來得好，
但是根據不同參數設置下，在模擬試驗有發現可能漏抓峰谷點的情況，其配適結果明顯能看出有異，而此情況在震盪幅度或週期不一的峰谷資料下更常出現，所以未來可以繼續在演算法上面做改進。

Peak and valley detection is a common problem, we propose a peak-finding method as a selection approach for some konts. In this study, we first finds peaks and valleys locations roughly via hypothesis test, then optimizes the locations
more precisely by minimizing the RSS of Isotonic regression.Finally, fit the curve by b-spline function.

The performance of our study is better than others
peak-finding method in periodic data. But according to
different combination of argument, there are some situations
that some peaks or valleys are not detected in simulation,
and this kind of mischance are much common in complicated
amplitude or non-periodic data.

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