{"categories":["Fundamentals of Time Series Analysis","time series analysis","Spectral Analysis","R","Python"],"author_name":"chaos_kiyono","published":"2026-01-28 22:17:48","width":"100%","blog_title":"Ken-Chaos\u2019s Random Notes on R","blog_url":"https://chaos-r.hatenadiary.jp/","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fchaos-r.hatenadiary.jp%2Fentry%2F2026%2F01%2F28%2F221748\" title=\"Nonparametric vs. Parametric Methods for Power Spectral Density (PSD) Estimation - Ken-Chaos\u2019s Random Notes on R\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","provider_name":"Hatena Blog","url":"https://chaos-r.hatenadiary.jp/entry/2026/01/28/221748","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/c/chaos_kiyono/20251230/20251230200120.png","title":"Nonparametric vs. Parametric Methods for Power Spectral Density (PSD) Estimation","description":"There are many ways to estimate the power spectral density (PSD) from a finite-length discrete time series. Most practical methods fall into two broad categories: Nonparametric methods Parametric methods Both aim to answer the same question\u2014how strongly each frequency component is present in the dat\u2026","version":"1.0","height":"190","provider_url":"https://hatena.blog","type":"rich","author_url":"https://blog.hatena.ne.jp/chaos_kiyono/"}