宠辱不惊淡看庭前花开花谢, 去留 ...分享 http://blog.sciencenet.cn/u/zhangshibin 专业: 概率论与数理统计 研究方向: 时空数据统计分析,包括随机过程统计、时间序列分析、空间统计、统计计算、贝叶斯统计等

博文

NW: Spectral density for irregularly spaced data

已有 1797 次阅读 2020-6-5 08:25 |个人分类:学术成果|系统分类:论文交流

https://www.sciencedirect.com/science/article/pii/S0167947320301109


Computational Statistics & Data Analysis

Volume 151, November 2020, 107019


Nonparametric Bayesian inference for the spectral density based on irregularly spaced data

Author links open overlay panelShibinZhang

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https://doi.org/10.1016/j.csda.2020.107019

Abstract

Various approaches for spectral analysis based on regularly spaced data have already been well-established, but the spectral inference based on irregularly spaced data are still essentially limited. Under the Bayesian framework, a detouring approach for spectral estimation is proposed for analyzing irregularly spaced data. The detouring process is accomplished by three steps: (1) normalizing the data in some sense on frequency domain by a time-scale change, (2) estimating the spectral density of the time-scale changed process, and (3) solving the estimated spectrum by the relation of spectral densities between the model and its time-scale-changed version. The proposed approach uses a Hamiltonian Monte Carlo—within Gibbs technique to fit smoothing splines to the periodogram. Our technique produces an automatically smoothed spectral estimate. The time-scale-change not only allows basis functions in the smoothing splines to be independent of sampling design, but also makes the proposed estimation need not to adjust tuning parameters according to different irregularly spaced data.

Keywords

Irregularly spaced data

Periodogram

Spectral density

Gibbs sampler

Hamiltonian Monte Carlo

Smoothing spline


https://www.sciencedirect.com/science/article/pii/S0167947320301109



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