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http://dx.doi.org/10.5351/KJAS.2017.30.6.877

Bayesian estimation for frequency using resampling methods  

Pak, Ro Jin (Department of Applied Statistics, Dankook University)
Publication Information
The Korean Journal of Applied Statistics / v.30, no.6, 2017 , pp. 877-888 More about this Journal
Abstract
Spectral analysis is used to determine the frequency of time series data. We first determine the frequency of the series through the power spectrum or the periodogram and then calculate the period of a cycle that may exist in a time series. Estimating the frequency using a Bayesian technique has been developed and proven to be useful; however, the Bayesian estimator for the frequency cannot be analytically solved through mathematical equations and may be handled numerically or computationally. In this paper, we make an inference on the Bayesian frequency through both resampling a parameter by Markov chain Monte Carlo (MCMC) methods and resampling data by bootstrap methods for a time series. We take the Korean real estate price index as an example for Bayesian frequency estimation. We have found a difference in the periods between the sale price index and the long term rental price index, but the difference is not statistically significant.
Keywords
filtering; fourier transform; Markov chain Monte Carlo; R-package; signal processing; spectral analysis; spectrum; time series;
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