• Corpus ID: 245836921

Bayesian Changepoint Estimation for Spatially Indexed Functional Time Series (preprint)/ en

  title={Bayesian Changepoint Estimation for Spatially Indexed Functional Time Series (preprint)/ en},
  author={Mengchen Wang and Trevor Harris and B. Li},
We propose a Bayesian hierarchical model to simultaneously estimate mean based changepoints in spatially correlated functional time series. Unlike previous methods that assume a shared changepoint at all spatial locations or ignore spatial correlation, our method treats changepoints as a spatial process. This allows our model to respect spatial heterogeneity and exploit spatial correlations to improve estimation. Our method is derived from the ubiquitous cumulative sum (CUSUM) statistic that… 



Detection of Local Differences in Spatial Characteristics Between Two Spatiotemporal Random Fields

Abstract Comparing the spatial characteristics of spatiotemporal random fields is often at demand. However, the comparison can be challenging due to the high-dimensional feature and dependency in the

Scalable multiple changepoint detection for functional data sequences

The Multiple Changepoint Isolation method for detecting multiple changes in the mean and covariance of a functional process is proposed and demonstrated on a large time series of water vapor mixing ratio profiles from atmospheric emitted radiance interferometer measurements.

Nonparametric change point detection in multivariate piecewise stationary time series

ABSTRACT Detecting change points in multivariate time series is an important problem with numerous applications. We develop a nonparametric method to detect multiple change points in multivariate

Bayesian change point detection for functional data

  • Xiuqi LiS. Ghosal
  • Computer Science, Mathematics
    Journal of Statistical Planning and Inference
  • 2021

Testing the structural stability of temporally dependent functional observations and application to climate projections

Abstract: We develop a self-normalization (SN) based test to test the structural stability of temporally dependent functional observations. Testing for a change point in the mean of functional data

Estimation and testing for spatially indexed curves with application to ionospheric and magnetic field trends

We develop methodology for the estimation of the functional mean and the functional principal components when the functions form a spatial process. The data consist of curves

Changepoint Detection in Periodic and Autocorrelated Time Series

Abstract Undocumented changepoints (inhomogeneities) are ubiquitous features of climatic time series. Level shifts in time series caused by changepoints confound many inference problems and are very

Local Prediction of a Spatio-Temporal Process with an Application to Wet Sulfate Deposition

Abstract A prediction method is given for a first- and second-order nonstationary spatio-temporal process. The predictor uses local data only and consists of a two-stage generalized regression

Multiple‐change‐point detection for auto‐regressive conditional heteroscedastic processes

A fast, well performing and theoretically tractable method for detecting multiple change points in the structure of an auto‐regressive conditional heteroscedastic model for financial returns with piecewise constant parameter values is proposed.