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Estimation of Non-stationary Covariance Function with/without Replications

Abstract Number:

3200 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

YIYING Fan (1), Jiayang Sun (2)

Institutions:

(1) Cleveland State University, N/A, (2) George Mason University, N/A

Co-Author:

Jiayang Sun  
George Mason University

First Author:

YIYING Fan  
Cleveland State University

Presenting Author:

YIYING Fan  
Cleveland State University

Abstract Text:

Spatial statistics frequently involve modeling (transformed) data by a Gaussian process F, but the covariance of F may NOT be stationary. In the context of replicated observations of a Gaussian spatial field, this study introduces a nonparametric approach applicable for a general covariance function that may be non-stationary. For situations where only a single observation is available with no replication, a local block bootstrap procedure is proposed to generate additional observations. We compare the estimated covariance obtained through the empirical method with those from parametric and nonparametric methodologies. We discuss the strengths and limitations of each approach in capturing the complex covariance structure inherent in spatial data.

Keywords:

Gaussian process|non-stationary |nonparametric|bootstrap|Covariance Function |

Sponsors:

Section on Nonparametric Statistics

Tracks:

Nonparametric modeling

Can this be considered for alternate subtype?

Yes

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I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

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