Beyond Matérn: On A Class of Interpretable Confluent Hypergeometric Covariance Functions

نویسندگان

چکیده

The Matérn covariance function is a popular choice for prediction in spatial statistics and uncertainty quantification literature. A key benefit of the class that it possible to get precise control over degree mean-square differentiability random process. However, possesses exponentially decaying tails, thus, may not be suitable modeling polynomially dependence. This problem can remedied using polynomial covariances; however, one loses corresponding processes, processes with existing covariances are either infinitely differentiable or nowhere at all. We construct new family functions called Confluent Hypergeometric (CH) scale mixture representation where obtains benefits both covariances. resultant contains two parameters: controls near origin other tail heaviness, independently each other. Using spectral representation, we derive theoretical properties this including equivalent measures asymptotic behavior maximum likelihood estimators under infill asymptotics. improved CH verified via extensive simulations. Application NASA’s Orbiting Carbon Observatory-2 satellite data confirms advantage class, especially extrapolative settings. Supplementary materials article available online.

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ژورنال

عنوان ژورنال: Journal of the American Statistical Association

سال: 2022

ISSN: ['0162-1459', '1537-274X', '2326-6228', '1522-5445']

DOI: https://doi.org/10.1080/01621459.2022.2027775