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Cross-validation estimation of covariance parameters under fixed-domain asymptotics
Statistics Theory · Submitted 10 Oct 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Under fixed-domain asymptotics, we prove the strong consistency and asymptotic normality of a cross validation estimator of the microergodic covariance parameter.
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The abstract, sentence by sentence
We consider a one-dimensional Gaussian process having exponential covariance function. Under fixed-domain asymptotics, we prove the strong consistency and asymptotic normality of a cross validation estimator of the microergodic covariance parameter. In this setting, Ying [40] proved the same asymptotic properties for the maximum likelihood estimator. Our proof includes several original or more involved components, compared to that of Ying. Also, while the asymptotic variance of maximum likelihood does not depend on the triangular array of observation points under consideration, that of cross validation does, and is shown to be lower and upper bounded. The lower bound coincides with the asymptotic variance of maximum likelihood. We provide examples of triangular arrays of observation points achieving the lower and upper bounds. We illustrate our asymptotic results with simulations, and provide extensions to the case of an unknown mean function. To our knowledge, this work constitutes the first fixed-domain asymptotic analysis of cross validation.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We consider a one-dimensional Gaussian process having exponential covariance function. | background 0.59 | 0.29 | 0.02 | asserted 0.97 |
| s1 | Under fixed-domain asymptotics, we prove the strong consistency and asymptotic normality of a cross validation estimator of the microergodic covariance parameter. | result 0.73 | 0.96 | 0.86 | asserted 1.00 |
| s2 | In this setting, Ying [40] proved the same asymptotic properties for the maximum likelihood estimator. | background 1.00 | 0.03 | 0.06 | asserted 1.00 |
| s3 | Our proof includes several original or more involved components, compared to that of Ying. | contribution 0.95 | 0.78 | 0.05 | asserted 1.00 |
| s4 | Also, while the asymptotic variance of maximum likelihood does not depend on the triangular array of observation points under consideration, that of cross validation does, and is shown to be lower and upper bounded. | result 0.99 | 0.90 | 0.13 | asserted 1.00 |
| s5 | The lower bound coincides with the asymptotic variance of maximum likelihood. | result 0.99 | 0.82 | 0.10 | asserted 1.00 |
| s6 | We provide examples of triangular arrays of observation points achieving the lower and upper bounds. | contribution 0.58 | 0.95 | 0.05 | asserted 1.00 |
| s7 | We illustrate our asymptotic results with simulations, and provide extensions to the case of an unknown mean function. | contribution 0.69 | 0.95 | 0.06 | asserted 1.00 |
| s8 | To our knowledge, this work constitutes the first fixed-domain asymptotic analysis of cross validation. | gap 0.60 | 0.47 | 0.16 | asserted 0.81 |
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The paper
Original abstract
We consider a one-dimensional Gaussian process having exponential covariance function. Under fixed-domain asymptotics, we prove the strong consistency and asymptotic normality of a cross validation estimator of the microergodic covariance parameter. In this setting, Ying [40] proved the same asymptotic properties for the maximum likelihood estimator. Our proof includes several original or more involved components, compared to that of Ying. Also, while the asymptotic variance of maximum likelihood does not depend on the triangular array of observation points under consideration, that of cross validation does, and is shown to be lower and upper bounded. The lower bound coincides with the asymptotic variance of maximum likelihood. We provide examples of triangular arrays of observation points achieving the lower and upper bounds. We illustrate our asymptotic results with simulations, and provide extensions to the case of an unknown mean function. To our knowledge, this work constitutes the first fixed-domain asymptotic analysis of cross validation.
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