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Pré-Publication, Document De Travail Année : 2022

Em algorithm for generalized ridge regression with spatial covariates

Résumé

The generalized Ridge penalty is a powerful tool for dealing with overfitting and for high-dimensional regressions. The generalized Ridge regression can be derived as the mean of a posterior distribution with a Normal prior and a given covariance matrix. The covariance matrix controls the structure of the coefficients, which depends on the particular application. For example, it is appropriate to assume that the coefficients have a spatial structure in spatial applications. This study proposes an expectation-maximization algorithm for estimating generalized Ridge parameters whose covariance structure depends on specific parameters. We focus on three cases: diagonal (when the covariance matrix is diagonal with constant elements), Matérn, and conditional autoregressive covariances. A simulation study is conducted to evaluate the performance of the proposed method, and then the method is applied to predict ocean wave heights using wind conditions.
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Dates et versions

hal-03825411 , version 1 (22-10-2022)

Identifiants

  • HAL Id : hal-03825411 , version 1

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Valérie Monbet, Said Obakrim, Nicolas Raillard, Pierre Ailliot. Em algorithm for generalized ridge regression with spatial covariates. 2022. ⟨hal-03825411⟩
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