Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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Pith papers citing it
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An INMA random field model for integer-valued spatial data is introduced, with closed-form marginal distributions, bivariate distributions, and autocovariances for arbitrary order including multilateral cases, and Poisson marginals are possible.
Fractional lower-order covariance yields new peFLOACF and peFLOPACF functions that enable dependence testing and periodic ARMA order identification for infinite-variance cyclostationary time series.
citing papers explorer
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Laplace Approximations for Mixed-Effects and Gaussian Process Quantile Regression
Laplace approximation framework for quantile regression with mixed-effects and Gaussian processes using Fisher information and population curvature of expected loss instead of observed Hessian.
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The Integer-valued Moving-Average Random Field
An INMA random field model for integer-valued spatial data is introduced, with closed-form marginal distributions, bivariate distributions, and autocovariances for arbitrary order including multilateral cases, and Poisson marginals are possible.
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Fractional lower-order covariance-based measures for cyclostationary time series with heavy-tailed distributions: application to dependence testing and model order identification
Fractional lower-order covariance yields new peFLOACF and peFLOPACF functions that enable dependence testing and periodic ARMA order identification for infinite-variance cyclostationary time series.