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.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
CINAR random fields extend INAR models by decoupling the marginal distribution (chosen from discrete self-decomposable laws) from the autoregressive dependence structure.
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.
EWMA chart for ratio of two normals outperforms Shewhart in short runs for small shifts via Markov chain TARL0 calibration and corrected density.
Survey of thinning-based INARMA models for count random fields on regular 2D grids, covering thinning operators, model orders, and unilateral/multilateral structures.
citing papers explorer
-
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.
-
A Class of Higher-Order INAR Random Fields for Poisson Counts and Beyond
CINAR random fields extend INAR models by decoupling the marginal distribution (chosen from discrete self-decomposable laws) from the autoregressive dependence structure.
-
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.
-
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.
-
Monitoring the Ratio of two Normal Variables using EWMA Type Control Charts in Short Production Runs
EWMA chart for ratio of two normals outperforms Shewhart in short runs for small shifts via Markov chain TARL0 calibration and corrected density.
-
INARMA Models for Count Random Fields -- a Survey
Survey of thinning-based INARMA models for count random fields on regular 2D grids, covering thinning operators, model orders, and unilateral/multilateral structures.