Works matching IS 00359254 AND DT 2024 AND VI 73 AND IP 1
Results: 13
Semiparametric M-quantile regression with measurement error in spatial covariates: an application to housing price modelling.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 82, doi. 10.1093/jrsssc/qlad086
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High-resolution global precipitation downscaling with latent Gaussian models and non-stationary stochastic partial differential equation structure.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 65, doi. 10.1093/jrsssc/qlad084
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Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 47, doi. 10.1093/jrsssc/qlad083
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Models and methods for analysing clustered recurrent hospitalisations in the presence of COVID-19 effects.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 28, doi. 10.1093/jrsssc/qlad082
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Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 257, doi. 10.1093/jrsssc/qlad094
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Penalised semi-parametric copula method for semi-competing risks data: application to hip fracture in elderly.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 241, doi. 10.1093/jrsssc/qlad093
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Causal inference with a functional outcome.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 221, doi. 10.1093/jrsssc/qlad092
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Horseshoe prior Bayesian quantile regression.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 193, doi. 10.1093/jrsssc/qlad091
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Statistical inference for complete and incomplete mobility trajectories under the flight-pause model.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 162, doi. 10.1093/jrsssc/qlad090
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Categorising the world into local climate zones: towards quantifying labelling uncertainty for machine learning models.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 143, doi. 10.1093/jrsssc/qlad089
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Modelling change processes in multivariate interrupted time series data using a multivariate dynamic additive model: An application to heart rate and blood pressure self-monitoring in heart failure with drug changes.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 123, doi. 10.1093/jrsssc/qlad088
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Multivariate longitudinal analysis for the association between brain atrophy and cognitive impairment in prodromal Huntington's disease subjects.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 104, doi. 10.1093/jrsssc/qlad087
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CAViaR models for Value-at-Risk and Expected Shortfall with long range dependency features.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 1, p. 1, doi. 10.1093/jrsssc/qlad081
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- Article