Works matching IS 00359254 AND DT 2020 AND VI 69 AND IP 4
Results: 14
Fault isolation for a complex decentralized waste water treatment facility.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 931, doi. 10.1111/rssc.12429
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Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 815, doi. 10.1111/rssc.12428
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Global forensic geolocation with deep neural networks.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 909, doi. 10.1111/rssc.12427
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Modelling fuel injector spray characteristics in jet engines by using vine copulas.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 863, doi. 10.1111/rssc.12421
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Inference for extreme values under threshold‐based stopping rules.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 765, doi. 10.1111/rssc.12420
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Estimating the binary endogenous effect of insurance on doctor visits by copula‐based regression additive models.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 953, doi. 10.1111/rssc.12419
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The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 991, doi. 10.1111/rssc.12418
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A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 791, doi. 10.1111/rssc.12417
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Statistical inference on tree swallow migrations with random forests.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 973, doi. 10.1111/rssc.12416
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Causal mechanism of extreme river discharges in the upper Danube basin network.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 741, doi. 10.1111/rssc.12415
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Small sample corrections for Wald tests in latent variable models.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 841, doi. 10.1111/rssc.12414
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Longitudinal networks of dyadic relationships using latent trajectories: evidence from the European interbank market.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 711, doi. 10.1111/rssc.12413
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Simulating gene silencing through intervention analysis.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 887, doi. 10.1111/rssc.12412
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- Article
Issue Information.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2020, v. 69, n. 4, p. 709, doi. 10.1111/rssc.12361
- Publication type:
- Article