Works matching IS 00359254 AND DT 2024 AND VI 73 AND IP 3
Results: 14
Estimating the size of undeclared work from partially misclassified survey data via the Expectation–Maximization algorithm.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 816, doi. 10.1093/jrsssc/qlae013
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Nonlinear network-based quantitative trait prediction from biological data.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 796, doi. 10.1093/jrsssc/qlae012
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A two-sample tree-based test for hierarchically organized genomic signals.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 774, doi. 10.1093/jrsssc/qlae011
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Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 755, doi. 10.1093/jrsssc/qlae010
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A doubly self-exciting Poisson model for describing scoring levels in NBA basketball.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 735, doi. 10.1093/jrsssc/qlae009
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Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 715, doi. 10.1093/jrsssc/qlae008
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Macroeconomic and financial mixed frequency factors in a big data environment.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 682, doi. 10.1093/jrsssc/qlae007
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Unsupervised Bayesian classification for models with scalar and functional covariates.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 658, doi. 10.1093/jrsssc/qlae006
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Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 639, doi. 10.1093/jrsssc/qlae005
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Identifying Brexit voting patterns in the British house of commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 621, doi. 10.1093/jrsssc/qlae004
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Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 598, doi. 10.1093/jrsssc/qlae003
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Bayesian smoothing for time-varying extremal dependence.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 581, doi. 10.1093/jrsssc/qlae002
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Prediction for distributional outcomes in high-performance computing input/output variability.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 561, doi. 10.1093/jrsssc/qlae001
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On the combination of data smoothing and Markov-switching models.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2024, v. 73, n. 3, p. 557, doi. 10.1093/jrsssc/qlad110
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- Article