Works matching IS 00359254 AND DT 2019 AND VI 68 AND IP 3
Results: 17
Ranking the importance of genetic factors by variable‐selection confidence sets.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 727, doi. 10.1111/rssc.12337
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A new competing risks decomposition: application to the effect of cutting unemployment benefit on unemployment durations.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 793, doi. 10.1111/rssc.12335
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Landmark linear transformation model for dynamic prediction with application to a longitudinal cohort study of chronic disease.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 771, doi. 10.1111/rssc.12334
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Topic modelling for medical prescription fraud and abuse detection.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 751, doi. 10.1111/rssc.12332
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- Article
Bayesian non‐parametric survival regression for optimizing precision dosing of intravenous busulfan in allogeneic stem cell transplantation.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 809, doi. 10.1111/rssc.12331
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RNASeqDesign: a framework for ribonucleic acid sequencing genomewide power calculation and study design issues.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 683, doi. 10.1111/rssc.12330
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PairClone: a Bayesian subclone caller based on mutation pairs.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 705, doi. 10.1111/rssc.12328
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Calibrating non‐probability surveys to estimated control totals using LASSO, with an application to political polling.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 657, doi. 10.1111/rssc.12327
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- Article
Detecting weak dependence in computer network traffic patterns by using higher criticism.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 641, doi. 10.1111/rssc.12325
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Exploring patterns of demand in bike sharing systems via replicated point process models.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 585, doi. 10.1111/rssc.12322
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Careful prior specification avoids incautious inference for log‐Gaussian Cox point processes.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 543, doi. 10.1111/rssc.12321
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Bayesian analysis of functional magnetic resonance imaging data with spatially varying auto‐regressive orders.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 521, doi. 10.1111/rssc.12320
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Optimal design of experiments for non‐linear response surface models.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 623, doi. 10.1111/rssc.12313
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Rectangular latent Markov models for time‐specific clustering, with an analysis of the wellbeing of nations.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 603, doi. 10.1111/rssc.12312
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Discrete Weibull generalized additive model: an application to count fertility data.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 565, doi. 10.1111/rssc.12311
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- Article
Issue Information.
- Published in:
- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 493, doi. 10.1111/rssc.12302
- Publication type:
- Article
Functional clustering of accelerometer data via transformed input variables.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2019, v. 68, n. 3, p. 495, doi. 10.1111/rssc.12310
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- Article