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h5 指数:36
h5 中位数:42

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2017-2021 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

#19 Probability & Statistics with Applications

标题/作者	引用次数	年份
Quantifying publication bias in meta‐analysis L Lin, H Chu Biometrics 74 (3), 785-794	394	2018
On the reliability of N‐mixture models for count data RJ Barker, MR Schofield, WA Link, JR Sauer Biometrics 74 (1), 369-377	178	2018
Outcome‐adaptive lasso: Variable selection for causal inference SM Shortreed, A Ertefaie Biometrics 73 (4), 1111-1122	135	2017
Convex biclustering EC Chi, GI Allen, RG Baraniuk Biometrics 73 (1), 10-19	115	2017
Statistical significance for hierarchical clustering PK Kimes, Y Liu, D Neil Hayes, JS Marron Biometrics 73 (3), 811-821	112	2017
A penalized framework for distributed lag non‐linear models A Gasparrini, F Scheipl, B Armstrong, MG Kenward Biometrics 73 (3), 938-948	110	2017
Generalizing causal inferences from individuals in randomized trials to all trial‐eligible individuals IJ Dahabreh, SE Robertson, EJ Tchetgen, EA Stuart, MA Hernán Biometrics 75 (2), 685-694	86	2019
A general statistical framework for subgroup identification and comparative treatment scoring S Chen, L Tian, T Cai, M Yu Biometrics 73 (4), 1199-1209	84	2017
Sample size determination for GEE analyses of stepped wedge cluster randomized trials F Li, EL Turner, JS Preisser Biometrics 74 (4), 1450-1458	64	2018
Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations L Tian, H Fu, SJ Ruberg, H Uno, LJ Wei Biometrics 74 (2), 694-702	58	2018
Operating characteristics of the rank‐based inverse normal transformation for quantitative trait analysis in genome‐wide association studies ZR McCaw, JM Lane, R Saxena, S Redline, X Lin Biometrics 76 (4), 1262-1272	56	2020
Covariate‐adjusted Spearman's rank correlation with probability‐scale residuals Q Liu, C Li, V Wanga, BE Shepherd Biometrics 74 (2), 595-605	56	2018
Pearson's chi‐square test and rank correlation inferences for clustered data JH Shih, MP Fay Biometrics 73 (3), 822-834	53	2017
Alternative measures of between‐study heterogeneity in meta‐analysis: Reducing the impact of outlying studies L Lin, H Chu, JS Hodges Biometrics 73 (1), 156-166	48	2017
Instrumental variables estimation of exposure effects on a time‐to‐event endpoint using structural cumulative survival models T Martinussen, S Vansteelandt, EJ Tchetgen Tchetgen, DM Zucker Biometrics 73 (4), 1140-1149	46	2017
On longitudinal prediction with time‐to‐event outcome: Comparison of modeling options M Maziarz, P Heagerty, T Cai, Y Zheng Biometrics 73 (1), 83-93	46	2017
Covariate selection with group lasso and doubly robust estimation of causal effects B Koch, DM Vock, J Wolfson Biometrics 74 (1), 8-17	44	2018
Greedy outcome weighted tree learning of optimal personalized treatment rules R Zhu, YQ Zhao, G Chen, S Ma, H Zhao Biometrics 73 (2), 391-400	43	2017
A generalized Levene's scale test for variance heterogeneity in the presence of sample correlation and group uncertainty D Soave, L Sun Biometrics 73 (3), 960-971	42	2017
Adaptive contrast weighted learning for multi‐stage multi‐treatment decision‐making Y Tao, L Wang Biometrics 73 (1), 145-155	42	2017

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