¡@corresponding author* |
General |
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Generalized regression estimators with high-dimensional covariates
Tram Ta, Jun Shao, Quefeng Li and Lei Wang* |
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doi:10.5705/ss.202017.0384
1135 |
¡@ |
Kernel balancing: A flexible non-parametric weighting procedure for estimating causal effects
Chad Hazlett |
Supp |
doi:10.5705/ss.202017.0555
1155 |
¡@ |
Estimation of sparse functional additive models with adaptive group Lasso
Peijun Sang, Liangliang Wang and Jiguo Cao* |
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doi:10.5705/ss.202017.0491
1191 |
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The Lq-norm learning for ultrahigh-dimensional survival data: An integrative framework
H. G. Hong*, X. Chen, J. Kang and Y. Li |
Supp |
doi:10.5705/ss.202017.0537
1213 |
¡@ |
A classical invariance approach to the normal mixture problem
Monia Ranalli*, Bruce G. Lindsay and David R. Hunter |
Supp |
doi:10.5705/ss.202016.0483
1235 |
¡@ |
Regularization parameter selection in indirect regression by residual based bootstrap
Nicolai Bissantz, Justin Chown* and Holger Dette |
Supp |
doi:10.5705/ss.202018.0160
1255 |
¡@ |
Sufficient dimension reduction with simultaneous estimation of effective dimensions for time-to-event data
Ming-Yueh Huang* and Kwun Chuen Gary Chan |
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doi:10.5705/ss.202017.0550
1285 |
¡@ |
Testing first-order spherical symmetry of spatial point processes
Tonglin Zhang* and Jorge Mateu |
Supp |
doi:10.5705/ss.202018.0214
1313 |
¡@ |
A bootstrap Lasso + partial ridge method to construct confidence intervals for parameters in high-dimensional sparse linear models
Hanzhong Liu, Xin Xu and Jingyi Jessica Li* |
Supp |
doi:10.5705/ss.202018.0131
1333 |
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Extremal linear quantile regression with Weibull-type tails
Fengyang He*, Huixia Judy Wang and Tiejun Tong |
Supp |
doi:10.5705/ss.202018.0073
1357 |
¡@ |
Inference for generalized partial functional linear regression
Ting Li and Zhongyi Zhu* |
Supp |
doi:10.5705/ss.202018.0155
1379 |
¡@ |
Optimal Gaussian approximation for multiple time series
Sayar Karmakar* and Wei Biao Wu |
Supp |
doi:10.5705/ss.202017.0303
1399 |
¡@ |
Network imputation for spatial autoregression model with incomplete data
Zhimeng Sun and Hansheng Wang* |
Supp |
doi:10.5705/ss.202017.0366
1419 |
¡@ |
Grouped network vector autoregression
Xuening Zhu and Rui Pan* |
Supp |
doi:10.5705/ss.202017.0533
1437 |
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Error-correction factor models for high-dimensional cointegrated time series
Yundong Tu*, Qiwei Yao and Rongmao Zhang |
Supp |
doi:10.5705/ss.202017.0250
1463 |
¡@ |
Ranking-based variable selection for high-dimensional data
Rafal Baranowski, Yining Chen and Piotr Fryzlewicz* |
Supp |
doi:10.5705/ss.202017.0139
1485 |
¡@ |
Identification and inference for marginal average treatment effect on the treated with an instrumental variable
Lan Liu*, Wang Miao, Baoluo Sun, James Robins and Eric Tchetgen Tchetgen |
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doi:10.5705/ss.202017.0196
1517 |
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Order-of-addition modeling
Robert W. Mee |
Supp |
doi:10.5705/ss.202018.0120
1543 |
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Evolutionary state-space model and its application to time-frequency analysis of local field potentials
Xu Gao, Weining Shen*, Babak Shahbaba, Norbert J. Fortin and Hernando Ombao |
Supp |
doi:10.5705/ss.202017.0420
1561 |
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New parsimonious multivariate spatial model: Spatial envelope
Hossein Moradi Rekabdarkolaee*, Qin Wang, Zahra Naji and Montserrat Fuente |
Supp |
doi:10.5705/ss.202017.0455
1583 |
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Time-varying hazards model for incorporating irregularly measured high-dimensional biomarkers
Xiang Li, Quefeng Li*, Donglin Zeng, Karen Marder, Jane Paulsen and Yuanjia Wang |
Supp |
doi:10.5705/ss.202017.0375
1605 |
¡@ |
Testing constancy of conditional variance in high dimension
Lu Deng, Changliang Zou, Zhaojun Wang* and Xin Chen |
Supp |
doi:10.5705/ss.202016.0492
1633 |
¡@ |
A fully flexible changepoint test for regression models with stationary errors
Michael W. Robbins |
Supp |
doi:10.5705/ss.202018.0275
1657 |