Deep Neural Networks Guided Ensemble Learning for Point Estimation in Finite Samples.
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Haoda Fu | Tianyu Zhan | Jian Kang | H. Fu | Jian Kang | Tianyu Zhan
[1] Dapeng Wu,et al. Learning Topology and Dynamics of Large Recurrent Neural Networks , 2014, IEEE Transactions on Signal Processing.
[2] C. Vogel. Computational Methods for Inverse Problems , 1987 .
[3] Changbao Wu,et al. Asymptotic Theory of Nonlinear Least Squares Estimation , 1981 .
[4] Halbert White,et al. Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.
[5] L Shen,et al. An improved method of evaluating drug effect in a multiple dose clinical trial , 2001, Statistics in medicine.
[6] Christopher K. Wikle,et al. An ensemble quadratic echo state network for non‐linear spatio‐temporal forecasting , 2017, 1708.05094.
[7] Peter L. Bartlett,et al. Neural Network Learning - Theoretical Foundations , 1999 .
[8] Yujia Deng,et al. Deep learning from a statistical perspective , 2020, Stat.
[9] A remark on estimating the mean of a normal distribution with known coefficient of variation , 2015 .
[10] Qingyi Gao,et al. Theoretical Investigation of Generalization Bounds for Adversarial Learning of Deep Neural Networks , 2021, Journal of Statistical Theory and Practice.
[11] D. Berry. Bayesian clinical trials , 2006, Nature Reviews Drug Discovery.
[12] Gilead Announces Results From Phase 3 Trial of Investigational Antiviral Remdesivir in Patients With Severe COVID-19 , 2020 .
[13] Sarah Eichmann. Introductory Econometrics With Applications , 2016 .
[14] E. L. Lehmann,et al. Theory of point estimation , 1950 .
[15] Hao Chen,et al. Theoretical Investigation of Generalization Bound for Residual Networks , 2019, IJCAI.
[16] Xiao Wang,et al. Nonlinear Variable Selection via Deep Neural Networks , 2020, J. Comput. Graph. Stat..
[17] Jelena Bradic. Randomized maximum-contrast selection: subagging for large-scale regression , 2013, 1306.3494.
[18] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[19] Joseph G Ibrahim,et al. Bayesian design of superiority clinical trials for recurrent events data with applications to bleeding and transfusion events in myelodyplastic syndrome. , 2014, Biometrics.
[20] Jinchi Lv,et al. Statistical insights into deep neural network learning in subspace classification , 2020, Stat.
[21] Jelena Bradic,et al. DeepHazard: neural network for time-varying risks , 2020, ArXiv.
[22] Han Liu,et al. Challenges of Big Data Analysis. , 2013, National science review.
[23] Frank Bretz,et al. Adaptive designs for confirmatory clinical trials , 2009, Statistics in medicine.
[24] John McDonald,et al. Using least squares and tobit in second stage DEA efficiency analyses , 2009, Eur. J. Oper. Res..
[25] Parul Parashar,et al. Neural Networks in Machine Learning , 2014 .
[26] Wenbin Lu,et al. Deep advantage learning for optimal dynamic treatment regime , 2018, Statistical theory and related fields.
[27] Francis R. Bach,et al. Breaking the Curse of Dimensionality with Convex Neural Networks , 2014, J. Mach. Learn. Res..
[28] R. Jennrich. Asymptotic Properties of Non-Linear Least Squares Estimators , 1969 .
[29] William Stafford Noble,et al. DeepPINK: reproducible feature selection in deep neural networks , 2018, NeurIPS.
[30] Xiao Wang,et al. Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units , 2018, NeurIPS.
[31] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[32] John Whitehead,et al. Estimation following selection of the largest of two normal means. Journal of Statistical Planning and Inference 138, 1629-1638. , 2008 .
[33] Jie Shen,et al. Efficient Spectral Sparse Grid Methods and Applications to High-Dimensional Elliptic Problems , 2010, SIAM J. Sci. Comput..
[34] Dmitry Yarotsky,et al. Error bounds for approximations with deep ReLU networks , 2016, Neural Networks.
[35] Peter Wanke. The uniform distribution as a first practical approach to new product inventory management , 2008 .
[36] T. Amemiya. Regression Analysis When the Variance of the Dependent Variable Is Proportional to the Square of Its Expectation , 1973 .
[37] Isaac Meilijson,et al. An Example of an Improvable Rao–Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator , 2016, The American statistician.
[38] Han Liu,et al. Heterogeneity adjustment with applications to graphical model inference. , 2016, Electronic journal of statistics.
[39] Yang Feng,et al. RaSE: Random Subspace Ensemble Classification , 2020, J. Mach. Learn. Res..
[40] Guang Cheng,et al. Directional Pruning of Deep Neural Networks , 2020, NeurIPS.
[41] Guannan Zhang,et al. A Hyperspherical Adaptive Sparse-Grid Method for High-Dimensional Discontinuity Detection , 2015, SIAM J. Numer. Anal..
[42] D. Spiegelhalter,et al. Summarizing historical information on controls in clinical trials , 2010, Clinical trials.
[43] Guang Cheng,et al. Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee , 2020, NeurIPS.
[44] Dapeng Oliver Wu,et al. Why Deep Learning Works: A Manifold Disentanglement Perspective , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[45] G. M.,et al. Partial Differential Equations I , 2023, Applied Mathematical Sciences.
[46] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[47] Erwan Scornet,et al. Neural Random Forests , 2016, Sankhya A.
[48] Zhuoran Yang,et al. Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval , 2018, ArXiv.
[49] Jonathan R. Stroud,et al. Understanding the Ensemble Kalman Filter , 2016 .
[50] Haoda Fu,et al. Boosting Algorithms for Estimating Optimal Individualized Treatment Rules , 2020, ArXiv.
[51] Gérard Biau,et al. Analysis of a Random Forests Model , 2010, J. Mach. Learn. Res..
[52] Michael H. Anderson,et al. Rent-to-own agreements: Customer characteristics and contract outcomes , 2009 .