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作者：Wang, Zhanfeng; Pan, Rui; Wang, Xueqin; Wang, Yuedong
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of California System; University of California Santa Barbara
摘要：Many methods have been developed to analyze complex data, such as non-Euclidean shape, network, and manifold data. However, there is a lack of methods for studying interactions among complex data. In this article, we first propose a novel kernel function for a metric space and construct its associated reproducing kernel Hilbert space. The new nonstationary kernel function provides a flexible and powerful tool for learning complex structures in non-Euclidean data. We then construct an analysis ...
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作者：Wang, Yibo; Lee, Sunghee; Elliott, Michael R.
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Respondent-driven sampling (RDS) is widely used to collect data from hidden populations in social and biomedical science. Although RDS may provide comprehensive coverage of the target hidden population through social network recruitment, its nonrandom sampling process poses challenges for generalizing findings beyond the sample. Current analytical methods rely on the network size (degree) reported by respondents to adjust for unequal sampling probabilities. However, the accuracy of the reporte...
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作者：Cai, Leheng; Guo, Xu; Lian, Heng; Zhu, Liping
作者单位：Tsinghua University; Beijing Normal University; City University of Hong Kong; Renmin University of China
摘要：High-dimensional penalized rank regression is a powerful tool for modeling high-dimensional data due to its robustness and estimation efficiency. However, the non-smoothness of the rank loss brings great challenges to the computation. To solve this critical issue, high-dimensional convoluted rank regression has been recently proposed, introducing penalized convoluted rank regression estimators. However, these developed estimators cannot be directly used to make inference. In this article, we i...
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作者：Kamath, Gautam; Mouzakis, Argyris; Regehr, Matthew; Singhal, Vikrant; Steinke, Thomas; Ullman, Jonathan
作者单位：University of Waterloo; Alphabet Inc.; DeepMind; Northeastern University
摘要：Differential privacy (DP) is a rigorous notion of data privacy, used for private statistics. The canonical algorithm for differentially private mean estimation is to first clip the samples to a bounded range and then add noise to their empirical mean. Clipping controls the sensitivity and, hence, the variance of the noise that we add for privacy. But clipping also introduces statistical bias. This tradeoff is inherent: we prove that no algorithm can simultaneously have low bias, low error, and...
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作者：Nguyen, Hien D.
作者单位：La Trobe University
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作者：Sun, Dayu; Sun, Zhuowei; Zhao, Xingqiu; Cao, Hongyuan
作者单位：Indiana University System; Indiana University Bloomington; Jilin University; Dalian Medical University; Hong Kong Polytechnic University; State University System of Florida; Florida State University
摘要：We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. We propose combining kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator and contribute to a ...
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作者：Lyu, Zhongyuan; Chen, Ling; Gu, Yuqi
作者单位：Columbia University; Columbia University
摘要：The latent class model is a widely used mixture model for multivariate discrete data. Besides the existence of qualitatively heterogeneous latent classes, real data often exhibit additional quantitative heterogeneity nested within each latent class. The modern latent class analysis also faces extra challenges, including the high-dimensionality, sparsity, and heteroscedastic noise inherent in discrete data. Motivated by these phenomena, we introduce the Degree-heterogeneous Latent Class Model a...
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作者：Gu, Yu; Zeng, Donglin; Lin, D. Y.
作者单位：University of Hong Kong; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertained through periodic examinations and thus interval-censored. The time of reaching the absorbing state is known or right-censored, with the transient state at the previous instant being unobserved. In this article, we provide a general framework for analyzin...
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作者：Ai, Mingyao; Dette, Holger; Liu, Zhengfu; Yu, Jun
作者单位：Peking University; Peking University; Ruhr University Bochum; Beijing Institute of Technology
摘要：An optimal design is usually model-dependent and is sub-optimal if the postulated model is not correctly specified. Furthermore, it is far from ideal even if it is efficient for model selection but has a poor performance for estimating parameters in the selected model. In practice, it is common that a researcher has a list of candidate models at hand and a design has to be found that is efficient for both model discrimination and parameter estimation in the (unknown) true model. In this articl...
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作者：Yan, Shunxing; Yao, Fang; Zhou, Hang
作者单位：Peking University; University of California System; University of California Davis
摘要：Nonparametric mean function regression with repeated measurements serves as a cornerstone for many statistical branches, such as longitudinal/panel/functional data analysis. In this work, we investigate this problem using fully connected deep neural network (DNN) estimators with flexible shapes. A novel theoretical framework allowing arbitrary sampling frequency is established by adopting empirical process techniques to tackle clustered dependence. We then consider the DNN estimators for Holde...
