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作者：Shao, Lingxuan; Yao, Fang
作者单位：Fudan University; Peking University
摘要：The exploration of dynamic systems governed by ordinary differential equations (ODEs) holds great interest in the field of statistics. Existing research mainly focuses on a single function. This study generalizes the scope to analyse a collection of functions observed at discretized times, with sampling frequencies varying from sparse to dense designs. The range of ODE models studied caters to diverse dynamic systems, and includes the complex nonlinear and non-Lipschitz scenarios. We introduce...
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作者：Cho, Haeran; Kley, Tobias; Li, Housen
作者单位：University of Bristol; University of Gottingen
摘要：For data segmentation in high-dimensional linear regression settings, the regression parameters are often assumed to be exactly sparse segment-wise, which enables many existing methods to estimate the parameters locally via & ell;1-regularized maximum-likelihood-type estimation and then contrast them for change point detection. Contrary to this common practice, we show that the exact sparsity of neither regression parameters nor their differences, a.k.a. differential parameters, is necessary f...
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作者：Zhang, Yichi; Yang, Shu
作者单位：Indiana University System; Indiana University Bloomington; North Carolina State University
摘要：Principal stratification is essential for revealing causal mechanisms involving post-treatment intermediate variables, in real-world applications like surrogate marker evaluation. Principal stratification analysis with continuous intermediate variables is increasingly common but challenging due to the infinite principal strata and the nonidentifiability and nonregularity of principal causal effects (PCEs). Inspired by recent research, we resolve these challenges by first using a flexible copul...
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作者：Lin, Xiaotong; Li, Weihao; Tian, Fangqiao; Huang, Dongming
作者单位：National University of Singapore; National University of Singapore
摘要：We introduce a general framework for testing goodness-of-fit for Gaussian graphical models in both the low- and high-dimensional settings. This framework is based on a novel algorithm for generating exchangeable copies by conditioning on sufficient statistics. This framework provides exact finite-sample error control regardless of the dimension and allows flexible choices of test statistics to improve power. We explore several candidate test statistics and conduct extensive simulation studies ...
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作者：Dombowsky, Alexander; Dunson, David B.
作者单位：Duke University; Duke University
摘要：While there is an immense literature on Bayesian methods for clustering, the multiview case has received little attention. This problem focuses on obtaining distinct but statistically dependent clusterings in a common set of entities for different data types. For example, clustering patients into subgroups with subgroup membership varying according to the domain of the patient variables. A challenge is how to model the across-view dependence between the partitions of patients into subgroups. T...
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作者：Yan, Yuling; Su, Weijie J.; Fan, Jianqing
作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Pennsylvania; Princeton University
摘要：In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their papers by perceived quality. In this paper, we leverage these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences by extending the isotonic mechanism to exponential family distributions. This mechanism produces adjusted scores closely aligned with the original scores while strictly adhering to the author-specified ran...
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作者：Sood, Anav; Hastie, Trevor
作者单位：Stanford University
摘要：We consider the problem of selecting a small subset of representative variables from a large dataset. In the computer science literature, this dimensionality reduction problem is typically formalized as column subset selection (CSS). Meanwhile, the typical statistical formalization is to find an information-maximizing set of principal variables. This paper shows that these two approaches are equivalent, and moreover, both can be viewed as maximum-likelihood estimation within a certain semi-par...
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作者：Singh, Rahul; Iliopoulos, George; Davidov, Ori
作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Delhi; University of Piraeus; University of Haifa
摘要：Least square estimators for graphical models for cardinal paired comparison data with and without covariates are rigorously analysed. Novel, graph-based, necessary, and sufficient conditions that guarantee strong consistency, asymptotic normality, and the exponential convergence of the estimated ranks are emphasized. A complete theory for models with covariates is laid out. In particular, conditions under which covariates can be safely omitted from the model are provided. The methodology is em...
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作者：Zhang, Chenlin; Zhou, Ling; Guo, Bin; Lin, Huazhen
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作者：Beraha, Mario; Argiento, Raffaele; Camerlenghi, Federico; Guglielmi, Alessandra
作者单位：University of Milano-Bicocca; University of Bergamo; Polytechnic University of Milan
摘要：The study of almost surely discrete random probability measures is an active line of research in Bayesian non-parametrics. The idea of assuming interaction across the atoms of the random probability measure has recently spurred significant interest in the context of Bayesian mixture models. This allows the definition of priors that encourage well-separated and interpretable clusters. In this work, we provide a unified framework for the construction and the Bayesian analysis of random probabili...
