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含干扰参数的多尺度扫描AI 解读

Abstract We develop a multiscale scanning method to find anomalies in a d-dimensional random field in the presence of nuisance parameters. This covers the common situation that either the baseline-level or additional parameters such as the variance are unknown and have to be estimated from the data. We argue that state of the art approaches to determine asymptotically correct critical values for multiscale scanning statistics will in general fail when such parameters are…

结果数据有限时替代指标对处理效应高效估计的作用AI 解读

Abstract In many experimental and observational studies, the outcome of interest is often difficult or expensive to observe, reducing effective sample sizes for estimating average treatment effects (ATEs) even when identifiable. We study how incorporating data on units for which only surrogate outcomes not of primary interest are observed can increase the precision of ATE estimation. We refrain from imposing stringent surrogacy conditions, which permit surrogates as perfect…

利用回访数据调整无应答的抵抗稳定性模型AI 解读

Abstract Nonresponse arises frequently in surveys, and follow-ups are routinely made to increase the response rate. In order to monitor the follow-up process, callback data have been used in social sciences and survey studies for decades. In modern surveys, the availability of callback data is increasing because the response rate is decreasing, and follow-ups are essential to collect maximum information. Although callback data are helpful to reduce the bias in surveys, such…

持续混杂下通过数据结合开展长期因果推断AI 解读

Abstract We study the identification and estimation of long-term treatment effects by combining short-term experimental data and long-term observational data subject to unobserved confounding. This problem arises often when concerned with long-term treatment effects since experiments are often short-term due to operational necessity while observational data can be more easily collected over longer time frames but may be subject to confounding. In this paper, we tackle the…

有限惩罚估计集成的修正广义交叉验证AI 解读

Abstract Generalized cross-validation (GCV) is a widely used method for estimating the squared out-of-sample prediction risk that employs scalar degrees of freedom adjustment (in a multiplicative sense) to the squared training error. In this paper, we examine the consistency of GCV for estimating the prediction risk of arbitrary ensembles of penalized least-squares estimators. We show that GCV is inconsistent for any finite ensemble of size greater than one. Towards…

超高维协变量下纵向响应的稳健模型平均预测AI 解读

Abstract Model averaging is an attractive ensemble technique to construct fast and accurate prediction. Despite of having been widely practiced in cross-sectional data analysis, its application to longitudinal data is rather limited so far. We consider model averaging for longitudinal response when the number of covariates is ultrahigh. To this end, we propose a novel two-stage procedure in which variable screening is first conducted and then followed by model averaging. In…

利用偏导数的非参数估计AI 解读

Abstract Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically large dataset sizes for reliable conclusions. We develop an approach based on partial derivatives, either observed or estimated, to effectively estimate the function at near-parametric convergence rates. This novel approach and computational algorithm could lead to methods useful to practitioners in many areas of science and engineering.…

基于隐私化成对排序的速率最优排序聚合AI 解读

In various real-world scenarios, such as recommender systems and political surveys, pairwise rankings are commonly collected and used for rank aggregation to derive an overall ranking of items. However, preference rankings can reveal individuals’ personal preferences, highlighting the need to protect them from exposure in downstream analysis. In this article, we address the challenge of preserving privacy while ensuring the utility of rank aggregation based on pairwise…

多计数表特征交互的稀疏贝叶斯组因子模型AI 解读

Group factor models have been developed to infer relationships between multiple co-occurring multivariate continuous responses. Motivated by complex count data from multi-domain microbiome studies using next-generation sequencing, we develop a sparse Bayesian group factor model (Sp-BGFM) for multiple count table data that captures the interaction between microorganisms in different domains. Sp-BGFM uses a rounded kernel mixture model using a Dirichlet process (DP) prior with…

利用电子病历中的回顾性既患信息：重新审视成对伪似然AI 解读

Electronic health records offer abundant data on various diseases and health conditions, enabling researchers to explore the relationship between disease onset age and underlying risk factors.Unlike mortality data, the event of interest is nonterminal, hence, individuals can retrospectively report their disease-onset-age upon recruitment to the study.These individuals, diagnosed with the disease before entering the study, are termed "prevalent." The ascertainment imposes a…

用条件对称多维高斯混合检验基因组研究的大量复合原假设AI 解读

Causal mediation, pleiotropy, and replication analyses are three highly popular genetic study designs. Although these analyses address different scientific questions, the underlying statistical inference problems all involve large-scale testing of composite null hypotheses. The goal is to determine whether all null hypotheses - as opposed to at least one - in a set of individual tests should simultaneously be rejected. Recently, various methods have been proposed for each of…

用于多维政治定位的 ℓ1 贝叶斯理想点模型AI 解读

Ideal point estimation methods in the social sciences lack a principled approach for identifying multidimensional ideal points. We present a novel method for estimating multidimensional ideal points based on ℓ1 distance. In the Bayesian framework, the use of ℓ1 distance transforms the invariance problem of infinite rotational turns into the signed perpendicular problem, yielding posterior estimates that contract around a small area. Our simulation shows that the proposed…

采用最优损失函数的深度回归学习AI 解读

In this article, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, which integrates the information from observed data as well as the information from the data distribution. Consequently, the resulting estimator has desirable optimal…

潜在高斯过程模型中 Vecchia－拉普拉斯近似的迭代方法AI 解读

Latent Gaussian process (GP) models are flexible probabilistic nonparametric function models.Vecchia approximations are accurate approximations for GPs to overcome computational bottlenecks for large data, and the Laplace approximation is a fast method with asymptotic convergence guarantees to approximate marginal likelihoods and posterior predictive distributions for non-Gaussian likelihoods.Unfortunately, the computational complexity of combined Vecchia-Laplace…

利用逐行张量扰动界估计高阶混合社群成员关系AI 解读

Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this article we propose the sub-Gaussian) tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the…

Wasserstein 生成对抗网络潜在空间的自适应学习AI 解读

Generative models based on latent variables, such as generative adversarial networks (GANs) and variational auto-encoders (VAEs), have gained lots of interests due to their impressive performance in many fields. However, many data such as natural images usually do not populate the ambient Euclidean space but instead reside in a lower-dimensional manifold. Thus an inappropriate choice of the latent dimension fails to uncover the structure of the data, possibly resulting in…

用于网络分析的稀疏 β 回归模型AI 解读

For statistical analysis of network data, the β-model has emerged as a useful tool, thanks to its flexibility in incorporating nodewise heterogeneity and theoretical tractability. To generalize the β-model, this article proposes the Sparse β-Regression Model (S β RM) that unites two research themes developed recently in modeling homophily and sparsity. In particular, we employ differential heterogeneity that assigns weights only to important nodes and propose penalized…

整合局部与全局信息的模型无关图神经网络AI 解读

Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to their black-box nature, and an inability to learn representations of varying orders. To tackle these issues, we propose a novel Model-agnostic Graph Neural Network (MaGNet) framework, which is able to effectively integrate information of…

利用支撑点开展大规模低秩高斯过程预测AI 解读

Low-rank approximation is a popular strategy to tackle the “big n problem” associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial and should be carefully specified. Predictive processes simplify the problem by inducing basis functions with a covariance function and a set of knots. The existing literature suggests certain practical implementations of knot selection and covariance estimation; however,…

基于汉明距离的类别数据模型聚类AI 解读

A model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability mass functions to model the data. The elements of this family are then considered as kernels of a finite mixture model with an unknown number of components. Conjugate Bayesian inference has been derived for the parameters of the Hamming distribution model. The mixture is framed in a Bayesian…

Mallows 模型平均的最优性AI 解读

In the past decades, model averaging (MA) has attracted much attention as it has emerged as an alternative tool to the model selection (MS) statistical approach. Hansen introduced a Mallows model averaging (MMA) method with model weights selected by minimizing a Mallows’ Cp criterion. The main theoretical justification for MMA is an asymptotic optimality (AOP), which states that the risk/loss of the resulting MA estimator is asymptotically equivalent to that of the best but…

基于 Spearman 相关矩阵的高维因子数稳健估计AI 解读

Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this article, we introduce a new estimator based on the spectral properties of Spearman sample correlation matrix under the high-dimensional setting, where both dimension and sample size tend to infinity proportionally. Our estimator is robust against heavy tails in either the common factors or idiosyncratic errors. The consistency…

因果发现之后的有效推断AI 解读

Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to "double dipping," invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery…

通过最优模型平均改进张量回归AI 解读

Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty about which rank to use. In this article, we develop a model averaging method to handle this uncertainty by weighting the estimators from candidate tensor regression…