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总体间接因果效应的稳健推断：广义前门准则AI 解读

Summary Standard methods for inference about direct and indirect effects require stringent no-unmeasured-confounding assumptions which often fail to hold in practice, particularly in observational studies. The goal of the paper is to introduce a new form of indirect effect, the population intervention indirect effect, that can be non-parametrically identified in the presence of an unmeasured common cause of exposure and outcome. This new type of indirect effect captures the…

控制混杂因素的条件独立性置换检验AI 解读

Summary We propose a general new method, the conditional permutation test, for testing the conditional independence of variables X and Y given a potentially high dimensional random vector Z that may contain confounding factors. The test permutes entries of X non-uniformly, to respect the existing dependence between X and Z and thus to account for the presence of these confounders. Like the conditional randomization test of Candès and co-workers in 2018, our test relies on…

复杂调查数据的贝叶斯经验似然推断AI 解读

Summary We propose a Bayesian empirical likelihood approach to survey data analysis on a vector of finite population parameters defined through estimating equations. Our method allows overidentified estimating equation systems and is applicable to both smooth and non-differentiable estimating functions. Our proposed Bayesian estimator is design consistent for general sampling designs and the Bayesian credible intervals are calibrated in the sense of having asymptotically…

张量图形Lasso：TeraLassoAI 解读

Summary The paper introduces a multiway tensor generalization of the bigraphical lasso which uses a two-way sparse Kronecker sum multivariate normal model for the precision matrix to model parsimoniously conditional dependence relationships of matrix variate data based on the Cartesian product of graphs. We call this tensor graphical lasso generalization TeraLasso. We demonstrate by using theory and examples that the TeraLasso model can be accurately and scalably estimated…

融合密度估计：理论与方法AI 解读

Summary We introduce a method for non-parametric density estimation on geometric networks. We define fused density estimators as solutions to a total variation regularized maximum likelihood density estimation problem. We provide theoretical support for fused density estimation by proving that the squared Hellinger rate of convergence for the estimator achieves the minimax bound over univariate densities of log-bounded variation. We reduce the original variational…

利用极值方法估计随访不足下的非参数治愈率AI 解读

Summary An important research topic in survival analysis is related to the modelling and estimation of the cure rate, i.e. the proportion of subjects who will never experience the event of interest. However, most estimation methods proposed so far in the literature do not handle the case of insufficient follow-up, i.e. when the right end point of the support of the censoring time is strictly less than that of the survival time of the susceptible subjects, and consequently…

具有处理顺序约束的实验设计AI 解读

Summary There are many situations where one expects an ordering among K ⩾ 2 experimental groups or treatments. Although there is a large body of literature dealing with the analysis under order restrictions, surprisingly, very little work has been done in the context of the design of experiments. Here, a principled approach to the design of experiments with ordered treatments is provided. In particular we propose two classes of designs which are optimal for testing different…

通过非局部先验选择混合模型的成分数AI 解读

Summary Choosing the number of mixture components remains an elusive challenge. Model selection criteria can be either overly liberal or conservative and return poorly separated components of limited practical use. We formalize non-local priors (NLPs) for mixtures and show how they lead to well-separated components with non-negligible weight, interpretable as distinct subpopulations. We also propose an estimator for posterior model probabilities under local priors and NLPs,…

高维线性判别分析：最优性、自适应算法与缺失数据AI 解读

Summary The paper develops optimality theory for linear discriminant analysis in the high dimensional setting. A data-driven and tuning-free classification rule, which is based on an adaptive constrained l1-minimization approach, is proposed and analysed. Minimax lower bounds are obtained and this classification rule is shown to be simultaneously rate optimal over a collection of parameter spaces. In addition, we consider classification with incomplete data under the…

通过百分位自助法对逆概率加权估计进行敏感性分析AI 解读

Summary To identify the estimand in missing data problems and observational studies, it is common to base the statistical estimation on the ‘missingness at random’ and ‘no unmeasured confounder’ assumptions. However, these assumptions are unverifiable by using empirical data and pose serious threats to the validity of the qualitative conclusions of statistical inference. A sensitivity analysis asks how the conclusions may change if the unverifiable assumptions are violated…

MALMEM：线性测量误差模型中的模型平均AI 解读

Summary We develop model averaging estimation in the linear regression model where some covariates are subject to measurement error. The absence of the true covariates in this framework makes the calculation of the standard residual-based loss function impossible. We take advantage of the explicit form of the parameter estimators and construct a weight choice criterion. It is asymptotically equivalent to the unknown model average estimator minimizing the loss function. When…

动态收缩过程AI 解读

Summary We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global–local priors, such…

多组大规模多重检验整合分析中的信号分类AI 解读

Summary The integrative analysis of multiple data sets is becoming increasingly important in many fields of research. When the same features are studied in several independent experiments, it can often be useful to analyse jointly the multiple sequences of multiple tests that result. It is frequently necessary to classify each feature into one of several categories, depending on the null and non-null configuration of its corresponding test statistics. The paper studies this…

多重变点及类似变点特征的最窄超阈值检测AI 解读

Summary We propose a new, generic and flexible methodology for non-parametric function estimation, in which we first estimate the number and locations of any features that may be present in the function and then estimate the function parametrically between each pair of neighbouring detected features. Examples of features handled by our methodology include change points in the piecewise constant signal model, kinks in the piecewise linear signal model and other similar…

分位数回归模型的失拟检验AI 解读

Summary The paper novelly transforms lack-of-fit tests for parametric quantile regression models into checking the equality of two conditional distributions of covariates. Accordingly, by applying some successful two-sample test statistics in the literature, two tests are constructed to check the lack of fit for low and high dimensional quantile regression models. The low dimensional test works well when the number of covariates is moderate, whereas the high dimensional test…

复杂约束域上的内蕴高斯过程AI 解读

Summary We propose a class of intrinsic Gaussian processes (GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irregularly shaped spaces arising as subsets or submanifolds of R, R2, R3 and beyond. For example, intrinsic GPs can accommodate spatial domains arising as complex subsets of Euclidean space. Intrinsic GPs respect the potentially complex boundary or interior conditions as well as the intrinsic…

分布无关的图型两样本检验：统一渐近框架AI 解读

Summary Testing equality of two multivariate distributions is a classical problem for which many non-parametric tests have been proposed over the years. Most of the popular two-sample tests, which are asymptotically distribution free, are based either on geometric graphs constructed by using interpoint distances between the observations (multivariate generalizations of the Wald–Wolfowitz runs test) or on multivariate data depth (generalizations of the Mann–Whitney rank…

聚合关联检验后的选择后估计与检验AI 解读

Summary The practice of pooling several individual test statistics to form aggregate tests is common in many statistical applications where individual tests may be underpowered. Although selection by aggregate tests can serve to increase power, the selection process invalidates inference based on the individual test statistics, making it difficult to identify those that drive the signal in follow-up inference. Here, we develop a general approach for valid inference following…

可扩展的重要性调温与贝叶斯变量选择AI 解读

Summary We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high dimensionality, explicit comparison with standard Markov chain Monte Carlo methods and illustrations of the potential improvements in efficiency. Simple and concrete intuition is provided for when the novel scheme is expected to…

存在部分隐藏变量时的稳健因果结构学习AI 解读

Summary We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG) in the presence of hidden variables, in settings where the underlying DAG among the observed variables is sparse, and there are a few hidden variables that have a direct effect on many of the observed variables. Building on the so-called low rank plus sparse framework, we suggest a two-stage approach which first removes the effect of the hidden variables and then…

大规模两样本推断中的协变量辅助排序与筛选AI 解读

Summary Two-sample multiple testing has a wide range of applications. The conventional practice first reduces the original observations to a vector of p-values and then chooses a cut-off to adjust for multiplicity. However, this data reduction step could cause significant loss of information and thus lead to suboptimal testing procedures. We introduce a new framework for two-sample multiple testing by incorporating a carefully constructed auxiliary variable in inference to…

通过矩调整随机梯度对总体目标景观进行统计推断AI 解读

Summary Modern statistical inference tasks often require iterative optimization methods to compute the solution. Convergence analysis from an optimization viewpoint informs us only how well the solution is approximated numerically but overlooks the sampling nature of the data. In contrast, recognizing the randomness in the data, statisticians are keen to provide uncertainty quantification, or confidence, for the solution obtained by using iterative optimization methods. The…

基于 Wasserstein 距离的近似贝叶斯计算AI 解读

Summary A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation has become a popular approach to overcome this issue, in which one simulates synthetic data sets given parameters and compares summaries of these data sets with the corresponding observed values. We propose to avoid the use of summaries and the ensuing loss of information by instead using the Wasserstein distance…

利用投影对函数型数据进行分组聚类AI 解读

Summary We show that, in the functional data context, by appropriately exploiting the functional nature of the data, it is possible to cluster the observations asymptotically perfectly. We demonstrate that this level of performance can sometimes be achieved by the k-means algorithm as long as the data are projected on a carefully chosen finite dimensional space. In general, the notion of an ideal cluster is not clearly defined. We derive our results in the setting where the…