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可信性函数与精确频率学派推断AI 解读

In the frequentist program, inferential methods with exact control on error rates are a primary focus. The standard approach, however, is to rely on asymptotic approximations, which may not be suitable. This article presents a general framework for the construction of exact frequentist procedures based on plausibility functions. It is shown that the plausibility function-based tests and confidence regions have the desired frequentist properties in finite samples—no…

基于倒数 L1 正则化的高维变量选择AI 解读

During the past decade, penalized likelihood methods have been widely used in variable selection problems, where the penalty functions are typically symmetric about 0, continuous and nondecreasing in (0, ∞). We propose a new penalized likelihood method, reciprocal Lasso (or in short, rLasso), based on a new class of penalty functions that are decreasing in (0, ∞), discontinuous at 0, and converge to infinity when the coefficients approach zero. The new penalty functions give…

基于聚类的多元数据异常值检测方案AI 解读

Detection power of the squared Mahalanobis distance statistic is significantly reduced when several outliers exist within a multivariate dataset of interest. To overcome this masking effect, we propose a computer-intensive cluster-based approach that incorporates a reweighted version of Rousseeuw’s minimum covariance determinant method with a multi-step cluster-based algorithm that initially filters out potential masking points. Compared to the most robust procedures,…

边际推断模型：针对目标参数的无先验概率推断AI 解读

The inferential models (IM) framework provides prior-free, frequency-calibrated, and posterior probabilistic inference. The key is the use of random sets to predict unobservable auxiliary variables connected to the observable data and unknown parameters. When nuisance parameters are present, a marginalization step can reduce the dimension of the auxiliary variable which, in turn, leads to more efficient inference. For regular problems, exact marginalization can be achieved,…

大型稀疏张量的贝叶斯分解AI 解读

It has become routine to collect data that are structured as multiway arrays (tensors). There is an enormous literature on low rank and sparse matrix factorizations, but limited consideration of extensions to the tensor case in statistics. The most common low rank tensor factorization relies on parallel factor analysis (PARAFAC), which expresses a rank k tensor as a sum of rank one tensors. When observations are only available for a tiny subset of the cells of a big tensor,…

连续处理效应的一致半参数有效估计AI 解读

This article studies identification, estimation, and inference of general unconditional treatment effects models with continuous treatment under the ignorability assumption. We show identification of the parameters of interest, the dose–response functions, under the assumption that selection to treatment is based on observables. We propose a semiparametric two-step estimator, and consider estimation of the dose–response functions through moment restriction models with…

超几何分布参数的精确最优置信区间AI 解读

For a hypergeometric distribution, denoted by Hyper(M,N,n), where N is the population size, M is the number of population units with some attribute, and n is the given sample size, there are two parametric cases: (i) N is unknown and M is given; (ii) M is unknown and N is given. For each case, we first show that the minimum coverage probability of commonly used approximate intervals is much smaller than the nominal level for any n, then we provide exact smallest lower and…

贝叶斯压缩回归AI 解读

As an alternative to variable selection or shrinkage in high-dimensional regression, we propose to randomly compress the predictors prior to analysis. This dramatically reduces storage and computational bottlenecks, performing well when the predictors can be projected to a low-dimensional linear subspace with minimal loss of information about the response. As opposed to existing Bayesian dimensionality reduction approaches, the exact posterior distribution conditional on the…

通过包络方法进行分组降维AI 解读

The family of sufficient dimension reduction (SDR) methods that produce informative combinations of predictors, or indices, are particularly useful for high dimensional regression analysis. In many such analyses, it becomes increasingly common that there is available a priori subject knowledge of the predictors; e.g., they belong to different groups. While many recent SDR proposals have greatly expanded the scope of the methods' applicability, how to effectively incorporate…

混合不同收敛速度估计量的简明公式AI 解读

Suppose that two estimators, θ^S,n and θ^N,n, are available for estimating an unknown parameter θ, and are known to have convergence rates n1/2 and rn = o(n1/2), respectively, based on a sample of size n. Typically, the more efficient estimator θ^S,n is less robust than θ^N,n, and a definitive choice cannot be easily made between them under practical circumstances. We propose a simple mixture estimator, in the form of a linear combination of θ^S,n and θ^N,n, which…

用于最优收缩的 Dirichlet–Laplace 先验AI 解读

regularization, are routinely used in high-dimensional applications, and there is a rich literature on optimality properties under sparsity assumptions. In the Bayesian paradigm, sparsity is routinely induced through two-component mixture priors having a probability mass at zero, but such priors encounter daunting computational problems in high dimensions. This has motivated continuous shrinkage priors, which can be expressed as global-local scale mixtures of Gaussians,…

IsoDOT：针对分类或连续协变量的 RNA 异构体差异表达与使用检验AI 解读

We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refers to relative isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an…

Studies of expression quantitative trait loci (eQTLs) offer insight into the molecular mechanisms of loci that were found to be associated with complex diseases and the mechanisms can be classified into cis- and trans-acting regulation. At present, high-throughput RNA sequencing (RNA-seq) is rapidly replacing expression microarrays to assess gene expression abundance. Unlike microarrays that only measure the total expression of each gene, RNA-seq also provides information on…

高维地标式二维形状数据的聚类AI 解读

An important goal in image analysis is to cluster and recognize objects of interest according to the shapes of their boundaries. Clustering such objects faces at least four major challenges including a curved shape space, a high-dimensional feature space, a complex spatial correlation structure, and shape variation associated with some covariates (e.g., age or gender). The aim of this article is to develop a penalized model-based clustering framework to cluster…

同时纳入随机序与变异序的贝叶斯非参数 ROC 曲线估计AI 解读

Beom Seuk Hwang & Zhen ChenBeom Seuk Hwang is Postdoctoral Fellow (E-mail: ) and Zhen Chen is Investigator (E-mail: ), Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892. This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health. This…

用于不完整结果数据估计的协变量平衡稳定权重AI 解读

Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of such methods is that one set of weights can be used to estimate a range of treatment effects based on different outcomes, or a variety of population means for several variables. However, this appeal can be diminished in practice by the instability of the estimated weights and by…

高维高斯图模型中用于网络构建的等价偏相关度量AI 解读

Gaussian graphical models (GGMs) are frequently used to explore networks, such as gene regulatory networks, among a set of variables. Under the classical theory of GGMs, the construction of Gaussian graphical networks amounts to finding the pairs of variables with nonzero partial correlation coefficients. However, this is infeasible for high-dimensional problems for which the number of variables is larger than the sample size. In this article, we propose a new measure of…

局部化函数主成分分析AI 解读

We propose localized functional principal component analysis (LFPCA), looking for orthogonal basis functions with localized support regions that explain most of the variability of a random process. The LFPCA is formulated as a convex optimization problem through a novel Deflated Fantope Localization method and is implemented through an efficient algorithm to obtain the global optimum. We prove that the proposed LFPCA converges to the original FPCA when the tuning parameters…

基于概率指数模型的秩检验回归框架AI 解读

We demonstrate how many classical rank tests, such as the Wilcoxon–Mann–Whitney, Kruskal–Wallis, and Friedman test, can be embedded in a statistical modeling framework and how the method can be used to construct new rank tests. In addition to hypothesis testing, the method allows for estimating effect sizes with an informative interpretation, resulting in a better understanding of the data. Supplementary materials for this article are available online.

含潜变量的加性风险模型回归分析AI 解读

We propose an additive hazards model with latent variables to investigate the observed and latent risk factors of the failure time of interest. Each latent risk factor is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a hybrid procedure that combines the expectation–maximization (EM) algorithm and the borrow-strength estimation approach to estimate the model parameters. We establish the consistency and asymptotic…

Network analysis is often focused on characterizing the dependencies between network relations and node-level attributes. Potential relationships are typically explored by modeling the network as a function of the nodal attributes or by modeling the attributes as a function of the network. These methods require specification of the exact nature of the association between the network and attributes, reduce the network data to a small number of summary statistics, and are…

随研究累积追踪交叉验证预测误差估计AI 解读

In recent years, “reproducibility” has emerged as a key factor in evaluating x applications of statistics to the biomedical sciences, for example, learning predictors of disease phenotypes from high-throughput “omics” data. In particular, “validation” is undermined when error rates on newly acquired data are sharply higher than those originally reported. More precisely, when data are collected from m “studies” representing possibly different subphenotypes, more generally…

利用鞍点近似进行 INAR(p) 模型的似然估计AI 解读

Xanthi Pedeli, Anthony C. Davison & Konstantinos FokianosXanthi Pedeli, Department of Statistics, Athens University of Economics & Business, 10434 Athens, Greece (E-mail: ). Anthony C. Davison, École Polytechnique Fédérale de Lausanne, EPFL-FSB-MATHAA-STAT, Station 8, 1015 Lausanne, Switzerland (E-mail: ). Konstantinos Fokianos, Department of Mathematics & Statistics, University of Cyprus, 1678 Nicosia, Cyprus (E-mail: ). X. Pedeli and K. Fokianos are supported by the…

混频非平稳存量与流量时间序列的估计、预测及信号提取AI 解读

An important practical problem for statistical agencies and central banks that publish economic data is the seasonal adjustment of mixed frequency stock and flow time series. This may arise in practice due to changes in funding of a particular survey. Mathematically, the problem can be reduced to the need to compute imputations, forecasts, and backcasts from a given model of the highest available frequency data. The nonstationarity of the economic time series coupled with…