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连续非线性交互作用稳健检验的交叉验证集成方法AI 解读

Gene-environment and nutrition-environment studies often involve testing of high-dimensional interactions between two sets of variables, each having potentially complex nonlinear main effects on an outcome. Construction of a valid and powerful hypothesis test for such an interaction is challenging, due to the difficulty in constructing an efficient and unbiased estimator for the complex, nonlinear main effects. In this work, we address this problem by proposing a…

匹配后的稳健统计推断AI 解读

Nearest-neighbor matching is a popular nonparametric tool to create balance between treatment and control groups in observational studies. As a preprocessing step before regression, matching reduces the dependence on parametric modeling assumptions. In current empirical practice, however, the matching step is often ignored in the calculation of standard errors and confidence intervals. In this article, we show that ignoring the matching step results in asymptotically valid…

观察性研究中结局依赖抽样的因果效应界AI 解读

Outcome-dependent sampling designs are common in many different scientific fields including epidemiology, ecology, and economics. As with all observational studies, such designs often suffer from unmeasured confounding, which generally precludes the nonparametric identification of causal effects. Nonparametric bounds can provide a way to narrow the range of possible values for a nonidentifiable causal effect without making additional untestable assumptions. The nonparametric…

半参数 VARMA 模型的中心向外秩估计AI 解读

We propose a new class of R-estimators for semiparametric VARMA models in which the innovation density plays the role of the nuisance parameter. Our estimators are based on the novel concepts of multivariate center-outward ranks and signs. We show that these concepts, combined with Le Cam’s asymptotic theory of statistical experiments, yield a class of semiparametric estimation procedures, which are efficient (at a given reference density), root-n consistent, and…

从分散化投影学习潜在因子：因子数高估与弱因子的应用AI 解读

Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that factors be relatively strong, data are stationary and weakly serially dependent, and the sample size be fairly large, although in practical applications, one or several of these conditions may fail. In these cases, it is difficult to analyze the eigenvectors of the data matrix. To address this issue,…

含大量协变量的线性回归异方差稳健推断AI 解读

We consider inference in linear regression models that is robust to heteroscedasticity and the presence of many control variables. When the number of control variables increases at the same rate as the sample size the usual heteroscedasticity-robust estimators of the covariance matrix are inconsistent. Hence, tests based on these estimators are size distorted even in large samples. An alternative covariance-matrix estimator for such a setting is presented that complements…

Warp 桥采样的新一代方法AI 解读

Bridge sampling is an effective Monte Carlo (MC) method for estimating the ratio of normalizing constants of two probability densities, a routine computational problem in statistics, physics, chemistry, and other fields. The MC error of the bridge sampling estimator is determined by the amount of overlap between the two densities. In the case of unimodal densities, Warp-I, II, and III transformations are effective for increasing the initial overlap, but they are less so for…

通过调整特征值阈值估计因子数量AI 解读

Determining the number of common factors is an important and practical topic in high dimensional factor models. The existing literatures are mainly based on the eigenvalues of the covariance matrix. Due to the incomparability of the eigenvalues of the covariance matrix caused by heterogeneous scales of observed variables, it is very difficult to give an accurate relationship between these eigenvalues and the number of common factors. To overcome this limitation, we appeal to…

多维函数型数据的低秩协方差函数估计AI 解读

Jiayi Wanga, Raymond K. W. Wonga* & Xiaoke Zhangb a Department of Statistics, Texas A&M University, College Station, TX; b Department of Statistics, George Washington University, Washington, DC Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA. CONTACT Raymond K. W. Wong raywong@tamu.edu Department of Statistics, Texas A&M University , College Station , TX 77843.AbstractMultidimensional function data arise from many fields…

基于模式导向抽样的大规模数据流监测AI 解读

Monitoring large-scale datastreams with limited resources has become increasingly important for real-time detection of abnormal activities in many applications. Despite the availability of large datasets, the challenges associated with designing an efficient change-detection when clustering or spatial pattern exists are not yet well addressed. In this article, a design-adaptive testing procedure is developed when only a limited number of streaming observations can be…

函数型数据的 Copula 高斯图模型AI 解读

We introduce a statistical graphical model for multivariate functional data, which are common in medical applications such as EEG and fMRI. Recently published functional graphical models rely on the multivariate Gaussian process assumption, but we relax it by introducing the functional copula Gaussian graphical model (FCGGM). This model removes the marginal Gaussian assumption but retains the simplicity of the Gaussian dependence structure, which is particularly attractive…

随机系数二元响应模型的非参数极大似然方法AI 解读

The venerable method of maximum likelihood has found numerous recent applications in nonparametric estimation of regression and shape constrained densities. For mixture models the nonparametric maximum likelihood estimator (NPMLE) of Kiefer and Wolfowitz plays a central role in recent developments of empirical Bayes methods. The NPMLE has also been proposed by Cosslett as an estimation method for single index linear models for binary response with random coefficients.…

刻画效应修饰的半参数方法AI 解读

One fundamental statistical question for research areas such as precision medicine and health disparity is about discovering effect modification of treatment or exposure by observed covariates. We propose a semiparametric framework for identifying such effect modification. Instead of using the traditional outcome models, we directly posit semiparametric models on contrasts, or expected differences of the outcome under different treatment choices or exposures. Through…

通过与替代分布混合进行贝叶斯因子的蒙特卡洛近似AI 解读

By mixing the target posterior distribution with a surrogate distribution, of which the normalizing constant is tractable, we propose a method for estimating the marginal likelihood using the Wang–Landau algorithm. We show that a faster convergence of the proposed method can be achieved via the momentum acceleration. Two implementation strategies are detailed: (i) facilitating global jumps between the posterior and surrogate distributions via the multiple-try Metropolis…

相关数据的交叉验证AI 解读

–K-fold cross-validation (CV) with squared error loss is widely used for evaluating predictive models, especially when strong distributional assumptions cannot be taken. However, CV with squared error loss is not free from distributional assumptions, in particular in cases involving non-iid data. This article analyzes CV for correlated data. We present a criterion for suitability of standard CV in presence of correlations. When this criterion does not hold, we introduce a…

面向多响应数据的低秩高维多元线性模型估计AI 解读

In this article, we study low rank high-dimensional multivariate linear models (LRMLM) for high-dimensional multi-response data. We propose an intuitively appealing estimation approach and develop an algorithm for implementation purposes. Asymptotic properties are established to justify the estimation procedure theoretically. Intensive simulation studies are also conducted to demonstrate performance when the sample size is finite, and a comparison is made with some popular…

基于高斯混合模型的多变量缺失数据半参数分数插补AI 解读

Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach of imputation for handling missing data. However, the parametric fractional imputation may be subject to bias under model misspecification. In this article, we propose a novel semiparametric fractional imputation (SFI) method using Gaussian mixture models. The proposed method is computationally…

基于最优匹配的分布无关多样本检验及单细胞基因组学应用AI 解读

In this article, we propose a nonparametric graphical test based on optimal matching, for assessing the equality of multiple unknown multivariate probability distributions. Our procedure pools the data from the different classes to create a graph based on the minimum non-bipartite matching, and then utilizes the number of edges connecting data points from different classes to examine the closeness between the distributions. The proposed test is exactly distribution-free (the…

Hellinger 相关系数AI 解读

In this article, the defining properties of any valid measure of the dependence between two continuous random variables are revisited and complemented with two original ones, shown to imply other usual postulates. While other popular choices are proved to violate some of these requirements, a class of dependence measures satisfying all of them is identified. One particular measure, that we call the Hellinger correlation, appears as a natural choice within that class due to…

局部高斯偏相关AI 解读

It is well known in econometrics and other fields that the dependence structure for jointly Gaussian variables can be fully captured using correlations, and that the conditional dependence structure in the same way can be described using partial correlations. The partial correlation does not, however, characterize conditional dependence in many non-Gaussian populations. This article introduces the local Gaussian partial correlation (LGPC), a new measure of conditional…

可能存在水平突变时的积分阶数半参数检验AI 解读

Lobato and Robinson developed semiparametric tests for the null hypothesis that a series is weakly autocorrelated, or I(0), about a constant level, against fractionally integrated alternatives. These tests have the advantage that the user is not required to specify a parametric model for any weak autocorrelation present in the series. We extend this approach in two distinct ways. First, we show that it can be generalized to allow for testing of the null hypothesis that a…

依赖关系对欧洲市场风险的影响：非参数时变方法AI 解读

Multivariate dependence measures are crucial for risk management, where variables usually have heavy tails and non-Gaussian distributions. We propose a multivariate time varying Kendall’s tau estimator in a nonparametric context, allowing for local stationary variables. Consistency and asymptotic normality of the estimator are provided. A simulation study is conducted which supports the idea of better performance than other related methods in many complex scenarios. The…

基于时间序列分组数据的洛伦兹曲线贝叶斯估计AI 解读

This study is concerned with estimating the inequality measures associated with the underlying hypothetical income distribution from the times series grouped data on the income proportions. We adopt the Dirichlet likelihood approach where the parameters of the Dirichlet likelihood are set to the differences between the Lorenz curve of the hypothetical income distribution for the consecutive income classes and propose a state-space model which combines the transformed…

基于广义帕累托分布的风险分析AI 解读

We compute the value-at-risk of financial losses by fitting a generalized Pareto distribution to exceedances over a threshold. Following the common practice of setting the threshold as high sample quantiles, we show that, for both independent observations and time-series data, the asymptotic variance for the maximum likelihood estimation depends on the choice of threshold, unlike the existing study of using a divergent threshold. We also propose a random weighted bootstrap…