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大规模相关性筛选AI 解读

This article addresses the problem of screening for variables with high correlations in high-dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single treatment (autocorrelation screening), screening for variables with large cross-correlations over two treatments (cross-correlation screening), and screening…

多变量时间序列的动态正交成分AI 解读

We introduce dynamic orthogonal components (DOC) for multivariate time series and propose a procedure for estimating and testing the existence of DOCs for a given time series. We estimate the dynamic orthogonal components via a generalized decorrelation method that minimizes the linear and quadratic dependence across components and across time. We then use Ljung–Box type statistics to test the existence of dynamic orthogonal components. When DOCs exist, univariate analysis…

计数数据的贝叶斯核混合模型AI 解读

Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not…

随机效应分位数回归模型的半参数方法AI 解读

We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds to the population-average effects of explanatory variables on the conditional quantile of interest, while the random coefficients represent cluster specific deviations in the…

死亡截断结局下主层化因果效应的可识别性与估计AI 解读

In medical studies, there are many situations where the final outcomes are truncated by death, in which patients die before outcomes of interest are measured. In this article we consider identifiability and estimation of causal effects by principal stratification when some outcomes are truncated by death. Previous studies mostly focused on large sample bounds, Bayesian analysis, sensitivity analysis. In this article, we propose a new method for identifying the causal…

积分波动率的预测推断AI 解读

Numerous volatility-based derivative products have been engineered in recent years. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this article we propose nonparametric estimators of the aforementioned quantities, based on model-free volatility estimators. We establish consistency and asymptotic normality for the feasible estimators and study their finite-sample properties through a Monte Carlo…

耦合可选 Pólya 树与两样本问题AI 解读

Testing and characterizing the difference between two data samples is of fundamental interest in statistics. Existing methods such as Kolmogorov–Smirnov and Cramer–von Mises tests do not scale well as the dimensionality increases and provide no easy way to characterize the difference should it exist. In this work, we propose a theoretical framework for inference that addresses these challenges in the form of a prior for Bayesian nonparametric analysis. The new prior is…

统计学：一门包罗万象的学科AI 解读

Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data.A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean.Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow…

离散指数族的不稳定性、敏感性与退化AI 解读

In applications to dependent data, first and foremost relational data, a number of discrete exponential family models has turned out to be near-degenerate and problematic in terms of Markov chain Monte Carlo simulation and statistical inference. We introduce the notion of instability with an eye to characterize, detect, and penalize discrete exponential family models that are near-degenerate and problematic in terms of Markov chain Monte Carlo simulation and statistical…

多元二元数据的序约束推断及其毒理学应用AI 解读

In many applications researchers collect multivariate binary response data under two or more, naturally ordered, experimental conditions. In such situations one is often interested in using all binary outcomes simultaneously to detect an ordering among the experimental conditions. To make such comparisons we develop a general methodology for testing for the multivariate stochastic order between K ≥ 2 multivariate binary distributions. The proposed test uses order restricted…

利用高维生物标志物轨迹预测病毒感染AI 解读

There is often interest in predicting an individual's latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent…

因果推断中连续中间变量的贝叶斯半参数建模AI 解读

In causal inference studies, treatment comparisons often need to be adjusted for confounded post-treatment variables. Principal stratification (PS) is a framework to deal with such variables within the potential outcome approach to causal inference. Continuous intermediate variables introduce inferential challenges to PS analysis. Existing methods either dichotomize the intermediate variable, or assume a fully parametric model for the joint distribution of the potential…

收入波动异质性的半参数贝叶斯建模AI 解读

Research on income risk typically treats its proxy—income volatility, the expected magnitude of income changes—as if it were unchanged for an individual over time, the same for everyone at a point in time, or both. In reality, income risk evolves over time, and some people face more of it than others. To model heterogeneity and dynamics in (unobserved) income volatility, we develop a novel semiparametric Bayesian stochastic volatility model. Our Markovian hierarchical…

采用正交象限正态先验的弹性网回归建模AI 解读

The elastic net procedure is a form of regularized optimization for linear regression that provides a bridge between ridge regression and the lasso. The estimate that it produces can be viewed as a Bayesian posterior mode under a prior distribution implied by the form of the elastic net penalty. This article broadens the scope of the Bayesian connection by providing a complete characterization of a class of prior distributions that generate the elastic net estimate as the…

多区域采样及其在贝叶斯网络结构推断中的应用AI 解读

When a posterior distribution has multiple modes, unconditional expectations, such as the posterior mean, may not offer informative summaries of the distribution. Motivated by this problem, we propose to decompose the sample space of a multimodal distribution into domains of attraction of local modes. Domain-based representations are defined to summarize the probability masses of and conditional expectations on domains of attraction, which are much more informative than the…

正则化回归估计推断的扰动方法AI 解读

Analysis of high dimensional data often seeks to identify a subset of important features and assess their effects on the outcome. Traditional statistical inference procedures based on standard regression methods often fail in the presence of high-dimensional features. In recent years, regularization methods have emerged as promising tools for analyzing high dimensional data. These methods simultaneously select important features and provide stable estimation of their…

基于似然比检验的信号检测及其 FDA 药物安全数据应用AI 解读

Several statistical methods that are available in the literature to analyze postmarket safety databases, such as the U.S. Federal Drug Administration’s (FDA) adverse event reporting system (AERS), for identifying drug-event combinations with disproportionately high frequencies, are subject to high false discovery rates. Here, we propose a likelihood ratio test (LRT) based method and show, via an extensive simulation study, that the proposed method while retaining good power…

结合混合效应建模的高维常微分方程动态基因调控网络识别AI 解读

Gene regulation is a complicated process. The interaction of many genes and their products forms an intricate biological network. Identification of this dynamic network will help us understand the biological process in a systematic way. However, the construction of such a dynamic network is very challenging for a high-dimensional system. In this article we propose to use a set of ordinary differential equations (ODE), coupled with dimensional reduction by clustering and…

评论AI 解读

Inspired by the non-regular framework studied in Laber and Murphy (2011), we propose a family of adaptive classifiers. We discuss briefly their asymptotic properties and show that under the non-regular framework these classifiers have an "oracle property," and consequently have smaller asymptotic variance and smaller asymptotic test error variance than those of the original classifier. We also show that confidence intervals for the test error of the adaptive classifiers,…

局部平稳过程的平稳性度量及检验应用AI 解读

In this article we investigate the problem of measuring deviations from stationarity in locally stationary time series. Our approach is based on a direct estimate of the L2-distance between the spectral density of the locally stationary process and its best approximation by a spectral density of a stationary process. An explicit expression of the minimal distance is derived, which depends only on integrals of the spectral density of the locally stationary process and its…

总体归属不确定时多个总体的密度估计AI 解读

We devise methods to estimate probability density functions of several populations using observations with uncertain population membership, meaning from which population an observation comes is unknown. The probability of an observation being sampled from any given population can be calculated. We develop general estimation procedures and bandwidth selection methods for our setting. We establish large-sample properties and study finite-sample performance using simulation…

函数混合模型框架下的稳健自适应函数回归AI 解读

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be…

动态因子模型的广义最小二乘估计AI 解读

In this article a simple two-step estimation procedure of the dynamic factor model is proposed. The estimator allows for heteroscedastic and serially correlated errors. It turns out that the feasible two-step estimator has the same limiting distribution as the generalized least squares (GLS) estimator assuming that the covariance parameters are known. In a Monte Carlo study of the small sample properties, we find that the GLS estimators may be substantially more efficient…

总体值分解：图像总体分析的框架AI 解读

Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating images recorded for hundreds or thousands of subjects at multiple visits. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality…