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平衡开关实验中含系统测量误差的 fMRI 时间序列因果推断AI 解读

Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate. Building on Rubin's causal model, we construct a framework for causal inference using blood oxygenation level…

利用倾向得分调整的广义 Kendall’s Tau 识别成瘾遗传变异AI 解读

Identifying replicable genetic variants for addiction has been extremely challenging. Besides the common difficulties with genome-wide association studies (GWAS), environmental factors are known to be critical to addiction, and comorbidity is widely observed. Despite the importance of environmental factors and comorbidity for addiction study, few GWAS analyses adequately considered them due to the limitations of the existing statistical methods. Although parametric methods…

基于最小描述长度原则的分段分位数回归模型选择AI 解读

This article proposes new model-fitting techniques for quantiles of an observed data sequence, including methods for data segmentation and variable selection. The main contribution, however, is in providing a means to perform these two tasks simultaneously. This is achieved by matching the data with the best-fitting piecewise quantile regression model, where the fit is determined by a penalization derived from the minimum description length principle. The resulting…

非高斯函数型数据的广义高斯过程回归AI 解读

In this article, we propose a generalized Gaussian process concurrent regression model for functional data, where the functional response variable has a binomial, Poisson, or other non-Gaussian distribution from an exponential family, while the covariates are mixed functional and scalar variables. The proposed model offers a nonparametric generalized concurrent regression method for functional data with multidimensional covariates, and provides a natural framework on…

厚尾噪声下的滤波AI 解读

An unobserved components model in which the signal is buried in noise that is non-Gaussian may throw up observations that, when judged by the Gaussian yardstick, are outliers. We describe an observation-driven model, based on a conditional Student’s t-distribution, which is tractable and retains some of the desirable features of the linear Gaussian model. Letting the dynamics be driven by the score of the conditional distribution leads to a specification that is not only…

时变方差下自回归过程二阶动态的检验AI 解读

This article considers the volatility modeling for autoregressive univariate time series. A benchmark approach is the stationary autoregressive conditional heteroscedasticity (ARCH) model of Engle. Motivated by real data evidence, processes with nonconstant unconditional variance and ARCH effects have been recently introduced. We take into account this type of nonstationarity in variance and propose simple testing procedures for ARCH effects. Adaptive McLeod and Li’s…

用于年龄依赖分类的针对性局部支持向量机AI 解读

We develop methods to accurately predict whether pre-symptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example age. To identify effective prediction rules using nonparametric decision functions,…

具有跳跃不连续性的神经影像空间变系数模型AI 解读

Motivated by recent work on studying massive imaging data in various neuroimaging studies, we propose a novel spatially varying coefficient model (SVCM) to capture the varying association between imaging measures in a three-dimensional (3D) volume (or 2D surface) with a set of covariates. Two stylized features of neuorimaging data are the presence of multiple piecewise smooth regions with unknown edges and jumps and substantial spatial correlations. To specifically account…

时空点过程的函数主成分分析及疾病监测应用AI 解读

In disease surveillance applications, the disease events are modeled by spatio-temporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatio-temporal process as spatially correlated functional data, and propose Poisson maximum likelihood and composite likelihood methods based on spline approximations…

缺失数据回归中的多重稳健估计AI 解读

Doubly robust estimators are widely used in missing-data analysis. They provide double protection on estimation consistency against model misspecifications. However, they allow only a single model for the missingness mechanism and a single model for the data distribution, and the assumption that one of these two models is correctly specified is restrictive in practice. For regression analysis with possibly missing outcome, we propose an estimation method that allows multiple…

模型选择与不确定性下的内在贝叶斯推断AI 解读

In this article, we present a fully coherent and consistent objective Bayesian analysis of the linear regression model using intrinsic priors. The intrinsic prior is a scaled mixture of g-priors and promotes shrinkage toward the subspace defined by a base (or null) model. While it has been established that the intrinsic prior provides consistent model selectors across a range of models, the posterior distribution of the model parameters has not previously been investigated.…

主成分与共同主成分的高效秩估计AI 解读

We propose rank-based estimators of principal components, both in the one-sample and, under the assumption of common principal components, in the m-sample cases. Those estimators are obtained via a rank-based version of Le Cam’s one-step method, combined with an estimation of cross-information quantities. Under arbitrary elliptical distributions with, in the m-sample case, possibly heterogeneous radial densities, those R-estimators remain root-n consistent and asymptotically…

非参数回归函数的自适应置信带AI 解读

A new formulation for the construction of adaptive confidence bands in non-parametric function estimation problems is proposed. Confidence bands are constructed which have size that adapts to the smoothness of the function while guaranteeing that both the relative excess mass of the function lying outside the band and the measure of the set of points where the function lies outside the band are small. It is shown that the bands adapt over a maximum range of Lipschitz…

多对照匹配观察研究中具有更高设计敏感度的加权 M 统计量AI 解读

In a nonrandomized or observational study, a weak association between receipt of the treatment and an outcome may be explained not as effects caused by the treatment but rather by a small bias in the assignment of individuals to treatment or control; however, a strong association may be explained as noncausal only by a large bias. The strength of the association between treatment and outcome is not uniform across the data from a study, and this motivates giving greater…

用稀疏线性规划优化随机试验总体与两个亚组的治疗效应检验AI 解读

We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such subpopulations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is…

结合扩展贝叶斯信息准则的序贯 Lasso 超高维特征选择AI 解读

In this article, we propose a method called sequential Lasso (SLasso) for feature selection in sparse high-dimensional linear models. The SLasso selects features by sequentially solving partially penalized least squares problems where the features selected in earlier steps are not penalized. The SLasso uses extended BIC (EBIC) as the stopping rule. The procedure stops when EBIC reaches a minimum. The asymptotic properties of SLasso are considered when the dimension of the…

模型选择后的估计与精度评估AI 解读

Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and confidence intervals that take model selection into account. The methodology involves bagging, also known as bootstrap smoothing, to tame the erratic discontinuities of selection-based estimators. A useful new formula for the accuracy of bagging then provides standard errors for the smoothed estimators. Two examples,…

强自相关时间序列的 HAC 修正AI 解读

Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations. In this article, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference that remains valid under a specific form of strong dependence. In particular, I…

评论AI 解读

Conducting valid inference in a time series setting often requires the use of a heteroscedasticity and autocorrelation (HAC) robust variance estimator. Under mild dependence structures, the theory ...

评述AI 解读

Inference in time series settings is complicated by the possibility of strong autocorrelation in the data. In general, some aspect of a time series model is assumed to satisfy stationarity and weak...

评述AI 解读

Müller applies the theory of optimal statistical testing to heteroscedasticity and autocorrelation robust (HAR) inference in the presence of strong autocorrelation. As a starting point, Müller uses...

评述AI 解读

Müller looks at the problems of interval estimation and hypothesis testing in autocorrelated models from a frequency domain point of view. This leads to good insights as well as proposals for new m...

预测回归模型的非参数检验AI 解读

This article considers testing the significance of a regressor with a near unit root in a predictive regression model. The procedures discussed in this article are nonparametric, so one can test the significance of a regressor without specifying a functional form. The results are used to test the null hypothesis that the entire function takes the value of zero. We show that the standardized test has a normal distribution regardless of whether there is a near unit root in the…

基于惩罚样条与狄利克雷过程混合的贝叶斯非参数工具变量回归AI 解读

We propose a Bayesian nonparametric instrumental variable approach under additive separability that allows us to correct for endogeneity bias in regression models where the covariate effects enter with unknown functional form. Bias correction relies on a simultaneous equations specification with flexible modeling of the joint error distribution implemented via a Dirichlet process mixture prior. Both the structural and instrumental variable equation are specified in terms of…