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近历元依赖重尾时间序列的极端局部线性分位数回归AI 解读

This paper develops a data-driven inference procedure for extreme analysis of data with near epoch dependence (NED), a condition less restrictive than traditional dependence structures like α-mixing, making it particularly useful for analyzing heavy-tailed time series data. To capture nonlinear data structures, we propose a new framework that combines local linear quantile regression with extreme value theory. We first study the asymptotic properties of the local-linear…

复杂区域上分位数空间变系数模型的估计与推断AI 解读

This article presents a flexible quantile spatially varying coefficient model (QSVCM) for the regression analysis of spatial data. The proposed model enables researchers to assess the dependence of conditional quantiles of the response variable on covariates while accounting for spatial nonstationarity. Our approach facilitates learning and interpreting heterogeneity in spatial data distributed over complex or irregular domains. We introduce a quantile regression method that…

分位数回归中的单指标门限模型AI 解读

Threshold regression models are useful for identifying subgroups with heterogeneous parameters. The conventional threshold regression models split the sample based on a single and observed threshold variable, which enforces the threshold point to be equal for all subgroups of the population. In this article, we consider a more flexible single-index threshold model in the quantile regression setup, in which the sample is split based on a linear combination of predictors. We…

超高维图像对标量回归中的稀疏学习与结构识别AI 解读

This article considers high-dimensional image-on-scalar regression, where the spatial heterogeneity of covariate effects on imaging responses is investigated via a flexible partially linear spatially varying coefficient model. To tackle the challenges of spatial smoothing over the imaging response’s complex domain consisting of regions of interest, we approximate the spatially varying coefficient functions via bivariate spline functions over triangulation. We first study…

基于分位数回归的面板数据聚类AI 解读

In panel data analysis, it is important to identify subgroups of units with heterogeneous parameters. This can not only increase the model flexibility but also produce more efficient estimation by pooling information across units within the same group. In this paper, we propose a new quantile-regression-based clustering method for panel data. We develop an iterative algorithm using a similar idea of k-means clustering to identify subgroups with heterogeneous slopes at a…

自回归模型的极端分位数估计AI 解读

A quantile autoregresive model is a useful extension of classical autoregresive models as it can capture the influences of conditioning variables on the location, scale, and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this article, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time series data based on extreme…

通过条件边际回归检验遗漏协变量的显著性AI 解读

Researchers sometimes have a priori information on the relative importance of predictors that can be used to screen out covariates. An important question is whether any of the discarded covariates have predictive power when the most relevant predictors are included in the model. We consider testing whether any discarded covariate is significant conditional on some pre-chosen covariates. We propose a maximum-type test statistic and show that it has a nonstandard asymptotic…

分位数回归中的边际线性效应检验AI 解读

Summary The paper develops a new marginal testing procedure to detect significant predictors that are associated with the conditional quantiles of a scalar response. The idea is to fit the marginal quantile regression on each predictor one at a time, and then to base the test on the t-statistics that are associated with the most predictive predictors. A resampling method is devised to calibrate this test statistic, which has non-regular limiting behaviour due to the…

通过幂变换估计极端条件分位数AI 解读

The estimation of extreme conditional quantiles is an important issue in numerous disciplines. Quantile regression (QR) provides a natural way to capture the covariate effects at different tails of the response distribution. However, without any distributional assumptions, estimation from conventional QR is often unstable at the tails, especially for heavy-tailed distributions due to data sparsity. In this article, we develop a new three-stage estimation procedure that…

重尾分布的高位条件分位数估计AI 解读

Estimation of conditional quantiles at very high or low tails is of interest in numerous applications. Quantile regression provides a convenient and natural way of quantifying the impact of covariates at different quantiles of a response distribution. However, high tails are often associated with data sparsity, so quantile regression estimation can suffer from high variability at tails especially for heavy-tailed distributions. In this article, we develop new estimation…

协变量受删失时 M 回归的多重插补AI 解读

We develop a new multiple imputation approach for M-regression models with censored covariates. Instead of specifying parametric likelihoods, our method imputes the censored covariates by their conditional quantiles given the observed data, where the conditional quantiles are estimated through fitting a censored quantile regression process. The resulting estimator is shown to be consistent and asymptotically normal, and it improves the estimation efficiency by using…

部分线性变系数模型中的分位数回归AI 解读

Semiparametric models are often considered for analyzing longitudinal data for a good balance between flexibility and parsimony. In this paper, we study a class of marginal partially linear quantile models with possibly varying coefficients. The functional coefficients are estimated by basis function approximations. The estimation procedure is easy to implement, and it requires no specification of the error distributions. The asymptotic properties of the proposed estimators…

局部加权删失分位数回归AI 解读

Censored quantile regression offers a valuable supplement to Cox proportional hazards model for survival analysis. Existing work in the literature often requires stringent assumptions, such as unconditional independence of the survival time and the censoring variable or global linearity at all quantile levels. Moreover, some of the work uses recursive algorithms, making it challenging to derive asymptotic normality. To overcome these drawbacks, we propose a new locally…

纵向研究中删失分位数回归模型的推断AI 解读

We develop inference procedures for longitudinal data where some of the measurements are censored by fixed constants. We consider a semi-parametric quantile regression model that makes no distributional assumptions. Our research is motivated by the lack of proper inference procedures for data from biomedical studies where measurements are censored due to a fixed quantification limit. In such studies the focus is often on testing hypotheses about treatment equality. To this…