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群体影像数据的检验与差异位置恢复AI 解读

In this article, we propose a multiscale adaptive test to detect differences between two samples of intrinsically smoothed image data in high-dimensional context. The test aggregates data from nearby locations using adaptive weights, significantly enhancing statistical power. We demonstrate that the test statistic converges to a Gumbel extreme value distribution under the null hypothesis. Moreover, we investigate its multiscale nature, showing that the chosen scales can grow…

基于 Bregman 散度的深度互密度比估计及其应用AI 解读

This article introduces a unified approach to estimating the mutual density ratio, defined as the ratio between the joint density function and the product of the individual marginal density functions of two random vectors. It serves as a fundamental measure for quantifying the relationship between two random vectors. Our method uses the Bregman divergence to construct the objective function and leverages deep neural networks to approximate the logarithm of the mutual density…

连续标记特异的分位数回归模型AI 解读

Summary Quantile regression has become a widely used tool for analysing competing risk data. However, quantile regression for competing risk data with a continuous mark is still scarce. The mark variable is an extension of cause of failure in a classical competing risk model where cause of failure is replaced by a continuous mark only observed at uncensored failure times. An example of the continuous mark variable is the genetic distance that measures dissimilarity between…

含成分协变量的高维线性对数对比模型稳健信号恢复AI 解读

In this article, we propose a robust signal recovery method for high-dimensional linear log-contrast models, when the error distribution could be heavy-tailed and asymmetric. The proposed method is built on the Huber loss with ℓ1 penalization. We establish the ℓ1 and ℓ2 consistency for the resulting estimator. Under conditions analogous to the irrepresentability condition and the minimum signal strength condition, we prove that the signed support of the slope parameter…

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…

信息性观测时点与依赖终止事件下的纵向数据联合分析AI 解读

In many longitudinal studies, repeated measures are often correlated with observation times. Also, there may exist a dependent terminal event such as death that stops the follow-up. In this article, we propose a new joint model for the analysis of longitudinal data in the presence of both informative observation times and a dependent terminal event via latent variables. Estimating equation approaches are developed for parameter estimation, and the resulting estimators are…

非参数比例风险模型的全局偏似然AI 解读

As an alternative to the local partial likelihood method of Tibshirani and Hastie and Fan, Gijbels, and King, a global partial likelihood method is proposed to estimate the covariate effect in a nonparametric proportional hazards model, λ(t|x) = exp{ψ(x)}λ(0)(t). The estimator, ψ̂(x), reduces to the Cox partial likelihood estimator if the covariate is discrete. The estimator is shown to be consistent and semiparametrically efficient for linear functionals of ψ(x). Moreover,…

右删失生存数据的一类变换平均剩余寿命模型AI 解读

The mean residual life function is an attractive alternative to the survival function or the hazard function of a survival time in practice. It provides the remaining life expectancy of a subject surviving up to time t. In this study, we propose a class of transformed mean residual life models for fitting survival data under right censoring. To estimate the model parameters, we make use of the inverse probability of censoring weighting approach and develop a system of…

信息性观测与删失时间下的纵向数据回归分析AI 解读

Longitudinal data frequently occur in many studies, such as longitudinal follow-up studies. To develop statistical methods and theory for the analysis of these data, independent or noninformative observation and censoring times are typically assumed, which naturally leads to inference procedures conditional on observation and censoring times. But in many situations this may not be true or realistic; that is, longitudinal responses may be correlated with observation times as…

信息性观测时点下纵向数据的半参数回归分析AI 解读

Statistical analysis of longitudinal data has been discussed by many authors, and a number of methods have been proposed. Most of the research have focused on situations where observation times are independent of or carry no information about the response variable and therefore rely on conditional inference procedures given the observation times. This article considers a different situation, where the independence assumption may not hold; that is, the observation times may…

估计重复事件过程的期望总事件数AI 解读

We consider estimation of the cumulative mean function of a process recurring in time, such as the numbers of arrests or migrations accrued by an individual, as a function of their age. We call this theage profile of a series of events. In some situations we can expect a finite value for the total number of events experienced by an individual, for example, when the distribution of the interevent times is improper, so that the process may cease at a finite time with positive…