Statistica Sinica最新文献

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On the Efficiency of Composite Likelihood Estimation for Gaussian Spatial Processes 高斯空间过程的复合似然估计效率研究

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2024-01-01 DOI: 10.5705/ss.202020.0311

N. Chua, Francis K. C. Hui, A. Welsh

the Efficiency of Composite Likelihood

最大复合似然估计是标准最大似然估计的一种有吸引力且常用的替代方法，标准最大似然估计通常涉及牺牲统计效率以提高计算效率。这种统计效率可以通过评估最大复合似然估计量的三明治信息矩阵来量化，然后将其与最大似然估计量的类似Fisher信息矩阵进行比较。本文导出了一维指数协方差高斯过程的各种极大复合似然估计的渐近相对效率的新的封闭表达式。这些表达式基于一种抽样方案，该方案允许在三种常见的空间渐近框架下进行分析:扩展域、填充和混合。我们的结果证明了复合似然的选择如何影响估计的效率和一致性，特别是对于填充和混合框架。

引用次数: 0

Adaptive Randomization via Mahalanobis Distance 基于马氏距离的自适应随机化

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2024-01-01 DOI: 10.5705/ss.202020.0440

Yichen Qin, Y. Li, Wei Ma, Haoyu Yang, F. Hu

: In comparative studies, researchers often seek an optimal covariate balance. However, chance imbalance still exists in randomized experiments, and becomes more serious as the number of covariates increases. To address this issue, we introduce a new randomization procedure, called adaptive randomization via the Mahalanobis distance (ARM). The proposed method allocates units sequentially and adaptively, using information on the current level of imbalance and the incoming unit’s covariate. Theoretical results and numerical comparison show that with a large number of covariates or a large number of units, the proposed method shows substantial advantages over traditional methods in terms of the covariate balance, estimation accuracy, hypothesis testing power, and computational time. The proposed method attains the optimal covariate balance, in the sense that the estimated treatment effect attains its minimum variance asymptotically, and can be applied in both causal inference and clinical trials. Lastly, numerical stud-1

在比较研究中，研究人员经常寻求最佳协变量平衡。然而，随机实验中仍然存在机会不平衡现象，并且随着协变量数量的增加，机会不平衡现象更加严重。为了解决这个问题，我们引入了一种新的随机化程序，称为通过马氏距离(ARM)的自适应随机化。该方法利用当前不平衡水平和输入单元的协变量信息，自适应地顺序分配单元。理论结果和数值比较表明，在协变量较多或单位较多的情况下，本文提出的方法在协变量平衡、估计精度、假设检验能力、计算时间等方面都比传统方法有较大的优势。该方法实现了最优协变量平衡，即估计的治疗效果渐近地达到其最小方差，可以应用于因果推理和临床试验。最后，数值研究[中国统计:预印本doi:10.5705/ss.202020.0440]

引用次数: 2

Regression Analysis of Spatially Correlated Event Durations With Missing Origins Annotated by Longitudinal Measures 纵向测量中缺失起源的空间相关事件持续时间回归分析

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0118

Y. Xiong, W. J. Braun, T. Duchesne, X. J. Hu

This paper is concerned with event durations in situations where the study units may be spatially correlated and the time origins of the events are missing. We develop regression models based on the partly observed durations with the aid of available longitudinal information. The first-hitting-time model (e.g. Lee and Whitmore, 2006) is employed to link the data of event durations and the associated longitudinal measures with shared random effects. We present procedures for estimating the model parameters and an induced estimator of the conditional distribution of the event duration. We apply the EM algorithm and Monte Carlo methods to compute the proposed estimators. We establish consistency and asymptotic normality of the estimators, and present their variance estimation. The proposed approach is illustrated with a collection of wildfire records from Alberta, Canada. Its performance is examined numerically and compared with two competitors via simulation.

本文关注的是在研究单元可能是空间相关的，而事件的时间起源缺失的情况下的事件持续时间。我们开发回归模型基于部分观测的持续时间与可用的纵向信息的帮助。采用首次撞击时间模型(如Lee和Whitmore, 2006)将事件持续时间和相关纵向测量数据与共享随机效应联系起来。我们提出了估计模型参数的程序和事件持续时间条件分布的诱导估计器。我们应用EM算法和蒙特卡罗方法来计算所提出的估计量。我们建立了估计量的相合性和渐近正态性，并给出了它们的方差估计。所提出的方法以加拿大阿尔伯塔省的野火记录集为例。对其性能进行了数值检验，并与两种竞争产品进行了仿真比较。

引用次数: 0

Simultaneous Functional Quantile Regression 同时功能分位数回归

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0248

Boyi Hu, Xixi Hu, Hua Liu, Jinhong You, Jiguo Cao

The conventional method for functional quantile regression (FQR) is to fit the regression model for each quantile of interest separately. Therefore, the slope function of the regression, as a bivariate function of time and quantile, is estimated as a univariate function of time for each fixed quantile. However, there are several limitations to this conventional strategy. For example, it cannot guarantee the monotonicity of the conditional quantiles, nor can it control the smoothness of the slope estimator as a bivariate function. In this paper, we propose a new framework for FQR, in which we simultaneously fit the FQR model for multiple quantiles, with the help of a bivariate basis under some constraints, such that the estimated quantiles satisfy the monotonicity conditions and the smoothness of the slope estimator is controlled. The proposed estimator for the slope function is shown to be asymptotically consistent, and we establish its asymptotic normality. We use simulation to evaluate the finite-sample performance of the proposed method and compare it with that of the conventional method. We demonstrate the proposed method by analyzing the effects of Statistica Sinica: Preprint doi:10.5705/ss.202021.0248

功能分位数回归(FQR)的传统方法是对每个感兴趣的分位数分别拟合回归模型。因此，回归的斜率函数作为时间和分位数的二元函数，被估计为每个固定分位数的单变量时间函数。然而，这种传统策略有几个限制。例如，它不能保证条件分位数的单调性，也不能控制斜率估计器作为二元函数的平滑性。本文提出了一种新的FQR框架，在一定的约束条件下，利用二元基同时拟合多个分位数的FQR模型，使估计的分位数满足单调性条件，并控制斜率估计量的平滑性。证明了所提出的斜率函数的估计量是渐近一致的，并建立了其渐近正态性。通过仿真对该方法的有限样本性能进行了评价，并与传统方法进行了比较。我们通过分析中国统计:预印本doi:10.5705/ss.202021.0248的效果来证明所提出的方法

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引用次数: 0

Generalized Odds Rate Frailty Models for Current Status Data with Informative Censoring 具有信息过滤的当前状态数据的广义优势率脆弱模型

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0411

Yang Xu, Shishun Zhao, T. Hu, Jianguo Sun

: Current-status data occur in many areas, and the analysis of such data attracted much attention. In this study, we consider a regression analysis of current-status data in the presence of informative censoring, for which most existing methods either apply only to limited situations or are computationally unstable. Here, we propose a new sieve maximum likelihood estimation procedure under the class of semiparametric generalized odds rate frailty models. The proposed method uses the latent variable to describe the informative censoring or relationship between the failure time of interest and the censoring time. We develop a novel expectation-maximization algorithm for determining the proposed estimators, and establish their asymptotic consistency and normality. The results of a simulation study show that the proposed method performs well in practical

现状数据出现在许多领域，对这些数据的分析引起了人们的广泛关注。在本研究中，我们考虑在存在信息审查的情况下对当前状态数据进行回归分析，因为大多数现有方法要么只适用于有限的情况，要么在计算上不稳定。在半参数广义优势率脆弱性模型下，我们提出了一种新的筛极大似然估计方法。该方法使用隐变量来描述信息的审查或感兴趣的失效时间与审查时间之间的关系。我们开发了一种新的期望最大化算法来确定所提出的估计量，并建立了它们的渐近相合性和正态性。仿真研究结果表明，该方法在实际应用中具有良好的性能。E-mail: hutaomath@foxmail.com中国统计:预印本doi:10.5705/ss.202021.0411

引用次数: 0

Statistical Inference for Mean Function of Longitudinal Imaging Data over Complicated Domains 复杂域纵向成像数据均值函数的统计推断

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0415

Qirui Hu, Jie Li

We propose a novel procedure for estimating the mean function of longitudinal imaging data with inherent spatial and temporal correlation. We depict the dependence between temporally ordered images using a functional moving average, and use flexible bivariate splines over triangulations to handle the irregular domain of images which is common in imaging studies. We establish both the global and the local asymptotic properties of the bivariate spline estimator for the mean function, with simultaneous confidence corridors (SCCs) as a theoretical byproduct. Under some mild conditions, the proposed estimator and its accompanying SCCs are shown to be consistent and oracle efficient, as though all images were entirely observed without errors. We use Monte Carlo simulation experiments to demonstrate the finite-sample performance of the proposed method, the results of which strongly corroborate the asymptotic theory. The proposed method is further illustrated by analyzing two seawater potential temperature data sets.

引用次数: 1

Group Testing Regression Analysis with Missing Data and Imperfect Tests 缺失数据和不完善检验的组检验回归分析

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0382

A. Delaigle, Ruoxu Tan

: Estimating the prevalence of an infectious disease in a big population typically requires testing a specimen (e.g., blood, urine, or swab) for the disease. When the disease spreads quickly, time constraints and limited resources often restrict the number of tests that can be performed. In such cases, if the prevalence is not too high, the group testing procedure can be employed to save time, money, and resources. The procedure tests pooled specimens of groups of individuals, rather than testing each individual for the disease. This technique is also used in other contexts, for example, to detect abnormalities or contamination in animals, plants, food, or water. Although methods exist for estimating a prevalence conditional on the explanatory variables from the group testing data, they require the specimen to be available for all individuals, which is not always possible. Therefore, we construct new nonparametric estimators that are consistent when some of the specimens are missing. We demonstrate the numerical performance of our methods using simulations and a hepatitis B example.

估计传染病在大人群中的流行情况通常需要检测该疾病的标本(如血液、尿液或拭子)。当疾病迅速传播时，时间限制和有限的资源往往会限制可进行的检测数量。在这种情况下，如果患病率不是太高，可以采用分组测试程序来节省时间、金钱和资源。该程序测试汇集了个体群体的标本，而不是对每个个体进行疾病检测。这项技术也可用于其他场合，例如，检测动物、植物、食物或水的异常或污染。虽然现有方法可以根据群体测试数据的解释变量来估计患病率，但它们要求所有个体都可以获得样本，这并不总是可能的。因此，我们构造了新的非参数估计量，当某些样本缺失时，它是一致的。我们用模拟和一个乙型肝炎的例子来证明我们的方法的数值性能。

引用次数: 1

A Linear Errors-in-Variables Model with Unknown Heteroscedastic Measurement Errors 具有未知异方差测量误差的线性变量误差模型

IF 1.4 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2023-10-21 DOI: 10.5705/ss.202022.0331

L. Nghiem, Cornelis J. Potgieter

In the classic measurement error framework, covariates are contaminated by independent additive noise. This paper considers parameter estimation in such a linear errors-in-variables model where the unknown measurement error distribution is heteroscedastic across observations. We propose a new generalized method of moment (GMM) estimator that combines a moment correction approach and a phase function-based approach. The former requires distributions to have four finite moments, while the latter relies on covariates having asymmetric distributions. The new estimator is shown to be consistent and asymptotically normal under appropriate regularity conditions. The asymptotic covariance of the estimator is derived, and the estimated standard error is computed using a fast bootstrap procedure. The GMM estimator is demonstrated to have strong finite sample performance in numerical studies, especially when the measurement errors follow non-Gaussian distributions.

在经典的测量误差框架中，协变量受到独立加性噪声的污染。本文考虑了这种线性变量误差模型中的参数估计问题，在这种模型中，未知测量误差分布在各观测值之间是异方差的。我们提出了一种新的广义矩法（GMM）估计方法，它结合了矩修正方法和基于相位函数的方法。前者要求分布具有四个有限矩，而后者则依赖于具有非对称分布的协变量。在适当的正则性条件下，新的估计器具有一致性和渐近正态性。推导出了估计器的渐近协方差，并使用快速引导程序计算了估计标准误差。在数值研究中，特别是当测量误差遵循非高斯分布时，证明了 GMM 估计器具有很强的有限样本性能。

引用次数: 0

NONPARAMETRIC ESTIMATION AND TESTING FOR PANEL COUNT DATA WITH INFORMATIVE TERMINAL EVENT. 具有信息终端事件的面板计数数据的非参数估计和检验。

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2023-10-01 DOI: 10.5705/ss.202021.0213

Xiangbin Hu, Li Liu, Ying Zhang, Xingqiu Zhao

Informative terminal events often occur in the long term recurrent event follow-up studies. To reflect their effects on recurrent event processes explicitly, we propose a reversed nonparametric mean model for panel count data with a terminal event subject to right censoring. This model enjoys meaningful interpretation for applications and robustness for statistical inference. Treating the distribution of the right-censored terminal event time as a nuisance functional parameter, we develop a two-stage estimation procedure by combining the Kaplan-Meier estimator and nonparametric sieve estimation techniques. The consistency, convergence rate and asymptotic normality of the proposed nonparametric estimator are established. Then we construct a class of new statistics for two-sample test. The asymptotic properties of the new tests are established and evaluated by extensive simulation studies. Panel count data from Chinese Longitudinal Healthy Longevity study are analyzed using the proposed method for illustration.

信息性终末事件经常发生在长期复发事件随访研究中。为了明确地反映它们对反复事件过程的影响，我们提出了一个反向非参数平均模型，用于具有右审查的终端事件的面板计数数据。该模型对应用有意义的解释，对统计推断具有鲁棒性。将右截尾终端事件时间的分布作为一个累加函数参数，结合Kaplan-Meier估计和非参数筛估计技术，提出了一种两阶段估计方法。给出了该非参数估计量的相合性、收敛率和渐近正态性。然后构造了一类新的双样本检验统计量。通过大量的模拟研究，建立并评估了新测试的渐近性质。采用本文提出的方法对中国纵向健康寿命研究的面板计数数据进行了分析。

引用次数: 0

Use of random integration to test equality of high dimensional covariance matrices. 使用随机积分来检验高维协方差矩阵的相等性。

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Sinica

Pub Date : 2023-10-01 DOI: 10.5705/ss.202020.0486

Yunlu Jiang, Canhong Wen, Yukang Jiang, Xueqin Wang, Heping Zhang

Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. Through a novel use of random integration, we can test the equality of high-dimensional covariance matrices without assuming parametric distributions for the two underlying populations, even if the dimension is much larger than the sample size. The asymptotic properties of our test for arbitrary number of covariates and sample size are studied in depth under a general multivariate model. The finite-sample performance of our test is evaluated through numerical studies. The empirical results demonstrate that our test is highly competitive with existing tests in a wide range of settings. In particular, our proposed test is distinctly powerful under different settings when there exist a few large or many small diagonal disturbances between the two covariance matrices.

检验两个协方差矩阵的相等性是统计学中的一个基本问题，当数据是高维时尤其具有挑战性。通过一种新的随机积分方法，我们可以在不假设两个潜在群体的参数分布的情况下测试高维协方差矩阵的相等性，即使维数远大于样本量。在一般的多元模型下，深入研究了我们对任意数量的协变量和样本大小的检验的渐近性质。通过数值研究评估了我们测试的有限样本性能。实证结果表明，我们的测试在广泛的环境中与现有测试具有很强的竞争力。特别地，当两个协方差矩阵之间存在一些大的或许多小的对角扰动时，我们提出的测试在不同的设置下是明显强大的。

引用次数: 0

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