Journal of Statistical Planning and Inference最新文献

英文中文

Too Many, Too Improbable: Testing joint hypotheses and closed testing shortcuts 太多，太不可能：测试联合假设和封闭测试捷径

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-07-11 DOI: 10.1016/j.jspi.2025.106311

Phillip B. Mogensen, Bo Markussen

Hypothesis testing is a key part of empirical science and multiple testing as well as the combination of evidence from several tests are continued areas of research. In this article we consider the problem of combining the results of multiple hypothesis tests to (i) test global hypotheses and (ii) make marginal inference while controlling the

k

-FWER. We propose a new family of combination tests for joint hypotheses, called the ‘Too Many, Too Improbable’ (TMTI) statistics, which we show through simulation to have higher power than other combination tests against many alternatives. Furthermore, we prove that a large family of combination tests – which includes the one we propose but also other combination tests – admits a quadratic shortcut when used in a Closed Testing Procedure, which controls the FWER strongly. We develop an algorithm that is linear in the number of hypotheses for obtaining confidence sets for the number of false hypotheses among a collection of hypotheses and an algorithm that is cubic in the number of hypotheses for controlling the

k

-FWER for any

k

greater than one.

假设检验是实证科学的一个重要组成部分，多重检验以及多个检验证据的结合是实证科学的持续研究领域。在本文中，我们考虑将多个假设检验的结果组合到(i)检验全局假设和（ii）在控制k-FWER的情况下进行边际推理的问题。我们提出了一种新的联合假设组合检验系列，称为“太多，太不可能”（TMTI）统计，我们通过模拟表明，它比针对许多替代方案的其他组合检验具有更高的功率。此外，我们证明了在封闭测试程序中使用二次型捷径时，包括我们提出的组合测试和其他组合测试在内的一大族的组合测试都承认一个二次型捷径，该方法对FWER有很强的控制。我们开发了一种算法，该算法在假设数量上是线性的，用于在假设集合中获得假假设数量的置信集，并且在控制k- fwer的任何k大于1的假设数量上是三次的。

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引用次数: 0

Minimax designs for partially linear models 部分线性模型的极大极小设计

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-07-09 DOI: 10.1016/j.jspi.2025.106312

Shaohua Xu, Yongdao Zhou

Partially linear models are widely used in many scientific and engineering fields due to their flexibility and interpretability. However, the design of experiments for these models remains underexplored. This paper tackles the challenge of robust experimental design for partially linear models within a minimax framework, focusing on the simultaneous robustness of both the regression function and the basis function. We derive explicit forms of minimax designs for various scenarios, including partially linear models with and without interactions. These designs are shown to have analytical expressions, specifically as the product measure of the orthogonal array and the uniform measure. For practical implementation, we present the exact

n

-point minimax design based on the qualitative–quantitative discrepancy. Simulation results indicate that the proposed minimax designs are robust and efficient, even when the assumed model faces moderate or large contamination, or when the model is misspecified. Finally, the practical applicability of our minimax designs is demonstrated through a synthetic data based on the Quinidine Kinetics dataset.

部分线性模型由于其灵活性和可解释性被广泛应用于许多科学和工程领域。然而，这些模型的实验设计仍未得到充分探索。本文解决了在极小极大框架下部分线性模型的鲁棒性实验设计的挑战，重点关注回归函数和基函数的同时鲁棒性。我们推导了各种情况下的显式极大极小设计形式，包括有和没有相互作用的部分线性模型。这些设计被证明具有解析表达式，特别是作为正交阵列和均匀测量的乘积。在实际应用中，我们提出了基于定性-定量差异的精确n点极大极小设计。仿真结果表明，即使假设模型面临中等或较大的污染，或者当模型被错误指定时，所提出的极大极小设计也具有鲁棒性和有效性。最后，通过基于奎尼丁动力学数据集的合成数据证明了我们的极大极小设计的实际适用性。

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引用次数: 0

Individual aliased effect number pattern for two-level designs and its applications 两级设计的个别混叠效应数模式及其应用

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-07-07 DOI: 10.1016/j.jspi.2025.106316

Shengli Zhao, Tao Sun

Most existing criteria for selecting optimal fractional factorial designs consider the overall confounding of all effects and are proposed according to the effect hierarchy principle. However, in practical applications, especially when experimenters are interested in certain effects, the confounding information of individual effects is particularly important. We propose an individual aliased effect number pattern (I-AENP) for two-level designs to handle this situation and establish the relationship between I-AENP and the core patterns of several existing criteria. Some applications of the new pattern are discussed.

大多数现有的选择最佳分数因子设计的标准考虑了所有效应的总体混淆，并根据效应层次原则提出。然而，在实际应用中，特别是当实验者对某些效应感兴趣时，个体效应的混杂信息就显得尤为重要。为了解决这一问题，我们提出了一种用于两级设计的单个叠加效应数模式（I-AENP），并建立了I-AENP与几个现有标准核心模式之间的关系。讨论了新模式的一些应用。

引用次数: 0

Bootstrap-based tests for the total time on test and the excess wealth orders 基于引导的测试的总测试时间和剩余财富顺序

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-06-30 DOI: 10.1016/j.jspi.2025.106315

Tommaso Lando, Sirio Legramanti

Given a pair of non-negative random variables

X

and

Y

, we introduce a class of nonparametric tests for the null hypothesis that

X

dominates

Y

in the total time on test order. Critical values are determined using bootstrap-based inference, and the tests are shown to be consistent. The same approach is used to construct tests for the excess wealth order. As a byproduct, we also obtain a class of goodness-of-fit tests for the NBUE (New Better than Used in Expectation) family of distributions.

给定一对非负随机变量X和Y，我们引入了对X在总时间上优于Y的零假设的一类非参数检验。使用基于自举的推理来确定临界值，并且测试表明是一致的。同样的方法也用于构造过剩财富顺序的检验。作为副产品，我们还获得了NBUE （New Better than Used in Expectation）分布族的一类拟合优度检验。

引用次数: 0

On semi-supervised estimation using exponential tilt mixture models 指数倾斜混合模型的半监督估计

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-06-29 DOI: 10.1016/j.jspi.2025.106314

Ye Tian , Xinwei Zhang , Zhiqiang Tan

Consider a semi-supervised setting with a labeled dataset of binary responses and predictors and an unlabeled dataset with only the predictors. Logistic regression is equivalent to an exponential tilt model in the labeled population. For semi-supervised estimation of regression coefficients in logistic regression, we develop further analysis and understanding of a statistical approach using exponential tilt mixture (ETM) models and maximum nonparametric likelihood estimation, while allowing that the class proportions may differ between the unlabeled and labeled data. We derive asymptotic properties of ETM-based estimation and demonstrate improved efficiency over supervised logistic regression in a random sampling setup and an outcome-stratified sampling setup previously used. Moreover, we reconcile such efficiency improvement with the existing semiparametric efficiency theory when the class proportions in the unlabeled and labeled data are restricted to be the same. We also provide a simulation study to numerically illustrate our theoretical findings.

考虑一个半监督设置，其中包含二元响应和预测器的标记数据集，以及一个仅包含预测器的未标记数据集。逻辑回归相当于标记群体中的指数倾斜模型。对于逻辑回归中回归系数的半监督估计，我们进一步分析和理解了使用指数倾斜混合（ETM）模型和最大非参数似然估计的统计方法，同时允许未标记和标记数据之间的类比例可能不同。我们推导了基于etm估计的渐近性质，并证明了在随机抽样设置和先前使用的结果分层抽样设置中优于监督逻辑回归的效率。此外，当未标记和标记数据中的类比例被限制为相同时，我们将这种效率改进与现有的半参数效率理论相协调。我们还提供了一个模拟研究来数值说明我们的理论发现。

引用次数: 0

Limit results for estimation of connectivity matrix in multi-layer stochastic block models 多层随机块模型连通性矩阵估计的极限结果

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-06-23 DOI: 10.1016/j.jspi.2025.106313

Wenqing Su , Xiao Guo , Ying Yang

Multi-layer networks arise naturally in various domains including biology, finance and sociology, among others. The multi-layer stochastic block model (multi-layer SBM) is commonly used for community detection in the multi-layer networks. Most of current literature focuses on statistical consistency of community detection methods under multi-layer SBMs. However, the asymptotic distributional properties are also indispensable which play an important role in statistical inference. In this work, we aim to study the estimation and asymptotic properties of the layer-wise scaled connectivity matrices in the multi-layer SBM. We study and analyze a computationally tractable method for estimating the scaled connectivity matrices. Under the multi-layer SBM and its variant multi-layer degree-corrected SBM, we establish the asymptotic normality of the estimated matrices under mild conditions, which can be used for interval estimation and hypothesis testing. Simulations show the superior performance of proposed method over existing methods in two considered statistical inference tasks. We apply the method to a real dataset and obtain interpretable results. In addition, we develop a moment estimator for the non-scaled connectivity matrices and study its asymptotic properties.

多层网络自然出现在包括生物学、金融学和社会学在内的各个领域。多层随机块模型（multi-layer SBM）是多层网络中常用的社区检测方法。目前的文献大多集中在多层SBMs下社区检测方法的统计一致性上。然而，渐近分布性质也是不可缺少的，它在统计推断中起着重要作用。在这项工作中，我们的目的是研究多层SBM中逐层缩放连接矩阵的估计和渐近性质。我们研究和分析了一种计算上易于处理的估计尺度连通性矩阵的方法。在多层SBM及其变体多层度校正SBM下，我们建立了估计矩阵在温和条件下的渐近正态性，可用于区间估计和假设检验。仿真结果表明，在两种统计推断任务中，本文方法的性能优于现有方法。将该方法应用于实际数据集，得到了可解释的结果。此外，我们建立了一个无尺度连通性矩阵的矩估计量，并研究了它的渐近性质。

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引用次数: 0

Deconvolution density estimation using penalized splines 利用惩罚样条进行反褶积密度估计

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-06-17 DOI: 10.1016/j.jspi.2025.106310

Hanxiao Jing , Mary C. Meyer , Jiayang Sun

A straight-forward solution to the deconvolution density estimation involves penalized splines. A priori information about shape of the densities is readily imposed; for example the estimates may be constrained to be unimodal or bimodal. With quadratic splines and uniform errors, a cube-root convergence rate is attained. Simulations show that the estimators perform well compared to kernel estimators in a variety of scenarios.

反褶积密度估计的直接解决方案涉及惩罚样条。关于密度形状的先验信息很容易被强加；例如，估计可能被限制为单峰或双峰。在二次样条和均匀误差条件下，得到了一个立方根的收敛速度。仿真结果表明，在各种情况下，与核估计器相比，该估计器具有良好的性能。

引用次数: 0

Accelerated failure time model under dependent truncated data 相关截断数据下的加速失效时间模型

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-05-27 DOI: 10.1016/j.jspi.2025.106297

Jin-Jian Hsieh, Siang-Ying Chen

This paper delves into the accelerated failure time model within the framework of dependent truncation data and leverages the copula model to establish correlations within the dataset. Building upon the work of Chaieb et al. (2006), who utilized the copula-graphic method to estimate survival functions and proposed an approach for estimating correlation parameters, we further extend the methodology by introducing two distinct estimation techniques for regression parameters. The first method involves parameter evaluation through the calculation of the area between survival curves, while the second method employs the weight of survival jump in conjunction with the least squares approach to estimate regression parameters. We evaluate the efficacy of these proposed estimation procedures through simulation studies and conduct a comparative analysis between the two approaches. Furthermore, we apply these methodologies to two real-world datasets, providing insights into their practical applicability. Through this analysis, we gain a deeper understanding of how these approaches can be effectively utilized in real-world scenarios.

本文在相关截断数据框架下深入研究加速失效时间模型，并利用copula模型建立数据集内部的相关性。Chaieb等人（2006）利用copula-graph方法估计生存函数，并提出了一种估计相关参数的方法。在此基础上，我们通过引入两种不同的回归参数估计技术，进一步扩展了该方法。第一种方法是通过计算生存曲线之间的面积来评估参数，第二种方法是利用生存跳权结合最小二乘法来估计回归参数。我们通过模拟研究来评估这些建议的估计程序的有效性，并在两种方法之间进行比较分析。此外，我们将这些方法应用于两个真实世界的数据集，提供了对其实际适用性的见解。通过此分析，我们可以更深入地了解如何在实际场景中有效地利用这些方法。

引用次数: 0

Semiparametric modal regression with varying coefficients and measurement error 变系数半参数模态回归及测量误差

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-05-25 DOI: 10.1016/j.jspi.2025.106307

Aman Ullah , Tao Wang

We in this paper propose a stepwise estimation procedure for semiparametric modal regression with varying coefficients and measurement error, where the linear covariate is unobserved but an ancillary variable is available. This modal regression framework, which is built on the mode value rather than the mean, captures the “most likely” effect instead of the traditional average effect. The proposed stepwise procedure introduces a restricted regression mode by imposing a structural constraint on the model, allowing us to concentrate out the varying coefficients using the “correction for attenuation” method commonly employed in mean regression. This transformation reduces the original model to a parametric modal regression. We establish the consistency and asymptotic normality of the resulting modal estimators by analyzing the tail behavior of the characteristic function of the error distribution, distinguishing between ordinary smooth and super smooth cases. Additionally, we investigate bandwidth selection strategies and propose a simulation-extrapolation algorithm as a practical approach for optimal bandwidth choice. We conduct Monte Carlo simulations to assess the finite sample performance of the resulting estimators and present a real data analysis to further illustrate the effectiveness of the suggested estimation procedure.

本文提出了一种具有变系数和测量误差的半参数模态回归的逐步估计方法，其中线性协变量是不可观测的，但辅助变量是可用的。这种模态回归框架是建立在模态值而不是平均值的基础上的，它捕捉的是“最可能”的效应，而不是传统的平均效应。所提出的逐步过程通过对模型施加结构约束引入了一种受限回归模式，允许我们使用平均回归中常用的“衰减校正”方法集中出变化系数。这种转换将原始模型简化为参数模态回归。通过分析误差分布特征函数的尾部行为，区分普通光滑和超光滑情况，建立了所得模态估计量的相合性和渐近正态性。此外，我们研究了带宽选择策略，并提出了一种模拟外推算法作为最优带宽选择的实用方法。我们进行蒙特卡罗模拟，以评估所得估计器的有限样本性能，并提供真实的数据分析，以进一步说明所建议的估计过程的有效性。

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引用次数: 0

Divide and conquer for generalized approximately expectile regression 广义近似期望回归的分治法

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2025-05-19 DOI: 10.1016/j.jspi.2025.106300

Zhen Zeng , Weixin Yao

When the size of the dataset becomes extremely large, it is computationally challenge for traditional statistical estimation methods and might be infeasible to store all the data on a single computer. Under the massive data framework, we extend the divide and conquer method to the generalized approximately expectile regression and investigate both of their finite and asymptotic properties. Bahadur representation of the estimators are established. Moreover, we prove that with the appropriate number of subsamples, the proposed method can ensure the accuracy of statistical inference. Simulations studies validate our theoretical findings. Supplementary materials for this article are available online.

当数据集的规模变得非常大时，传统的统计估计方法在计算上是一个挑战，并且在一台计算机上存储所有的数据可能是不可行的。在海量数据框架下，我们将分治法推广到广义近似期望回归，并研究了它的有限性质和渐近性质。建立了估计量的Bahadur表示。此外，我们还证明了在适当的子样本数量下，所提出的方法可以保证统计推断的准确性。模拟研究验证了我们的理论发现。本文的补充材料可在网上获得。

引用次数: 0

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Journal of Statistical Planning and Inference

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