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Semiparametric tests for Lorenz dominance based on density ratio model 基于密度比模型的Lorenz优势度半参数检验
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-11-12 DOI: 10.1016/j.jspi.2025.106361
Weiwei Zhuang , Weiqi Yang , Wenchen Liao , Yukun Liu
Lorenz dominance is a fundamental tool for assessing whether wealth or income disparity is greater in one population than another. Based on the well-established density ratio model, we propose a new semiparametric test for Lorenz dominance. We show that the limiting distribution of the proposed test statistic is the supremum of a Gaussian process. To facilitate practical application, we devise a bootstrap procedure to calculate the p-value and establish its theoretical validity. Our simulation studies demonstrate that the proposed test correctly controls the Type I error and outperforms its competitors in terms of statistical power. Finally, we apply the test to compare salary distributions among higher education employees in Ohio from 2011 to 2015.
洛伦兹优势是评估一个人群的财富或收入差距是否大于另一个人群的基本工具。基于已建立的密度比模型,我们提出了一种新的洛伦兹优势度的半参数检验方法。我们证明了所提出的检验统计量的极限分布是高斯过程的极大值。为了便于实际应用,我们设计了一个自举程序来计算p值并验证其理论有效性。我们的仿真研究表明,所提出的测试正确地控制了I型误差,并在统计功率方面优于其竞争对手。最后,我们运用该检验比较了2011 - 2015年俄亥俄州高等教育员工的薪酬分布。
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引用次数: 0
Self-weighted estimation for nonstationary processes with infinite variance GARCH errors 具有无限方差GARCH误差的非平稳过程的自加权估计
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-11-08 DOI: 10.1016/j.jspi.2025.106360
Yuze Yuan , Shuyu Liu , Rongmao Zhang
Zhang and Chan (2021) considered the augmented Dickey–Fuller (ADF) test for an unit root process with linear noise driven by generalized autoregressive conditional heteroskedasticity (GARCH), and showed that the ADF test may perform even worse than the Dickey–Fuller test. The main reason is that the parameters of the lag terms in the ADF regression cannot be estimated consistently for infinite variance GARCH noises based on least square estimation (LSE). In this paper, we propose a self-weighted least square estimation (SWLSE) procedure to solve this problem. Consequently, a new test based on SWLSE for the unit-root is also proposed. It is shown that the SWLSE are consistent, and the proposed test converges to a functional of a stable process and a Brownian motion and performs well in term of size and power. Simulation study is conducted to evaluate the performance of our procedure, and a real-world illustrative example is provided.
Zhang和Chan(2021)考虑了广义自回归条件异方差(GARCH)驱动线性噪声的单位根过程的增广Dickey-Fuller (ADF)检验,并表明ADF检验的表现可能比Dickey-Fuller检验更差。主要原因是基于最小二乘估计(LSE)的无限方差GARCH噪声的ADF回归中滞后项的参数无法一致估计。本文提出一种自加权最小二乘估计(SWLSE)方法来解决这一问题。在此基础上,提出了一种新的基于SWLSE的单位根检验方法。结果表明,SWLSE是一致的,所提出的测试收敛于稳定过程和布朗运动的泛函,并且在大小和功率方面表现良好。通过仿真研究对该方法的性能进行了评价,并给出了一个实例。
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引用次数: 0
Mixed latent graphical models with mixed measurement error and misclassification in variables 具有混合测量误差和变量误分类的混合潜在图形模型
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-11-01 DOI: 10.1016/j.jspi.2025.106359
Yu Shi , Grace Y. Yi
Graphical models are powerful tools for characterizing conditional dependence structures among variables with complex relationships. Although many methods have been developed under the graphical modeling framework, their validity often hinges on the quality of the data. A fundamental assumption in most existing approaches is that all variables are measured precisely, an assumption frequently violated in practice. In many applications, mismeasurement of mixed discrete and continuous variables is a common challenge. In this paper, we address error-contaminated data involving both continuous and discrete variables by proposing a mixed latent Gaussian copula graphical measurement error model. To perform inference, we develop a simulation-based expectation–maximization procedure that explicitly accounts for mismeasurement effects. We further introduce a computationally efficient refinement to reduce the computational burden. Asymptotic properties of the proposed estimator are established, and its finite-sample performance is evaluated through numerical studies.
图形模型是描述具有复杂关系的变量间条件依赖结构的有力工具。尽管在图形建模框架下开发了许多方法,但它们的有效性往往取决于数据的质量。大多数现有方法的一个基本假设是,所有变量都是精确测量的,这一假设在实践中经常被违反。在许多应用中,离散和连续混合变量的测量错误是一个常见的挑战。在本文中,我们通过提出一个混合潜在高斯耦合图形测量误差模型来处理涉及连续和离散变量的误差污染数据。为了进行推理,我们开发了一个基于模拟的期望最大化程序,该程序明确地说明了误测量效应。我们进一步引入了一种计算效率高的改进来减少计算负担。建立了该估计器的渐近性质,并通过数值研究对其有限样本性能进行了评价。
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引用次数: 0
General sliced minimum aberration designs for multi-platform experiments 用于多平台实验的一般切片最小像差设计
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-10-30 DOI: 10.1016/j.jspi.2025.106357
Yuliang Zhou, Qianqian Zhao, Shengli Zhao
Sliced designs are widely used in multi-platform experiments. A sliced design contains several sub-designs divided by the sliced factor, and each sub-design is assigned to a platform, respectively. In some experimental scenarios, it is necessary to consider the optimality of both the sub-designs and the complete sliced designs, such sliced designs are referred to as general sliced (GS) designs. To construct the optimal GS designs for such scenarios, we propose the general sliced effect hierarchy principle (GSEHP). Based on the GSEHP, we introduce the general sliced minimum aberration (GSMA) criterion and choose the GSMA designs as optimal GS designs when the sliced factor and design factors are equally important. Some GSMA designs with 32 and 64 runs are tabulated. Additionally, we present a practical example to illustrate the application of GSMA designs in guiding strategies of webpage setting on two platforms.
切片设计广泛应用于多平台实验。一个切片设计包含若干个被切片因子划分的子设计,每个子设计分别分配给一个平台。在某些实验场景中,需要同时考虑子设计和完整切片设计的最优性,这种切片设计称为一般切片设计(GS)。为了构建这种场景下的最优GS设计,我们提出了通用切片效应层次原则(GSEHP)。在GSEHP的基础上,引入了通用最小像差(GSMA)准则,并在切片因素和设计因素同等重要的情况下,选择GSMA设计作为最优的GS设计。一些运行32次和64次的GSMA设计被制成表格。此外,我们还通过一个实例说明了GSMA设计在两个平台的网页设置指导策略中的应用。
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引用次数: 0
Robust and consistent model evaluation criteria in high-dimensional regression 高维回归中稳健一致的模型评价准则
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-10-28 DOI: 10.1016/j.jspi.2025.106358
Sumito Kurata, Kei Hirose
Most of the regularization methods such as the LASSO have one (or more) regularization parameter(s), and to select the value of the regularization parameter is essentially equal to select a model. Thus, to obtain a model suitable for the data and phenomenon, we need to determine an adequate value of the regularization parameter. Regarding the determination of the regularization parameter in the linear regression model, we often apply the information criteria like the AIC and BIC, however, it has been pointed out that these criteria are sensitive to outliers and tend not to perform well in high-dimensional settings. Outliers generally have a negative effect on not only estimation but also model selection, consequently, it is important to employ a selection method with robustness against outliers. In addition, when the number of explanatory variables is quite large, most conventional criteria are prone to select unnecessary explanatory variables. In this paper, we propose model evaluation criteria based on the statistical divergence with excellence in robustness in both of parametric estimation and model selection, by applying the quasi-Bayesian procedure. Our proposed criteria achieve the selection consistency even in high-dimensional settings due to precise approximation, simultaneously with robustness. We also investigate the conditions for establishing robustness and consistency, and provide an appropriate example of the divergence and penalty term that can achieve the desirable properties. We finally report the results of some numerical examples to verify that the proposed criteria perform robust and consistent variable selection compared with the conventional selection methods.
大多数正则化方法(如LASSO)都有一个(或多个)正则化参数,选择正则化参数的值本质上等于选择一个模型。因此,为了获得适合于数据和现象的模型,我们需要确定一个适当的正则化参数值。对于线性回归模型中正则化参数的确定,我们通常采用AIC和BIC等信息准则,但已有研究指出,这些准则对异常值敏感,在高维环境下往往表现不佳。异常值不仅对估计有负面影响,而且对模型选择也有负面影响,因此,采用对异常值具有鲁棒性的选择方法非常重要。此外,当解释变量的数量相当大时,大多数常规标准容易选择不必要的解释变量。本文应用拟贝叶斯过程,提出了基于统计散度的模型评价准则,该准则在参数估计和模型选择上都具有较好的鲁棒性。我们提出的标准即使在高维环境下,由于精确的近似,也能实现选择一致性,同时具有鲁棒性。我们还研究了建立鲁棒性和一致性的条件,并提供了一个适当的散度和惩罚项的例子,可以达到期望的性质。最后给出了一些数值算例,验证了所提出的准则与传统的选择方法相比具有鲁棒性和一致性。
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引用次数: 0
Tuning differential evolution algorithm for constructing uniform projection designs 构造均匀投影设计的差分进化优化算法
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-10-21 DOI: 10.1016/j.jspi.2025.106356
Samuel Onyambu, Hongquan Xu
Space-filling designs are extensively used in computer experiments to analyze complex systems. Among these, uniform projection designs stand out for their desirable low-dimensional projection properties and robustness against other criteria. However, no efficient algorithm currently exists for generating such designs. This study explores the construction of uniform projection designs using a differential evolution (DE) algorithm. DE, an evolutionary algorithm, is known for its simplicity, robustness, and effectiveness in solving complex optimization problems, though its performance is highly sensitive to several hyperparameters. Our goal is to investigate the structure of the hyperparameter space, evaluate the contribution of each hyperparameter, and provide guidelines for optimal hyperparameter settings across various scenarios. To achieve this, we conduct a comprehensive comparison of different experimental designs and surrogate models.
空间填充设计在分析复杂系统的计算机实验中被广泛使用。其中,均匀投影设计以其理想的低维投影特性和对其他标准的鲁棒性而脱颖而出。然而,目前还没有有效的算法来生成这样的设计。本研究探讨了使用差分进化(DE)算法构建均匀投影设计。DE是一种进化算法,以其简单性、鲁棒性和解决复杂优化问题的有效性而闻名,尽管它的性能对几个超参数非常敏感。我们的目标是研究超参数空间的结构,评估每个超参数的贡献,并为各种场景下的最佳超参数设置提供指导。为了实现这一点,我们对不同的实验设计和替代模型进行了全面的比较。
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引用次数: 0
Variable selection in high-dimensional varying coefficient panel data models with fixed effects 固定效应高维变系数面板数据模型的变量选择
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-09-30 DOI: 10.1016/j.jspi.2025.106355
Yiping Yang , Peixin Zhao
To address the challenges of variable selection in panel data models with fixed effects and varying coefficients, we introduce a novel method that combines basis function approximations with group nonconcave penalty functions. By utilizing a forward orthogonal deviation transformation, we eliminate fixed effects, allowing us to select significant variables and estimate non-zero coefficient functions. Under certain regularity conditions, we demonstrate that our method consistently identifies the true model structure, and the resulting estimators exhibit oracle properties. For computational efficiency, we have developed a group gradient descent algorithm that incorporates a transformation of the penalty terms. Simulation studies reveal that nonconvex penalties (SCAD/MCP) outperform the Lasso across various performance metrics. Furthermore, compared to existing methods, our approach significantly reduces false positives (FPs). To demonstrate the practical applicability and effectiveness of our method, we present an analysis of a real dataset.
为了解决固定效应和变系数面板数据模型中变量选择的挑战,我们提出了一种结合基函数逼近和群非凹惩罚函数的新方法。通过利用正向正交偏差变换,我们消除了固定效应,允许我们选择重要变量并估计非零系数函数。在一定的规则条件下,我们证明了我们的方法一致地识别了真实的模型结构,并且得到的估计器显示了oracle属性。为了提高计算效率,我们开发了一种包含惩罚项变换的群梯度下降算法。仿真研究表明,非凸惩罚(SCAD/MCP)在各种性能指标上都优于Lasso。此外,与现有方法相比,我们的方法显著降低了误报(FPs)。为了证明我们的方法的实用性和有效性,我们给出了一个真实数据集的分析。
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引用次数: 0
Causal inference in early phase clinical trials: Variance decomposition and order of patient inclusion 早期临床试验的因果推断:方差分解和患者纳入顺序
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-09-29 DOI: 10.1016/j.jspi.2025.106352
Matthieu Clertant , Meliha Akouba , Alexia Iasonos , John O’Quigley
Causal inference tools, in particular those of variance decomposition, hierarchical data structures and counterfactuals, are applied to the study of the methodology of dose-finding studies in oncology. A detailed variance decomposition brings into a much sharper focus the relative performance of different designs. We develop and present new results on the role played by the order of patient inclusions into a sequential dose-finding study. These results make it clear why, previously, authors could easily be misled into a conclusion that different designs enjoy similar performances. This is not so and we show how to avoid making that mistake. We highlight our findings via both theoretical and numerical studies.
因果推理工具,特别是方差分解、分层数据结构和反事实的工具,应用于肿瘤学剂量发现研究方法的研究。详细的方差分解使不同设计的相对性能得到更清晰的关注。我们开发并提出了新的结果,在顺序的剂量发现研究中,患者包裹体的顺序所起的作用。这些结果清楚地表明,为什么以前,作者很容易被误导得出不同设计具有相似性能的结论。事实并非如此,我们将展示如何避免犯这种错误。我们通过理论和数值研究强调了我们的发现。
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引用次数: 0
The k-sample Behrens-Fisher problem for high-dimensional data with model free assumption 具有无模型假设的高维数据k样本Behrens-Fisher问题
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-09-27 DOI: 10.1016/j.jspi.2025.106354
Yanbo Pei, Xiaoxiao Ren, Baoxue Zhang
The problem of testing the equality of k-sample mean vectors with different covariance matrices, known as the Behrens-Fisher (BF) problem for k-sample, is a significant issue in statistics. Hu and Bai (2017) proposed a test statistic that operates under a factor-like model structure assumption and demonstrated its normal limit. Building on this work, we further explore the asymptotic properties of the test statistic. We prove that the asymptotic null distribution of the test statistic is a Chi-square-type mixture distribution under a model-free assumption and establish its asymptotic power under a full alternative hypothesis. Moreover, we show that the asymptotic null distribution of the test statistic is either normal or a weighted sum of normal and Chi-square random variables, depending on the convergence rate of the eigenvalues of the covariance matrix with model free assumption. To address practical challenges in high-dimensional data, we propose a new weighted bootstrap procedure that is simple to implement. Simulation studies demonstrate that our proposed test procedure outperforms existing methods in terms of size control under various settings. Furthermore, real data applications illustrate the applicability of our test procedure to a variety of high-dimensional data analysis problems.
用不同的协方差矩阵检验k-样本均值向量是否相等的问题,被称为k-样本的Behrens-Fisher (BF)问题,是统计学中的一个重要问题。Hu和Bai(2017)提出了在类因子模型结构假设下运行的检验统计量,并证明了其正常极限。在此基础上,我们进一步探讨了检验统计量的渐近性质。在无模型假设下证明了检验统计量的渐近零分布是一个卡方型混合分布,在完全备择假设下证明了检验统计量的渐近幂。此外,我们证明了检验统计量的渐近零分布要么是正态分布,要么是正态和卡方随机变量的加权和,这取决于在无模型假设下协方差矩阵的特征值的收敛速度。为了解决高维数据中的实际挑战,我们提出了一种新的加权自举过程,该过程易于实现。仿真研究表明,我们提出的测试程序在各种设置下的尺寸控制方面优于现有方法。此外,实际数据应用说明了我们的测试程序对各种高维数据分析问题的适用性。
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引用次数: 0
Joint distribution of numbers of occurrences of countably many runs of specified lengths in a sequence of discrete random variables 离散随机变量序列中指定长度的可数多次运行的出现次数的联合分布
IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Pub Date : 2025-09-25 DOI: 10.1016/j.jspi.2025.106353
Kiyoshi Inoue
In this paper, we consider the joint distribution of numbers of occurrences of countably many runs of several lengths in a sequence of nonnegative integer valued independent and identically distributed random variables through the generating functions. We propose a generalization of the potential partition polynomials, which gives effective computational tools for the derivation of probability functions. The waiting time problems associated with infinitely many runs are investigated and formulae for the evaluation of the generating functions are given. The results presented here provide a wide framework for developing the multivariate distribution theory of runs. Finally, we discuss several applications and numerical examples to show how our theoretical results are applied to the investigation of runs, as well as parameter estimation problems.
本文通过生成函数研究了非负整数值独立同分布随机变量序列中若干长度的可数多次运行的出现次数的联合分布。我们提出了一种潜在配分多项式的推广方法,它为概率函数的推导提供了有效的计算工具。研究了无限次运行的等待时间问题,给出了生成函数的求值公式。本文提出的结果为发展多变量运行分布理论提供了一个广泛的框架。最后,我们讨论了几个应用和数值例子,以显示我们的理论结果如何应用于研究运行,以及参数估计问题。
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引用次数: 0
期刊
Journal of Statistical Planning and Inference
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