Statistica Neerlandica最新文献

英文中文

Efficient estimation for the multivariate Cox model with missing covariates. 缺少协变量的多变量Cox模型的有效估计。

IF 0.8 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2025-02-01 Epub Date: 2025-01-30 DOI: 10.1111/stan.70000

Youngjoo Cho, Soyoung Kim, Kwang Woo Ahn

Missing covariates are a ubiquitous issue in the data analysis. One of the widely-used approaches for efficient parameter estimation is using augmentation based on the semiparametric efficiency theory. However, existing methods for right-censored data with Cox model did not correctly implement augmentation, which may result in inefficient parameter estimation. In this paper, we derive a correct augmentation term for the stratified proportional hazards model with missing covariates. We study the statistical properties of the estimators for known and unknown missing mechanisms. Thus, a popular study design such as the casecohort study design can be handled as a special case. Simulation studies show that our new estimators for an unknown missing mechanism and the case-cohort study design obtain estimation efficiency gains compared with inverse probability weighted estimators. We apply our method to the Atherosclerosis Risk in Communities study under the case-cohort study design.

协变量缺失是数据分析中普遍存在的问题。基于半参数效率理论的增广法是目前广泛应用的有效参数估计方法之一。然而，现有的Cox模型右截尾数据增强方法没有正确实现，可能导致参数估计效率低下。本文导出了缺少协变量的分层比例风险模型的一个正确增广项。研究了已知和未知缺失机制估计量的统计性质。因此，一个流行的研究设计，如病例研究设计，可以作为一个特殊的情况下处理。仿真研究表明，与逆概率加权估计器相比，我们的未知缺失机制估计器和病例队列研究设计获得了估计效率的提高。我们采用病例队列研究设计，将我们的方法应用于社区动脉粥样硬化风险研究。

引用次数: 0

On global robustness of an adversarial risk analysis solution 论对抗性风险分析解决方案的全局稳健性

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-09-09 DOI: 10.1111/stan.12361

Jinming Yang, Chaitanya Joshi, Fabrizio Ruggeri

Adversarial Risk Analysis (ARA) can be a more realistic and practical alternative to traditional game theoretic or decision theoretic approaches for modeling strategic decision‐making in the presence of an adversary. ARA relies on quantifying the decision‐maker's (DM's) uncertainties about the adversary's strategic thinking, choices and utilities via probability distributions to identify the optimal solution for the DM. ARA solution will be sensitive to the choices of prior distributions used for modelling the expert beliefs. Yet, to date, no mathematical results to characterize the robustness of the ARA solution to the misspecification of one or more prior distributions exist. Prior elicitation is known to be challenging. We present the very first mathematical results on the global robustness of the ARA solution. We use the distorted band class of priors and establish the conditions under which an ordering on the ARA solution can be established when modelling the first‐price sealed‐bid auctions using the nonstrategic play and level‐ thinking solution concepts. We illustrate these results using numerical examples and discuss further areas of research.

与传统的博弈论或决策论方法相比，对抗性风险分析（ARA）是一种更现实、更实用的方法，可用于在有对手的情况下建立战略决策模型。对抗风险分析依赖于通过概率分布量化决策者（DM）对对手战略思维、选择和效用的不确定性，从而为决策者确定最优解。ARA 解决方案对用于模拟专家信念的先验分布的选择非常敏感。然而，迄今为止，还没有任何数学结果可以描述 ARA 解决方案对一个或多个先验分布的错误指定的鲁棒性。众所周知，先验激发具有挑战性。我们首次提出了 ARA 解决方案全局鲁棒性的数学结果。我们使用了扭曲带类先验，并建立了一些条件，在这些条件下，使用非战略博弈和水平思维解决方案概念对第一价格密封出价拍卖进行建模时，可以建立 ARA 解决方案的排序。我们用数字示例说明了这些结果，并讨论了进一步的研究领域。

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引用次数: 0

Heterogeneous dense subhypergraph detection 异构密集子超图检测

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-09-03 DOI: 10.1111/stan.12360

Mingao Yuan, Zuofeng Shang

We study the problem of testing the existence of a heterogeneous dense subhypergraph. The null hypothesis corresponds to a heterogeneous Erdös–Rényi uniform random hypergraph and the alternative hypothesis corresponds to a heterogeneous uniform random hypergraph that contains a dense subhypergraph. We establish detection boundaries when the edge probabilities are known and construct an asymptotically powerful test for distinguishing the hypotheses. We also construct an adaptive test which does not involve edge probabilities, and hence, is more practically useful.

我们研究了检验异质密集子超图存在性的问题。零假设对应于异质埃尔德斯-雷尼均匀随机超图，备择假设对应于包含密集子超图的异质均匀随机超图。当边缘概率已知时，我们建立了检测边界，并构建了一个渐近强大的检验来区分假设。我们还构建了一种自适应检验，它不涉及边缘概率，因此更实用。

引用次数: 0

General adapted‐threshold monitoring in discrete environments and rules for imbalanced classes 离散环境中的一般自适应阈值监测和不平衡类规则

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-08-22 DOI: 10.1111/stan.12352

Ansgar Steland, Ewaryst Rafajłowicz, Wojciech Rafajłowicz

Having in mind applications in statistics and machine learning such as individualized care monitoring, or watermark detection in large language models, we consider the following general setting: When monitoring a sequence of observations, , there may be additional information, , on the environment which should be used to design the monitoring procedure. This additional information can be incorporated by applying threshold functions to the standardized measurements to adapt the detector to the environment. For the case of categorical data encoding of discrete‐valued environmental information we study several classes of level threshold functions including a proportional one which favors rare events among imbalanced classes. For the latter rule asymptotic theory is developed for independent and identically distributed and dependent learning samples including data from new discrete autoregressive moving average model (NDARMA) series and Hidden Markov Models. Further, we propose two‐stage designs which allow to distribute in a controlled way the budget over an a priori partition of the sample space of . The approach is illustrated by a real medical data set.

考虑到统计学和机器学习中的应用，如个性化护理监控或大型语言模型中的水印检测，我们考虑了以下一般情况：在监控一系列观察结果时，可能会有关于环境的附加信息，这些信息应被用于设计监控程序。可以通过对标准化测量应用阈值函数来纳入这些附加信息，从而使检测器适应环境。对于离散值环境信息的分类数据编码情况，我们研究了几类水平阈值函数，包括在不平衡类别中偏好罕见事件的比例函数。对于后一种规则，我们开发了独立同分布和依赖学习样本的渐近理论，包括新离散自回归移动平均模型（NDARMA）序列和隐马尔可夫模型的数据。此外，我们还提出了两阶段设计方案，允许在样本空间的先验分区上以可控方式分配预算。我们通过一个真实的医疗数据集对该方法进行了说明。

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引用次数: 0

VC‐PCR: A prediction method based on variable selection and clustering VC-PCR：基于变量选择和聚类的预测方法

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-08-22 DOI: 10.1111/stan.12358

Rebecca Marion, Johannes Lederer, Bernadette Goevarts, Rainer von Sachs

Sparse linear prediction methods suffer from decreased prediction accuracy when the predictor variables have cluster structure (e.g., highly correlated groups of variables). To improve prediction accuracy, various methods have been proposed to identify variable clusters from the data and integrate cluster information into a sparse modeling process. But none of these methods achieve satisfactory performance for prediction, variable selection and variable clustering performed simultaneously. This paper presents Variable Cluster Principal Component Regression (VC‐PCR), a prediction method that uses variable selection and variable clustering in order to solve this problem. Experiments with real and simulated data demonstrate that, compared to competitor methods, VC‐PCR is the only method that achieves simultaneously good prediction, variable selection, and clustering performance when cluster structure is present.

当预测变量具有聚类结构（如高度相关的变量组）时，稀疏线性预测方法的预测精度就会下降。为了提高预测精度，人们提出了各种方法来从数据中识别变量聚类，并将聚类信息整合到稀疏建模过程中。但这些方法都无法同时实现令人满意的预测、变量选择和变量聚类效果。为了解决这个问题，本文提出了一种使用变量选择和变量聚类的预测方法--变量聚类主成分回归（VC-PCR）。使用真实数据和模拟数据进行的实验表明，与其他竞争方法相比，VC-PCR 是唯一一种在存在聚类结构的情况下同时实现良好预测、变量选择和聚类性能的方法。

引用次数: 0

Estimation of density functionals via cross‐validation 通过交叉验证估计密度函数

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-08-01 DOI: 10.1111/stan.12359

José E. Chacón, Carlos Tenreiro

In density estimation, the mean integrated squared error (MISE) is commonly used as a measure of performance. In that setting, the cross‐validation criterion provides an unbiased estimator of the MISE minus the integral of the squared density. Since the minimum MISE is known to converge to zero, this suggests that the minimum value of the cross‐validation criterion could be regarded as an estimator of minus the integrated squared density. This novel proposal presents the outstanding feature that, unlike all other existing estimators, it does not need the choice of any tuning parameter. Indeed, it is proved here that this approach results in a consistent and efficient estimator, with remarkable performance in practice. Moreover, apart from this base case, it is shown how several other problems on density functional estimation can be similarly handled using this new principle, thus demonstrating full potential for further applications.

在密度估计中，通常使用平均综合平方误差（MISE）来衡量性能。在这种情况下，交叉验证准则提供了 MISE 减去密度平方积分的无偏估计值。由于已知最小 MISE 趋于零，这表明交叉验证准则的最小值可被视为减去平方密度积分的估计值。与其他所有现有估计器不同的是，这一新颖的建议具有无需选择任何调整参数的突出特点。事实上，本文证明了这种方法能产生一致且高效的估计器，并在实践中表现出卓越的性能。此外，除了这个基本案例，本文还展示了如何利用这一新原理同样处理其他几个关于密度函数估计的问题，从而充分展示了进一步应用的潜力。

引用次数: 0

Artificial neural network small‐sample‐bias‐corrections of the AR(1) parameter close to unit root 人工神经网络对接近单位根的 AR(1) 参数进行小样本偏置校正

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-08-01 DOI: 10.1111/stan.12354

Haozhe Jiang, Ostap Okhrin, Michael Rockinger

This paper introduces an artificial neural network (ANN) approach to estimate the autoregressive process AR(1) when the autocorrelation parameter is near one. Traditional ordinary least squares (OLS) estimators suffer from biases in small samples, necessitating various correction methods proposed in the literature. The ANN, trained on simulated data, outperforms these methods due to its nonlinear structure. Unlike competitors requiring simulations for bias corrections based on specific sample sizes, the ANN directly incorporates sample size as input, eliminating the need for repeated simulations. Stability tests involve exploring different ANN architectures and activation functions and robustness to varying distributions of the process innovations. Empirical applications on financial and industrial data highlight significant differences among methods, with ANN estimates suggesting lower persistence than other approaches.

本文介绍了一种人工神经网络（ANN）方法，用于估计自相关参数接近 1 时的自回归过程 AR(1)。传统的普通最小二乘法（OLS）估计器在小样本时存在偏差，因此需要采用文献中提出的各种修正方法。在模拟数据基础上训练的方差网络因其非线性结构而优于这些方法。与需要根据特定样本大小进行模拟以纠正偏差的竞争对手不同，方差网络直接将样本大小作为输入，无需重复模拟。稳定性测试包括探索不同的 ANN 架构和激活函数，以及对过程创新的不同分布的稳健性。金融和工业数据的实证应用凸显了各种方法之间的显著差异，其中方差网络估算的持久性低于其他方法。

引用次数: 0

Inference for Kumaraswamy‐G family of distributions under unified progressive hybrid censoring with partially observed competing risks data 库马拉斯瓦米-G 系列分布在统一渐进混合删减法下的推断，以及部分观察到的竞争风险数据

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-07-25 DOI: 10.1111/stan.12357

Subhankar Dutta, Hon Keung Tony Ng, Suchandan Kayal

In this study, statistical inference for competing risks model with latent failure times following the Kumaraswamy‐G (Kw‐G) family of distributions under a unified progressive hybrid censoring (UPHC) scheme is developed. Maximum likelihood estimates (MLEs) of the unknown model parameters are obtained, and their existence and uniqueness properties are discussed. Using the asymptotic properties of MLEs, the approximate confidence intervals for the model parameters are constructed. Further, Bayes estimates with associated highest posterior density credible intervals for the model parameters are developed under squared error loss function with informative and noninformative priors. These estimates are obtained under both restricted and nonrestricted parameter spaces. Moreover, frequentist and Bayesian approaches are developed to test the equality of shape parameters of the two competing failure causes. The comparison of censoring schemes based on different criteria is also discussed. Monte Carlo simulation studies are used to evaluate the performance of the proposed statistical inference procedures. An electrical appliances data set is analyzed to illustrate the applicability of the proposed methodologies.

本研究在统一渐进混合删减（UPHC）方案下，建立了具有潜在失败时间的库马拉斯瓦米-G（Kw-G）族分布的竞争风险模型的统计推断。得到了未知模型参数的最大似然估计值（MLE），并讨论了它们的存在性和唯一性。利用 MLE 的渐近特性，构建了模型参数的近似置信区间。此外，在有信息和无信息先验的平方误差损失函数下，建立了模型参数的贝叶斯估计和相关最高后验密度可信区间。这些估计值是在受限和非受限参数空间下获得的。此外，还开发了频数法和贝叶斯法来检验两种相互竞争的故障原因的形状参数是否相等。此外，还讨论了基于不同标准的剔除方案的比较。蒙特卡罗模拟研究用于评估所提出的统计推断程序的性能。对一个电器数据集进行了分析，以说明建议方法的适用性。

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引用次数: 0

Computing optimal allocation of trials to subregions in crop‐variety testing in case of correlated genotype effects 在基因型效应相关的情况下，计算作物品种测试中试验在次区域的最佳分配方案

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-07-25 DOI: 10.1111/stan.12353

Maryna Prus

The subject of this work is the allocation of trials to subregions in crop variety testing in the case of correlated genotype effects. A solution for computation of optimal allocations using the OptimalDesign package in R is proposed. The obtained optimal designs minimize linear criteria based on the mean squared error matrix of the best linear unbiased prediction of the genotype effects. The proposed computational approach allows for any kind of additional linear constraint on the designs. The results are illustrated by a real data example.

这项工作的主题是在基因型效应相关的情况下，如何在作物品种测试中将试验分配到子区域。本文提出了一种使用 R 软件包 OptimalDesign 计算最优分配的解决方案。根据基因型效应的最佳线性无偏预测的均方误差矩阵，所获得的最优设计能最大限度地降低线性标准。所提出的计算方法允许对设计进行任何额外的线性约束。一个真实数据实例对结果进行了说明。

引用次数: 0

Degree distributions in networks: Beyond the power law 网络中的学位分布：超越幂律

IF 1.5 3区数学 Q2 STATISTICS & PROBABILITY

Statistica Neerlandica

Pub Date : 2024-07-23 DOI: 10.1111/stan.12355

Clement Lee, Emma F. Eastoe, Aiden Farrell

The power law is useful in describing count phenomena such as network degrees and word frequencies. With a single parameter, it captures the main feature that the frequencies are linear on the log‐log scale. Nevertheless, there have been criticisms of the power law, for example, that a threshold needs to be preselected without its uncertainty quantified, that the power law is simply inadequate, and that subsequent hypothesis tests are required to determine whether the data could have come from the power law. We propose a modeling framework that combines two different generalizations of the power law, namely the generalized Pareto distribution and the Zipf‐polylog distribution, to resolve these issues. The proposed mixture distributions are shown to fit the data well and quantify the threshold uncertainty in a natural way. A model selection step embedded in the Bayesian inference algorithm further answers the question whether the power law is adequate.

幂律适用于描述网络度和词频等计数现象。只需一个参数，它就能捕捉到频率在对数尺度上呈线性的主要特征。然而，也有人对幂律提出了批评，例如，需要预先选择一个阈值，而不对其不确定性进行量化；幂律根本不够充分；需要进行后续的假设检验来确定数据是否来自幂律。为了解决这些问题，我们提出了一个建模框架，将幂律的两种不同概括（即广义帕累托分布和 Zipf-Polylog 分布）结合起来。结果表明，所提出的混合分布能够很好地拟合数据，并以自然的方式量化阈值的不确定性。贝叶斯推理算法中的模型选择步骤进一步回答了幂律是否合适的问题。

引用次数: 0

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