Journal of Statistical Planning and Inference最新文献

英文中文

Marginally constrained nonparametric Bayesian inference through Gaussian processes 基于高斯过程的边际约束非参数贝叶斯推理

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-12-30 DOI: 10.1016/j.jspi.2024.106261

Bingjing Tang , Vinayak Rao

Nonparametric Bayesian models are used routinely as flexible and powerful models of complex data. In many situations, an applied scientist may have additional informative beliefs about the data distribution of interest, for instance, the distribution of its mean or a subset components. This often will not be compatible with the nonparametric prior. An important challenge is then to incorporate this partial prior belief into nonparametric Bayesian models. In this paper, we are motivated by settings where practitioners have additional distributional information about a subset of the coordinates of the observations being modeled. Our approach links this problem to that of conditional density modeling. Our main idea is a novel constrained Bayesian model, based on a perturbation of a parametric distribution with a transformed Gaussian process prior on the perturbation function. We develop a corresponding posterior sampling method based on data augmentation. We illustrate the efficacy of our proposed constrained nonparametric Bayesian model in a variety of real-world scenarios including modeling environmental and earthquake data.

非参数贝叶斯模型通常被用作复杂数据的灵活而强大的模型。在许多情况下，应用科学家可能对感兴趣的数据分布有额外的信息信念，例如，其平均值或子集组件的分布。这通常与非参数先验不兼容。然后，一个重要的挑战是将这种部分先验信念纳入非参数贝叶斯模型。在本文中，我们的动机来自于这样的设置，即实践者拥有关于正在建模的观测坐标子集的额外分布信息。我们的方法将这个问题与条件密度建模的问题联系起来。我们的主要思想是一种新的约束贝叶斯模型，它基于参数分布的扰动，在扰动函数上有一个转换的高斯过程。提出了一种基于数据增强的后验抽样方法。我们说明了我们提出的约束非参数贝叶斯模型在各种现实世界场景中的有效性，包括建模环境和地震数据。

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引用次数: 0

Deterministic construction methods for asymmetrical uniform designs 不对称均匀设计的确定性构造方法

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-12-28 DOI: 10.1016/j.jspi.2024.106262

Liuping Hu , Kashinath Chatterjee , Jianhui Ning , Hong Qin

Asymmetrical (mixed-level) uniform designs are useful for both computer and physical experiments. However, constructing these designs is often challenging due to their complex asymmetrical structure. In this paper, we propose novel methods for constructing uniform designs with mixed two-, three-, and four/nine-levels. Our construction methods are deterministic, allowing us to circumvent the complexity associated with stochastic algorithms. We evaluate uniformity using the wrap-around

L_{2}

- and Lee discrepancies. We establish useful analytic relationships between uniformity and aberration, and derive new general lower bounds for discrepancies that are tighter than those currently available in the literature. These new benchmarks can effectively measure the uniformity of asymmetrical designs. Additionally, we provide examples demonstrating the efficacy of our construction methods and the relevance of the newly obtained lower bounds. Finally, through simulations, we show that the designs produced using our methods perform well in constructing statistical surrogate models.

不对称（混合水平）均匀设计对计算机和物理实验都很有用。然而，由于其复杂的不对称结构，构建这些设计通常具有挑战性。在本文中，我们提出了一种新的方法来构建混合二、三、四/九层的均匀设计。我们的构造方法是确定性的，允许我们规避与随机算法相关的复杂性。我们使用环绕L2-和Lee差异来评估均匀性。我们在均匀性和像差之间建立了有用的分析关系，并推导出比目前文献中可用的更严格的差异的新一般下界。这些新的基准可以有效地测量非对称设计的均匀性。此外，我们还提供了一些例子来证明我们的构造方法的有效性和新获得的下界的相关性。最后，通过仿真，我们表明使用我们的方法产生的设计在构建统计代理模型方面表现良好。

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引用次数: 0

Maximum likelihood estimation of short panel autoregressive models with flexible form of fixed effects 具有灵活形式固定效应的短面板自回归模型的最大似然估计

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-12-18 DOI: 10.1016/j.jspi.2024.106252

Kazuhiko Hayakawa, Boyan Yin

This paper proposes the maximum likelihood (ML) estimator for a short panel autoregressive model with a flexible form of observed factors as well as unknown interactive fixed effects. We show that the ML estimator is consistent and asymptotically normally distributed as the number of cross-sectional units increases with the number of time periods being fixed. It should be noted that this asymptotic result holds uniformly for the autoregressive coefficient less than, equal to, or greater than one, in sharp contrast to existing estimators. Monte Carlo simulation results show that the ML estimator has desirable finite sample properties.

本文提出了具有柔性观测因子形式和未知交互固定效应的短面板自回归模型的最大似然估计量。我们证明了ML估计量是一致的，并且是渐近正态分布的，因为截面单元的数量随着时间段的数量固定而增加。应该注意的是，对于小于、等于或大于1的自回归系数，这个渐近结果一致成立，与现有的估计量形成鲜明对比。蒙特卡罗仿真结果表明，该估计器具有良好的有限样本特性。

引用次数: 0

Outcome dependent subsampling divide and conquer in generalized linear models for massive data 海量数据广义线性模型的结果依赖子抽样分治方法

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-12-04 DOI: 10.1016/j.jspi.2024.106253

Jie Yin , Jieli Ding , Changming Yang

In order to break the constraints and barriers caused by limited computing power in processing massive datasets, we propose an outcome dependent subsampling divide and conquer strategy in this paper. The proposed strategy can process data on multiple blocks in parallel and concentrate the computing resources of each block on regions with the most information. We develop a distributed statistical inference method and propose a computation-efficient algorithm in the generalized linear models for massive data. The proposed method only need to preserve some summary statistics from each data block and then use them to directly construct the proposed estimator. The asymptotic properties of the proposed method are established. Simulation studies and real data analysis are conducted to illustrate the merits of the proposed method.

为了打破计算能力有限对海量数据集处理的限制和障碍，本文提出了一种结果依赖的子抽样分治策略。该策略可以并行处理多个块上的数据，并将每个块的计算资源集中在信息最多的区域上。本文提出了一种分布式统计推理方法，并在海量数据的广义线性模型中提出了一种计算效率高的算法。该方法只需要从每个数据块中保留一些汇总统计信息，然后使用它们直接构造所提出的估计器。建立了该方法的渐近性。仿真研究和实际数据分析表明了该方法的优越性。

引用次数: 0

Nonparametric estimators of inequality curves and inequality measures 不等式曲线的非参数估计和不等式测度

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-11-28 DOI: 10.1016/j.jspi.2024.106251

Alicja Jokiel-Rokita, Sylwester Pia̧tek

Classical inequality curves and inequality measures are defined for distributions with finite mean value. Moreover, their empirical counterparts are not resistant to outliers. For these reasons, quantile versions of known inequality curves such as the Lorenz, Bonferroni, Zenga and

D

curves, and quantile versions of inequality measures such as the Gini, Bonferroni, Zenga and

D

indices have been proposed in the literature. We propose various nonparametric estimators of quantile versions of inequality curves and inequality measures, prove their consistency, and compare their accuracy in a simulation study. We also give examples of the use of quantile versions of inequality measures in real data analysis.

经典的不等式曲线和不等式测度是针对有限均值分布定义的。此外，他们的经验对应物对异常值没有抵抗力。由于这些原因，文献中已经提出了已知不平等曲线的分位数版本，如Lorenz， Bonferroni， Zenga和D曲线，以及不平等测量的分位数版本，如基尼指数，Bonferroni指数，Zenga指数和D指数。我们提出了不等式曲线和不等式测度的分位数版本的各种非参数估计，证明了它们的一致性，并在模拟研究中比较了它们的准确性。我们还给出了在实际数据分析中使用分位数版本的不平等度量的例子。

引用次数: 0

Estimation and group-feature selection in sparse mixture-of-experts with diverging number of parameters 参数数量分散的稀疏专家混合物中的估计和组特征选择

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-11-19 DOI: 10.1016/j.jspi.2024.106250

Abbas Khalili , Archer Yi Yang , Xiaonan Da

Mixture-of-experts provide flexible statistical models for a wide range of regression (supervised learning) problems. Often a large number of covariates (features) are available in many modern applications yet only a small subset of them is useful in explaining a response variable of interest. This calls for a feature selection device. In this paper, we present new group-feature selection and estimation methods for sparse mixture-of-experts models when the number of features can be nearly comparable to the sample size. We prove the consistency of the methods in both parameter estimation and feature selection. We implement the methods using a modified EM algorithm combined with proximal gradient method which results in a convenient closed-form parameter update in the M-step of the algorithm. We examine the finite-sample performance of the methods through simulations, and demonstrate their applications in a real data example on exploring relationships in body measurements.

专家混合模型为各种回归（监督学习）问题提供了灵活的统计模型。在许多现代应用中，往往会有大量的协变量（特征），但其中只有一小部分对解释感兴趣的响应变量有用。这就需要一种特征选择装置。在本文中，我们针对稀疏专家混合物模型提出了新的分组特征选择和估计方法，当特征数量几乎与样本大小相当时，就可以使用这种方法。我们证明了这些方法在参数估计和特征选择方面的一致性。我们使用改进的 EM 算法结合近似梯度法来实现这些方法，从而在算法的 M 步中方便地进行闭式参数更新。我们通过仿真检验了这些方法的有限样本性能，并在一个探索人体测量关系的真实数据示例中演示了这些方法的应用。

引用次数: 0

Modeling and testing for endpoint-inflated count time series with bounded support 有界支持端点膨胀计数时间序列的建模与测试

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-11-15 DOI: 10.1016/j.jspi.2024.106248

Yao Kang , Xiaojing Fan , Jie Zhang , Ying Tang

Count time series with bounded support frequently exhibit binomial overdispersion, zero inflation and right-endpoint inflation in practical scenarios. Numerous models have been proposed for the analysis of bounded count time series with binomial overdispersion and zero inflation, yet right-endpoint inflation has received comparatively less attention. To better capture these features, this article introduces three versions of extended first-order binomial autoregressive (BAR(1)) models with endpoint inflation. Corresponding stochastic properties of the new models are investigated and model parameters are estimated by the conditional maximum likelihood and quasi-maximum likelihood methods. A binomial right-endpoint inflation index is also constructed and further used to test whether the data set has endpoint-inflated characteristic with respect to a BAR(1) process. Finally, the proposed models are applied to two real data examples. Firstly, we illustrate the usefulness of the proposed models through an application to the voting data on supporting interest rate changes during consecutive monthly meetings of the Monetary Policy Council at the National Bank of Poland. Then, we apply the proposed models to the number of police stations that received at least one drunk driving report per month. The results of the two real data examples indicate that the new models have significant advantages in terms of fitting performance for the bounded count time series with endpoint inflation.

具有有界支持的计数时间序列在实际场景中经常表现为二项过分散、零膨胀和右端点膨胀。对于具有二项过分散和零膨胀的有界计数时间序列的分析，已经提出了许多模型，但右端点膨胀受到的关注相对较少。为了更好地捕捉这些特征，本文介绍了具有端点膨胀的扩展一阶二项自回归（BAR(1)）模型的三个版本。研究了新模型的随机性质，并利用条件极大似然和拟极大似然方法估计了模型参数。还构造了一个二项式右端点膨胀指数，并进一步用于测试数据集相对于BAR(1)过程是否具有端点膨胀特征。最后，将所提出的模型应用于两个实际数据实例。首先，我们通过对波兰国家银行货币政策委员会连续每月会议期间支持利率变化的投票数据的应用来说明所提出模型的实用性。然后，我们将所提出的模型应用于每月至少收到一份酒驾报告的警察局数量。两个实际数据示例的结果表明，新模型在具有端点膨胀的有界计数时间序列的拟合性能方面具有显著的优势。

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引用次数: 0

Semi-parametric empirical likelihood inference on quantile difference between two samples with length-biased and right-censored data 利用长度偏差和右删失数据对两个样本之间的量差进行半参数经验似然推断

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-11-14 DOI: 10.1016/j.jspi.2024.106249

Li Xun , Xin Guan , Yong Zhou

Exploring quantile differences between two populations at various probability levels offers valuable insights into their distinctions, which are essential for practical applications such as assessing treatment effects. However, estimating these differences can be challenging due to the complex data often encountered in clinical trials. This paper assumes that right-censored data and length-biased right-censored data originate from two populations of interest. We propose an adjusted smoothed empirical likelihood (EL) method for inferring quantile differences and establish the asymptotic properties of the proposed estimators. Under mild conditions, we demonstrate that the adjusted log-EL ratio statistics asymptotically follow the standard chi-squared distribution. We construct confidence intervals for the quantile differences using both normal and chi-squared approximations and develop a likelihood ratio test for these differences. The performance of our proposed methods is illustrated through simulation studies. Finally, we present a case study utilizing Oscar award nomination data to demonstrate the application of our method.

探索两个人群在不同概率水平上的量纲差异，可以深入了解它们之间的区别，这对评估治疗效果等实际应用至关重要。然而，由于临床试验中经常遇到复杂的数据，估计这些差异可能具有挑战性。本文假设右删失数据和长度偏倚右删失数据来自两个相关人群。我们提出了一种用于推断量纲差异的调整平滑经验似然法（EL），并建立了所提估计值的渐近特性。在温和条件下，我们证明了调整后的对数-EL 比率统计量渐近遵循标准的卡方分布。我们使用正态和卡方近似值构建了量纲差异的置信区间，并开发了针对这些差异的似然比检验。我们通过模拟研究说明了所提方法的性能。最后，我们利用奥斯卡奖提名数据进行了案例研究，展示了我们方法的应用。

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引用次数: 0

Sieve estimation of the accelerated mean model based on panel count data 基于面板计数数据的加速平均模型的筛分估计

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-11-12 DOI: 10.1016/j.jspi.2024.106247

Xiaoyang Li , Zhi-Sheng Ye , Xingqiu Zhao

Panel count data are gathered when subjects are examined at discrete times during a study, and only the number of recurrent events occurring before each examination time is recorded. We consider a semiparametric accelerated mean model for panel count data in which the effect of the covariates is to transform the time scale of the baseline mean function. Semiparametric inference for the model is inherently challenging because the finite-dimensional regression parameters appear in the argument of the (infinite-dimensional) functional parameter, i.e., the baseline mean function, leading to the phenomenon of bundled parameters. We propose sieve pseudolikelihood and likelihood methods to construct the random criterion function for estimating the model parameters. An inexact block coordinate ascent algorithm is used to obtain these estimators. We establish the consistency and rate of convergence of the proposed estimators, as well as the asymptotic normality of the estimators of the regression parameters. Novel consistent estimators of the asymptotic covariances of the estimated regression parameters are derived by leveraging the counting process associated with the examination times. Comprehensive simulation studies demonstrate that the optimization algorithm is much less sensitive to the initial values than the Newton–Raphson method. The proposed estimators perform well for practical sample sizes, and are more efficient than existing methods. An example based on real data shows that due to this efficiency gain, the proposed method is better able to detect the significance of practically meaningful covariates than an existing method.

面板计数数据是在研究过程中对受试者进行离散时间检查时收集的数据，只记录每次检查时间之前发生的重复事件的数量。我们考虑了面板计数数据的半参数加速均值模型，其中协变量的作用是转换基线均值函数的时间尺度。由于有限维回归参数出现在（无限维）函数参数（即基线均值函数）的参数中，导致了捆绑参数现象，因此该模型的半参数推断本身就具有挑战性。我们提出了筛分伪似然法和似然法，以构建估计模型参数的随机准则函数。我们使用非精确块坐标上升算法来获得这些估计值。我们确定了所提出的估计值的一致性和收敛率，以及回归参数估计值的渐近正态性。通过利用与考试时间相关的计数过程，我们得出了估计回归参数渐近协方差的新一致估计值。综合模拟研究表明，优化算法对初始值的敏感度远低于牛顿-拉斐森方法。所提出的估计方法在实际样本量中表现良好，比现有方法更有效。一个基于真实数据的例子表明，由于效率的提高，所提出的方法比现有方法更能检测出具有实际意义的协变量的重要性。

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引用次数: 0

The proximal bootstrap for constrained estimators 受约束估计器的近似自举法

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Statistical Planning and Inference

Pub Date : 2024-10-28 DOI: 10.1016/j.jspi.2024.106245

Jessie Li

We demonstrate how to conduct uniformly asymptotically valid inference for

\sqrt{n}

-consistent estimators defined as the solution to a constrained optimization problem with a possibly nonsmooth or nonconvex sample objective function and a possibly nonconvex constraint set. We allow for the solution to the problem to be on the boundary of the constraint set or to drift towards the boundary of the constraint set as the sample size goes to infinity. We construct a confidence set by benchmarking a test statistic against critical values that can be obtained from a simple unconstrained quadratic programming problem. Monte Carlo simulations illustrate the uniformly correct coverage of our method in a boundary constrained maximum likelihood model, a boundary constrained nonsmooth GMM model, and a conditional logit model with capacity constraints.

我们演示了如何对 n 个一致估计器进行统一渐近有效推断，这些估计器被定义为一个约束优化问题的解，该问题具有可能是非光滑或非凸的样本目标函数和可能是非凸的约束集。我们允许问题的解处于约束集的边界上，或随着样本量的增加而向约束集的边界漂移。我们通过将测试统计量与临界值进行比对来构建置信集，这些临界值可以从一个简单的无约束二次编程问题中获得。蒙特卡罗模拟说明了我们的方法在边界约束最大似然模型、边界约束非光滑 GMM 模型和带容量约束的条件 logit 模型中的均匀正确覆盖率。

引用次数: 0

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