Journal of Probability and Statistics最新文献

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A Comparison of Mean-Based and Quantile Regression Methods for Analyzing Self-Report Dietary Intake Data 基于均值回归和分位数回归分析自述饮食摄入数据的比较

IF 1.1

Journal of Probability and Statistics

Pub Date : 2019-03-03 DOI: 10.1155/2019/9750538

Michelle L Vidoni, B. Reininger, Minjae Lee

In mean-based approaches to dietary data analysis, it is possible for potentially important associations at the tails of the intake distribution, where inadequacy or excess is greatest, to be obscured due to unobserved heterogeneity. Participants in the upper or lower tails of dietary intake data will potentially have the greatest change in their behavior when presented with a health behavior intervention; thus, alternative statistical methods to modeling these relationships are needed to fully describe the impact of the intervention. Using data from Tu Salud ¡Si Cuenta! (Your Health Matters!) at Home Intervention, we aimed to compare traditional mean-based regression to quantile regression for describing the impact of a health behavior intervention on healthy and unhealthy eating indices. The mean-based regression model identified no differences in dietary intake between intervention and standard care groups. In contrast, the quantile regression indicated a nonconstant relationship between the unhealthy eating index and study groups at the upper tail of the unhealthy eating index distribution. The traditional mean-based linear regression was unable to fully describe the intervention effect on healthy and unhealthy eating, resulting in a limited understanding of the association.

在基于均值的饮食数据分析方法中，由于未观察到的异质性，摄入分布尾部的潜在重要关联可能会被掩盖，而摄入分布尾部的不足或过量是最大的。饮食摄入数据上尾或下尾的参与者在进行健康行为干预时，其行为可能发生最大的变化;因此，需要替代的统计方法来对这些关系进行建模，以充分描述干预的影响。使用Tu Salud ' Si Cuenta!(Your Health Matters!)，我们旨在比较传统的基于均值的回归和分位数回归，以描述健康行为干预对健康和不健康饮食指数的影响。基于均值的回归模型确定干预组和标准护理组之间的饮食摄入量没有差异。相比之下，分位数回归显示，在不健康饮食指数分布的上尾，不健康饮食指数与研究组之间存在非恒定关系。传统的基于均值的线性回归无法完全描述健康和不健康饮食的干预效果，导致对这种关联的理解有限。

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引用次数: 2

One-Sided and Two-Sided w-of-w Runs-Rules Schemes: An Overall Performance Perspective and the Unified Run-Length Derivations 单侧和双侧w-of-w运行规则方案：总体性能视角和统一的运行长度推导

IF 1.1

Journal of Probability and Statistics

Pub Date : 2019-02-19 DOI: 10.1155/2019/6187060

S. Shongwe, J. Malela‐Majika, E. Rapoo

The one-sided and two-sided Shewhart w-of-w standard and improved runs-rules monitoring schemes to monitor the mean of normally distributed observations from independent and identically distributed (iid) samples are investigated from an overall performance perspective, i.e., the expected weighted run-length (EWRL), for every possible positive integer value of w. The main objective of this work is to use the Markov chain methodology to formulate a theoretical unified approach of designing and evaluating Shewhart w-of-w standard and improved runs-rules for one-sided and two-sided X- schemes in both the zero-state and steady-state modes. Consequently, the main findings of this paper are as follows: (i) the zero-state and steady-state ARL and initial probability vectors of some of the one-sided and two-sided Shewhart w-of-w standard and improved runs-rules schemes are theoretically similar in design; however, their empirical performances are different and (ii) unlike previous studies that use ARL only, we base our recommendations using the zero-state and steady-state EWRL metrics and we observe that the steady-state improved runs-rules schemes tend to yield better performance than the other considered competing schemes, separately, for one-sided and two-sided schemes. Finally, the zero-state and steady-state unified approach run-length equations derived here can easily be used to evaluate other monitoring schemes based on a variety of parametric and nonparametric distributions.

从整体性能的角度，即w的每个可能的正整数值的预期加权游程长度（EWRL），研究了单侧和双侧Shewhart w-of-w标准和改进的游程规则监测方案，以监测来自独立和同分布（iid）样本的正态分布观测的平均值。这项工作的主要目标是使用马尔可夫链方法来制定一种设计和评估休哈特w-of-w标准的理论统一方法，以及在零态和稳态模式下单边和双边X-方案的改进运行规则。因此，本文的主要发现如下：（i）一些单侧和双侧Shewhart w-of-w标准和改进的运行规则方案的零态和稳态ARL以及初始概率向量在设计上是相似的；然而，它们的经验性能是不同的，（ii）与以前只使用ARL的研究不同，我们使用零状态和稳态EWRL度量来建立我们的建议，并且我们观察到，对于单边和双边方案，稳态改进的运行规则方案往往比其他考虑的竞争方案产生更好的性能。最后，本文导出的零状态和稳态统一方法游程方程可以很容易地用于评估基于各种参数和非参数分布的其他监测方案。

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引用次数: 15

A Nonuniform Bound to an Independent Test in High Dimensional Data Analysis via Stein’s Method 用Stein方法分析高维数据中独立检验的非均匀界

IF 1.1

Journal of Probability and Statistics

Pub Date : 2019-02-03 DOI: 10.1155/2019/8641870

N. Rerkruthairat

The Berry-Esseen bound for the random variable based on the sum of squared sample correlation coefficients and used to test the complete independence in high diemensions is shown by Stein’s method. Although the Berry-Esseen bound can be applied to all real numbers in R, a nonuniform bound at a real number z usually provides a sharper bound if z is fixed. In this paper, we present the first version of a nonuniform bound on a normal approximation for this random variable with an optimal rate of 1/0.5+|z|·O1/m by using Stein’s method.

Stein方法给出了基于样本相关系数平方和的随机变量的Berry-Essen界，该界用于检验高维中的完全独立性。尽管Berry-Esseen界可以应用于R中的所有实数，但如果z是固定的，则实数z处的非均匀界通常提供更尖锐的界。本文用Stein方法给出了最优速率为1/0.5+|z|·O1/m的随机变量在正态近似上的非均匀界的第一个版本。

引用次数: 1

The New Odd Log-Logistic Generalized Inverse Gaussian Regression Model 一种新的奇对数Logistic广义逆高斯回归模型

IF 1.1

Journal of Probability and Statistics

Pub Date : 2019-01-10 DOI: 10.1155/2019/8575424

Julio Cezar Souza Vasconcelos, G. Cordeiro, E. Ortega, E. G. Araújo

We define a new four-parameter model called the odd log-logistic generalized inverse Gaussian distribution which extends the generalized inverse Gaussian and inverse Gaussian distributions. We obtain some structural properties of the new distribution. We construct an extended regression model based on this distribution with two systematic structures, which can provide more realistic fits to real data than other special regression models. We adopt the method of maximum likelihood to estimate the model parameters. In addition, various simulations are performed for different parameter settings and sample sizes to check the accuracy of the maximum likelihood estimators. We provide a diagnostics analysis based on case-deletion and quantile residuals. Finally, the potentiality of the new regression model to predict price of urban property is illustrated by means of real data.

我们定义了一个新的四参数模型，称为奇对数逻辑广义逆高斯分布，它扩展了广义逆高斯和逆高斯分布。我们得到了新分布的一些结构性质。我们构建了一个基于这种分布的扩展回归模型，该模型具有两个系统结构，与其他特殊的回归模型相比，它可以提供更真实的真实数据拟合。我们采用最大似然法来估计模型参数。此外，针对不同的参数设置和样本大小进行各种模拟，以检查最大似然估计器的准确性。我们提供了基于病例删除和分位数残差的诊断分析。最后，通过实际数据说明了新回归模型预测城市房地产价格的潜力。

引用次数: 11

Atrial Fibrillation Detection by the Combination of Recurrence Complex Network and Convolution Neural Network 递归复杂网络与卷积神经网络联合检测心房颤动

IF 1.1

Journal of Probability and Statistics

Pub Date : 2019-01-03 DOI: 10.1155/2019/8057820

Xiaoling Wei, Jimin Li, Chenghao Zhang, Ming Liu, Peng Xiong, Xin-Pan Yuan, Yifei Li, Feng Lin, Xiuling Liu

In this paper, R wave peak interval independent atrial fibrillation detection algorithm is proposed based on the analysis of the synchronization feature of the electrocardiogram signal by a deep neural network. Firstly, the synchronization feature of each heartbeat of the electrocardiogram signal is constructed by a Recurrence Complex Network. Then, a convolution neural network is used to detect atrial fibrillation by analyzing the eigenvalues of the Recurrence Complex Network. Finally, a voting algorithm is developed to improve the performance of the beat-wise atrial fibrillation detection. The MIT-BIH atrial fibrillation database is used to evaluate the performance of the proposed method. Experimental results show that the sensitivity, specificity, and accuracy of the algorithm can achieve 94.28%, 94.91%, and 94.59%, respectively. Remarkably, the proposed method was more effective than the traditional algorithms to the problem of individual variation in the atrial fibrillation detection.

本文在利用深度神经网络分析心电图信号同步特征的基础上，提出了一种与R波峰间隔无关的心房颤动检测算法。首先，通过递归复杂网络构造心电图信号的每个心跳的同步特征。然后，通过分析递归复杂网络的特征值，使用卷积神经网络来检测心房颤动。最后，开发了一种投票算法来提高逐拍心房颤动检测的性能。MIT-BIH心房颤动数据库用于评估所提出方法的性能。实验结果表明，该算法的灵敏度、特异性和准确性分别达到94.28%、94.91%和94.59%。值得注意的是，对于心房颤动检测中的个体变异问题，该方法比传统算法更有效。

引用次数: 15

On the use of min-max combination of biomarkers to maximize the partial area under the ROC curve. 关于使用最小-最大生物标记物组合来最大化ROC曲线下的部分面积。

IF 1.1

Journal of Probability and Statistics

Pub Date : 2019-01-01 Epub Date: 2019-02-03 DOI: 10.1155/2019/8953530

Hua Ma, Susan Halabi, Aiyi Liu

Background: Evaluation of diagnostic assays and predictive performance of biomarkers based on the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are vital in diagnostic and targeted medicine. The partial area under the curve (pAUC) is an alternative metric focusing on a range of practical and clinical relevance of the diagnostic assay. In this article, we adopt and extend the min-max method to the estimation of the pAUC when multiple continuous scaled biomarkers are available and compare the performances of our proposed approach with existing approaches via simulations.

Methods: We conducted extensive simulation studies to investigate the performance of different methods for the combination of biomarkers based on their abilities to produce the largest pAUC estimates. Data were generated from different multivariate distributions with equal and unequal variance-covariance matrices. Different shapes of the ROC curves, false positive fraction ranges, and sample size configurations were considered. We obtained the mean and standard deviation of the pAUC estimates through re-substitution and leave-one-pair-out cross validation.

Results: Our results demonstrate that the proposed method provides the largest pAUC estimates under the following three important practical scenarios: (1) multivariate normally distributed data for non-diseased and diseased participants have unequal variance-covariance matrices; or (2) the ROC curves generated from individual biomarker are relative close regardless of the latent normality distributional assumption; or (3) the ROC curves generated from individual biomarker have straight-line shapes.

Conclusions: The proposed method is robust and investigators are encouraged to use this approach in the estimation of the pAUC for many practical scenarios.

背景:基于受试者工作特征(ROC)曲线和ROC曲线下面积(AUC)评估诊断分析和生物标志物的预测性能在诊断和靶向医学中至关重要。曲线下部分面积(pAUC)是另一种度量，侧重于诊断分析的一系列实际和临床相关性。在本文中，我们采用并扩展了最小-最大方法来估计多个连续缩放的生物标记物时的pac，并通过仿真比较了我们提出的方法与现有方法的性能。方法:我们进行了广泛的模拟研究，根据生物标志物组合的不同方法产生最大pac估计值的能力来研究它们的性能。数据来自方差-协方差矩阵相等和不相等的不同多元分布。考虑了不同形状的ROC曲线、假阳性分数范围和样本量配置。我们通过重新替换和留下一对的交叉验证得到了pac估计的均值和标准差。结果表明，本文提出的方法在以下三种重要的实际情况下提供了最大的pac估计:(1)非患病和患病参与者的多元正态分布数据具有不等的方差-协方差矩阵;或(2)无论潜在正态分布假设如何，个体生物标志物生成的ROC曲线都相对接近;(3)个体生物标志物生成的ROC曲线呈直线形状。结论:所提出的方法是稳健的，鼓励研究者在许多实际情况下使用这种方法来估计pac。

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引用次数: 4

On the Alpha Power Transformed Power Lindley Distribution 关于Alpha幂变换的幂Lindley分布

IF 1.1

Journal of Probability and Statistics

Pub Date : 2019-01-01 DOI: 10.1155/2019/8024769

A. Hassan, M. Elgarhy, Rokaya E. Mohamd, Sharifah Alrajhi

In this paper, we introduce a new generalization of the power Lindley distribution referred to as the alpha power transformed power Lindley (APTPL). The APTPL model provides a better fit than the power Lindley distribution. It includes the alpha power transformed Lindley, power Lindley, Lindley, and gamma as special submodels. Various properties of the APTPL distribution including moments, incomplete moments, quantiles, entropy, and stochastic ordering are obtained. Maximum likelihood, maximum products of spacings, and ordinary and weighted least squares methods of estimation are utilized to obtain the estimators of the population parameters. Extensive numerical simulation is performed to examine and compare the performance of different estimates. Two important data sets are employed to show how the proposed model works in practice.

在本文中，我们引入了功率Lindley分布的一个新的推广，称为α功率变换功率Lindley（APTPL）。APTPL模型提供了比功率Lindley分布更好的拟合。它包括阿尔法幂变换的Lindley、幂Lindley、Lindley和gamma作为特殊子模型。得到了APTPL分布的各种性质，包括矩、不完全矩、分位数、熵和随机排序。利用最大似然、空间的最大乘积以及普通和加权最小二乘估计方法来获得总体参数的估计量。进行了广泛的数值模拟，以检查和比较不同估计的性能。使用两个重要的数据集来显示所提出的模型在实践中是如何工作的。

引用次数: 31

Retirement Consumption Puzzle in Malaysia: Evidence from Bayesian Quantile Regression Model 马来西亚的退休消费难题：来自贝叶斯分位数回归模型的证据

IF 1.1

Journal of Probability and Statistics

Pub Date : 2019-01-01 DOI: 10.1155/2019/2723069

R. I. Alaudin, N. Ismail, Z. Isa

The objective of this study is to use the Bayesian quantile regression for studying the retirement consumption puzzle, which is defined as the drop in consumption upon retirement, using the cross-sectional data of the Malaysian Household Expenditure Survey (HES) 2009/2010. Three different measures of consumption, namely, total expenditure, work-related expenditure, and nonwork-related expenditure, are suggested for studying the retirement consumption puzzle. The results show that the drop in consumption upon retirement is significant and has a regressive distributional effect as indicated by larger drops at lower percentiles and smaller drops at higher percentiles. The smaller drops among higher consumption retirees (or higher income retirees) may imply that they have more savings and/or retirement benefits than the smaller consumption retirees (or lower income retirees). Comparison between the three types of consumption shows that the work-related expenditure has a uniform drop across the distribution. The drop under the nonwork-related expenditure varies across the distribution, implying that it is the source behind the variation of the consumption drop.

本研究的目的是利用马来西亚家庭支出调查（HES）2009/2010年的横断面数据，使用贝叶斯分位数回归来研究退休消费难题，即退休后消费的下降。提出了三种不同的消费指标，即总支出、与工作相关的支出和与工作无关的支出，以研究退休消费难题。结果表明，退休后消费的下降是显著的，并具有回归分布效应，表现为较低百分比的下降幅度较大，较高百分比的下降程度较小。高消费退休人员（或高收入退休人员）的降幅较小，这可能意味着他们比低消费退休人员有更多的储蓄和/或退休福利。三种消费类型的比较表明，与工作相关的支出在整个分布中都有均匀的下降。非工作相关支出项下的下降因分布而异，这意味着它是消费下降变化背后的来源。

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引用次数: 0

A Note on the Adaptive LASSO for Zero-Inflated Poisson Regression 关于零膨胀Poisson回归的自适应LASSO的一个注记

IF 1.1

Journal of Probability and Statistics

Pub Date : 2018-12-30 DOI: 10.1155/2018/2834183

Prithish Banerjee, Broti Garai, Himel Mallick, PhD, FASA, S. Chowdhury, Saptarshi Chatterjee

We consider the problem of modelling count data with excess zeros using Zero-Inflated Poisson (ZIP) regression. Recently, various regularization methods have been developed for variable selection in ZIP models. Among these, EM LASSO is a popular method for simultaneous variable selection and parameter estimation. However, EM LASSO suffers from estimation inefficiency and selection inconsistency. To remedy these problems, we propose a set of EM adaptive LASSO methods using a variety of data-adaptive weights. We show theoretically that the new methods are able to identify the true model consistently, and the resulting estimators can be as efficient as oracle. The methods are further evaluated through extensive synthetic experiments and applied to a German health care demand dataset.

我们考虑使用零膨胀泊松（ZIP）回归对具有多余零的计数数据进行建模的问题。最近，已经开发了各种正则化方法用于ZIP模型中的变量选择。其中，EM LASSO是一种流行的同时进行变量选择和参数估计的方法。然而，EM LASSO存在估计效率低和选择不一致的问题。为了解决这些问题，我们提出了一组使用各种数据自适应权重的EM自适应LASSO方法。我们从理论上证明了新方法能够一致地识别真实模型，并且由此产生的估计量可以像oracle一样高效。通过广泛的合成实验对这些方法进行了进一步评估，并将其应用于德国医疗保健需求数据集。

引用次数: 6

Detecting Spatial Clusters via a Mixture of Dirichlet Processes 利用Dirichlet过程的混合检测空间聚类

IF 1.1

Journal of Probability and Statistics

Pub Date : 2018-12-18 DOI: 10.1155/2018/3506794

M. Ray, Jian Kang, Hongmei Zhang

We proposed an approach that has the ability to detect spatial clusters with skewed or irregular distributions. A mixture of Dirichlet processes (DP) was used to describe spatial distribution patterns. The effects of different batches of data collection efforts were also modeled with a Dirichlet process. To cluster spatial foci, a birth-death process was applied due to its advantage of easier jumping between different numbers of clusters. Inferences of parameters including clustering were drawn under a Bayesian framework. Simulations were used to demonstrate and assess the method. We applied the method to an fMRI meta-analysis dataset to identify clusters of foci corresponding to different emotions.

我们提出了一种能够检测具有偏斜或不规则分布的空间聚类的方法。混合狄利克雷过程（DP）被用来描述空间分布模式。不同批次数据收集工作的效果也用狄利克雷过程建模。为了对空间焦点进行聚类，应用了出生-死亡过程，因为它更容易在不同数量的聚类之间跳跃。包括聚类在内的参数推断是在贝叶斯框架下得出的。使用模拟来演示和评估该方法。我们将该方法应用于fMRI荟萃分析数据集，以识别与不同情绪相对应的病灶簇。

引用次数: 1

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