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Journal of Statistical Theory and Practice最新文献

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Spatial Change of Support Models for Differentially Private Decennial Census Counts of Persons by Detailed Race and Ethnicity 按详细种族和民族划分的差异私人十年人口普查人口支持模型的空间变化
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-03-22 DOI: 10.1007/s42519-023-00328-5
R. Janicki, S. Holan, Kyle M. Irimata, James Livsey, Andrew M. Raim
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引用次数: 0
Parametric Analysis of Tampered Random Variable Model for Multiple Step-Stress Life Test 多阶应力寿命试验的篡改随机变量模型参数分析
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-03-03 DOI: 10.1007/s42519-022-00316-1
Tamalika Koley, Farha Sultana, A. Dewanji
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引用次数: 1
A Catalog of 2-Level Orthogonal Minimally Aliased Designs with Small Runs 具有小游程的二阶正交最小混叠设计目录
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-02-28 DOI: 10.1007/s42519-023-00321-y
Nam-Ky Nguyen, Tung-Dinh Pham, Mai Phuong Vuong
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引用次数: 0
Nonparametric Entropy Estimation of Conditional Distribution Under Length-Biased Right Censored Sample 长度偏右截尾样本下条件分布的非参数熵估计
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-02-27 DOI: 10.1007/s42519-023-00326-7
R. G., R. Rajesh, S. Sunoj
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引用次数: 0
Statistical Inference for the Jointly Adaptive Progressive Type-II Censored Weibull Distributions 联合自适应渐进式ii型截尾威布尔分布的统计推断
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-02-24 DOI: 10.1007/s42519-022-00318-z
Farha Sultana, Çaǧatay Çetinkaya, D. Kundu
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引用次数: 0
Wildlife Population Assessment: Changing Priorities Driven by Technological Advances 野生动物种群评估:技术进步推动的优先事项变化
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-02-02 DOI: 10.1007/s42519-023-00319-6
S. Buckland, D. Borchers, T. Marques, R. Fewster
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引用次数: 5
Analysis of Spatial Patterns and Associated Factors of Stillbirth in Pakistan, PDHS (2017–18): A Spatial and Multilevel Analysis 巴基斯坦死产的空间格局及其相关因素分析,PDHS(2017-18):空间和多层次分析
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-01-12 DOI: 10.1007/s42519-022-00308-1
Abeera Shakeel, Asif Kamal, G. Tesema, M. Siddiqa
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引用次数: 0
Optimality of Some Row–Column Designs 一些行列设计的最优性
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-01-10 DOI: 10.1007/s42519-022-00315-2
J. Morgan, S. Bagchi
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引用次数: 0
Semi-Lévy-Driven CARMA Process: Estimation and Prediction 半lsamv驱动的CARMA过程:估计与预测
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-01-06 DOI: 10.1007/s42519-022-00317-0
N. Modarresi, S. Rezakhah, M. Mohammadi
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引用次数: 0
An Algorithm of Nonparametric Quantile Regression. 一种非参数分位数回归算法。
IF 0.6 Q4 STATISTICS & PROBABILITY Pub Date : 2023-01-01 DOI: 10.1007/s42519-023-00325-8
Mei Ling Huang, Yansan Han, William Marshall

Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem. Regular linear quantile regression uses an L 1 loss function [Koenker in Quantile regression, Cambridge University Press, Cambridge, 2005], and the optimal solution of linear programming for estimating coefficients of regression. A problem with linear quantile regression is that the estimated curves for different quantiles can cross, a result that is logically inconsistent. To overcome the curves crossing problem, and to improve high quantile estimation in the nonlinear case, this paper proposes a nonparametric quantile regression method to estimate high conditional quantiles. A three-step computational algorithm is given, and the asymptotic properties of the proposed estimator are derived. Monte Carlo simulations show that the proposed method is more efficient than linear quantile regression method. Furthermore, this paper investigates COVID-19 and blood pressure real-world examples of extreme events by using the proposed method.

极端事件,如地震、海啸和市场崩溃,可以对社会和生态系统产生重大影响。分位数回归可以用于预测这些极端事件,使其成为一个在许多领域都有应用的重要问题。估计高条件分位数是一个难题。正则线性分位数回归使用1损失函数[Koenker in quantile regression, Cambridge University Press, Cambridge, 2005],并使用线性规划的最优解来估计回归系数。线性分位数回归的一个问题是,不同分位数的估计曲线可能交叉,结果在逻辑上是不一致的。为了克服曲线交叉问题,改进非线性情况下的高分位数估计,本文提出了一种非参数分位数回归方法来估计高条件分位数。给出了一个三步计算算法,并给出了该估计量的渐近性质。蒙特卡罗仿真结果表明,该方法比线性分位数回归方法更有效。此外,本文还利用该方法研究了COVID-19和血压极端事件的现实例子。
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引用次数: 3
期刊
Journal of Statistical Theory and Practice
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