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Communications for Statistical Applications and Methods最新文献

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Modeling clustered count data with discrete weibull regression model 离散威布尔回归模型对聚类计数数据进行建模
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-07-31 DOI: 10.29220/csam.2022.29.4.413
Hanna Yoo
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引用次数: 0
Extension of the Mantel-Haenszel test to bivariate interval censored data Mantel-Haenszel检验在二元区间截尾数据中的推广
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-07-31 DOI: 10.29220/csam.2022.29.4.403
Dong-Hyun Leea, Yang-jin Kim
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引用次数: 0
A guideline for the statistical analysis of compositional data in immunology. 免疫学成分数据统计分析指南
IF 0.5 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-07-01 Epub Date: 2022-07-31 DOI: 10.29220/csam.2022.29.4.453
Jinkyung Yoo, Zequn Sun, Michael Greenacre, Qin Ma, Dongjun Chung, Young Min Kim

The study of immune cellular composition has been of great scientific interest in immunology because of the generation of multiple large-scale data. From the statistical point of view, such immune cellular data should be treated as compositional. In compositional data, each element is positive, and all the elements sum to a constant, which can be set to one in general. Standard statistical methods are not directly applicable for the analysis of compositional data because they do not appropriately handle correlations between the compositional elements. In this paper, we review statistical methods for compositional data analysis and illustrate them in the context of immunology. Specifically, we focus on regression analyses using log-ratio transformations and the alternative approach using Dirichlet regression analysis, discuss their theoretical foundations, and illustrate their applications with immune cellular fraction data generated from colorectal cancer patients.

由于产生了多个大规模数据，免疫细胞组成的研究在免疫学中引起了极大的科学兴趣。从统计学的角度来看，这种免疫细胞数据应该被视为成分数据。在组成数据中，每个元素都是正的，所有元素的总和为一个常数，通常可以设置为一。标准统计方法不直接适用于成分数据的分析，因为它们不能适当地处理成分元素之间的相关性。在本文中，我们回顾了成分数据分析的统计方法，并在免疫学的背景下对其进行了说明。具体而言，我们专注于使用对数变换和具有狄利克雷分布的广义线性模型进行回归分析，讨论其理论基础，并说明其在癌症结直肠癌患者免疫细胞分数数据中的应用。
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引用次数: 0
The skew-t censored regression model: parameter estimation via an EM-type algorithm 偏t截尾回归模型：基于EM型算法的参数估计
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-05-31 DOI: 10.29220/csam.2022.29.3.333
V. H. Lachos, J. Bazán, L. M. Castro, Jiwon Park
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引用次数: 0
A spatial heterogeneity mixed model with skew-elliptical distributions 具有偏斜椭圆分布的空间异质性混合模型
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-05-31 DOI: 10.29220/csam.2022.29.3.373
Mohadeseh Alsadat Farzammehr, G. McLachlan
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引用次数: 0
Stochastic structures of world's death counts after World War II 二战后世界死亡统计的随机结构
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-05-31 DOI: 10.29220/csam.2022.29.3.353
J. Lee
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引用次数: 0
Letter to the editor: Discussion of proposed t-statistic in "ppcor: An R Package for a fast calculation to semi-partial correlation coefficients," CSAM 2015; 22:665-674 致编辑的信：《ppcor:An R Package for a fast calculation to semi-partial correlation coefficients》中提出的t-统计量的讨论，CSAM 2015；22:665-674
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-05-31 DOI: 10.29220/csam.2022.29.3.393
A. Britto
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引用次数: 0
Estimating dose-response curves using splines: a nonparametric Bayesian knot selection method 使用样条估计剂量-响应曲线:一种非参数贝叶斯结选择方法
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-05-31 DOI: 10.29220/csam.2022.29.3.287
Jiwon Lee, Yongku Kim, Young Min Kim
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引用次数: 0
Modified information criterion for testing changes in generalized lambda distribution model based on confidence distribution 改进了基于置信度分布的广义lambda分布模型变化检验信息准则
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-05-31 DOI: 10.29220/csam.2022.29.3.301
Suthakaran Ratnasingama, Elena Buzaianub, Wei Ning
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引用次数: 0
Prediction of extreme PM2.5 concentrations via extreme quantile regression 极端分位数回归预测PM2.5极端浓度
IF 0.4 Q4 STATISTICS & PROBABILITY
Communications for Statistical Applications and Methods
Pub Date : 2022-05-31 DOI: 10.29220/csam.2022.29.3.319
Sanghyuk Lee, Seoncheol Park, Yaeji Lim
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引用次数: 0
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