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Japanese Journal of Statistics and Data Science最新文献

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Methodological challenges in studying disease processes using observational cohort data. 使用观察性队列数据研究疾病过程的方法学挑战。
IF 1.1 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2025-01-01 Epub Date: 2024-10-30 DOI: 10.1007/s42081-024-00276-9
Richard J Cook, Jerald F Lawless

Cohort studies of disease processes deal with events and other outcomes that may occur in individuals following disease onset. The particular goals are often the evaluation of interventions and estimation of the effects of risk factors that may affect the disease course. Models and methods of event history analysis and longitudinal data analysis provide tools for understanding disease processes, but there are numerous challenges in practice. These are related to the complexity of the disease processes and to the difficulty of recruiting representative individuals and acquiring detailed longitudinal data on their disease course. Our objectives here are to describe some of these challenges and to review methods of addressing them. We emphasize the appeal of multistate models as a framework for understanding both disease processes and the processes governing recruitment of individuals for cohort studies and the collection of data. The use of other observational data sources in order to enhance model fitting and analysis is discussed.

疾病过程的队列研究处理疾病发病后个体可能发生的事件和其他结果。具体目标通常是评估干预措施和估计可能影响疾病进程的风险因素的影响。事件历史分析和纵向数据分析的模型和方法为了解疾病过程提供了工具，但在实践中存在许多挑战。这与疾病过程的复杂性以及招募具有代表性的个体和获取其疾病过程的详细纵向数据的难度有关。我们在这里的目标是描述其中的一些挑战，并回顾解决这些挑战的方法。我们强调多状态模型作为理解疾病过程和管理队列研究和数据收集的个体招募过程的框架的吸引力。讨论了利用其他观测数据源来加强模型拟合和分析。
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引用次数: 0
Selection of statistics for a multinomial goodness-of-fit test and a test of independence for a multi-way contingency table when data are sparse 在数据稀少的情况下，选择多项式拟合优度检验和多向或然表独立性检验的统计量
IF 1.3 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2024-01-03 DOI: 10.1007/s42081-023-00233-y
Nobuhiro Taneichi, Yuri Sekiya
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引用次数: 0
Sparse inference of structural equation modeling with latent variables for diffusion processes 扩散过程潜变量结构方程模型的稀疏推理
IF 1.3 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2023-12-26 DOI: 10.1007/s42081-023-00230-1
Shogo Kusano, Masayuki Uchida
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引用次数: 0
Stein-rule M-estimation in sparse partially linear models 稀疏部分线性模型中的斯坦因规则 M-估计
IF 1.3 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2023-12-23 DOI: 10.1007/s42081-023-00231-0
E. Raheem, S. E. Ahmed, Shuangzhe Liu
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引用次数: 0
Expansion estimators improving the bias and risk of James–Stein’s shrinkage estimator 改进詹姆斯-斯坦缩减估算器偏差和风险的扩展估算器
IF 1.3 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2023-12-16 DOI: 10.1007/s42081-023-00227-w
Hisayuki Tsukuma
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引用次数: 0
Bivariate dynamic weighted cumulative residual entropy 双变量动态加权累积残差熵
IF 1.3 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2023-12-14 DOI: 10.1007/s42081-023-00232-z
R. Nair, E. I. A. Sathar
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引用次数: 0
Reliability of stress–strength model for exponentiated Teissier distribution based on lower record values 基于较低记录值的指数化泰西分布应力强度模型的可靠性
IF 1.3 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2023-12-09 DOI: 10.1007/s42081-023-00229-8
Hossein Pasha-Zanoosi
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引用次数: 0
Shrinkage estimation with logarithmic penalties 用对数惩罚进行收缩估计
IF 1.3 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2023-11-29 DOI: 10.1007/s42081-023-00225-y
Tatsuya Kubokawa
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引用次数: 0
Confidence interval for normal means in meta-analysis based on a pretest estimator 基于预测试估计值的荟萃分析中正态均值的置信区间
IF 1.3 Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2023-11-27 DOI: 10.1007/s42081-023-00221-2
Nanami Taketomi, Yuan-Tsung Chang, Yoshihiko Konno, Mihoko Mori, Takeshi Emura
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引用次数: 0
Multivariate functional subspace classification for high-dimensional longitudinal data 高维纵向数据的多元泛函子空间分类
Q3 STATISTICS & PROBABILITY
Japanese Journal of Statistics and Data Science
Pub Date : 2023-11-14 DOI: 10.1007/s42081-023-00226-x
Tatsuya Fukuda, Hidetoshi Matsui, Hiroya Takada, Toshihiro Misumi, Sadanori Konishi
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引用次数: 0
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