Computational Statistics & Data Analysis SCIE
国际简称：COMPUT STAT DATA AN 参考译名：计算统计和数据分析
主要研究方向：数学-计算机：跨学科应用 非预警期刊 审稿周期： 较快,2-4周
《计算统计和数据分析》(Computational Statistics & Data Analysis)是一本由Elsevier出版的以数学-计算机：跨学科应用为研究特色的国际期刊，发表该领域相关的原创研究文章、评论文章和综述文章，及时报道该领域相关理论、实践和应用学科的最新发现，旨在促进该学科领域科学信息的快速交流。该期刊是一本未开放期刊，近三年没有被列入预警名单。
Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas:
I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article.
II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures.
[...]
III) Special Applications - [...]
IV) Annals of Statistical Data Science [...]
Computational Statistics & Data Analysis期刊信息
- ISSN：0167-9473
- 出版语言：English
- 是否OA：未开放
- E-ISSN：1872-7352
- 出版地区：NETHERLANDS
- 是否预警：否
- 出版商：Elsevier
- 出版周期：Monthly
- 创刊时间：1983
- 开源占比：0.0862
- Gold OA文章占比：25.33%
- OA被引用占比：0.0678...
- 出版国人文章占比：0.2
- 出版撤稿占比：0
- 研究类文章占比：100.00%
Computational Statistics & Data Analysis CiteScore评价数据（2024年最新版）
| CiteScore | SJR | SNIP | CiteScore 指数 | ||||||||||||||||||||
| 3.7 | 1.008 | 1.42 |
|
名词解释：CiteScore 是衡量期刊所发表文献的平均受引用次数，是在 Scopus 中衡量期刊影响力的另一个指标。当年CiteScore 的计算依据是期刊最近4年（含计算年度）的被引次数除以该期刊近四年发表的文献数。例如，2022年的 CiteScore 计算方法为：2022年的 CiteScore =2019-2022年收到的对2019-2022年发表的文件的引用数量÷2019-2022年发布的文献数量 注：文献类型包括：文章、评论、会议论文、书籍章节和数据论文。
Computational Statistics & Data Analysis中科院评价数据
中科院 2023年12月升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 数学 | 3区 | STATISTICS & PROBABILITY 统计学与概率论 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 | 3区 4区 |
中科院 2022年12月升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 数学 | 3区 | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 3区 3区 |
中科院 2021年12月旧的升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 数学 | 3区 | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 3区 3区 |
中科院 2021年12月基础版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 数学 | 3区 | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 4区 3区 |
中科院 2021年12月升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 数学 | 3区 | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 3区 3区 |
中科院 2020年12月旧的升级版
| Top期刊 | 综述期刊 | 大类学科 | 小类学科 | ||
| 否 | 否 | 数学 | 3区 | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机：跨学科应用 STATISTICS & PROBABILITY 统计学与概率论 | 3区 3区 |
Computational Statistics & Data Analysis JCR评价数据（2023-2024年最新版）
| 按JIF指标学科分区 | 收录子集 | 分区 | 排名 | 百分位 |
| 学科：COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | SCIE | Q3 | 126 / 169 |
25.7% |
| 学科：STATISTICS & PROBABILITY | SCIE | Q2 | 43 / 168 |
74.7% |
| 按JCI指标学科分区 | 收录子集 | 分区 | 排名 | 百分位 |
| 学科：COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | SCIE | Q3 | 86 / 169 |
49.41% |
| 学科：STATISTICS & PROBABILITY | SCIE | Q2 | 60 / 168 |
64.58% |
Computational Statistics & Data Analysis历年数据统计
影响因子
中科院分区
Computational Statistics & Data Analysis中国学者发文选摘
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1、Sparse spatially clustered coefficient model via adaptive regularization
Author: Zhong, Yan; Sang, Huiyan; Cook, Scott J.; Kellstedt, Paul M.
Journal: COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2023; Vol. 177, Issue , pp. -. DOI: 10.1016/j.csda.2022.107581
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2、Estimation for partial functional partially linear additive model
Author: Tang, Qingguo; Tu, Wei; Kong, Linglong
Journal: COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2023; Vol. 177, Issue , pp. -. DOI: 10.1016/j.csda.2022.107584
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3、Specification testing for ordinary differential equation models with fixed design and applications to COVID-19 epidemic models
Author: Liu, Ran; Zhu, Lixing
Journal: COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2023; Vol. 180, Issue , pp. -. DOI: 10.1016/j.csda.2022.107616
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4、Empirical Gittins index strategies with ?-explorations for multi-armed bandit problems
Author: Li, Xiao; Li, Yuqiang; Wu, Xianyi
Journal: COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2023; Vol. 180, Issue , pp. -. DOI: 10.1016/j.csda.2022.107610
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5、Generalized martingale difference divergence: Detecting conditional mean independence with applications in variable screening
Author: Li, Lu; Ke, Chenlu; Yin, Xiangrong; Yu, Zhou
Journal: COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2023; Vol. 180, Issue , pp. -. DOI: 10.1016/j.csda.2022.107618
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6、Applications on linear spectral statistics of high-dimensional sample covariance matrix with divergent spectrum
Author: Zhang, Yangchun; Zhou, Yirui; Liu, Xiaowei
Journal: COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2023; Vol. 178, Issue , pp. -. DOI: 10.1016/j.csda.2022.107617
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7、Optimal designs for semi-parametric dose-response models under random contamination
Author: Yu, Jun; Meng, Xiran; Wang, Yaping
Journal: COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2023; Vol. 178, Issue , pp. -. DOI: 10.1016/j.csda.2022.107615
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8、Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures
Author: Yang, Yuehan; Xia, Siwei; Yang, Hu
Journal: COMPUTATIONAL STATISTICS & DATA ANALYSIS. 2023; Vol. 178, Issue , pp. -. DOI: 10.1016/j.csda.2022.107620
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