Journal Title：Computational Statistics & Data Analysis

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.

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III) Special Applications - [...]

IV) Annals of Statistical Data Science [...]

Computational Statistics and Data Analysis (CSDA) 是网络计算和方法统计 (CMStatistics) 和国际统计计算协会 (IASC) 的官方出版物，是一本致力于传播方法研究和应用的国际期刊在计算统计和数据分析领域。该期刊由四个参考部分组成，分为以下主题领域：

I) 计算统计 - 涉及以下内容的手稿：1) 计算机对统计方法的显式影响（例如，贝叶斯计算、生物信息学、计算机图形学、计算机密集型推理方法、数据探索、数据挖掘、专家系统、启发式方法、知识基于系统、机器学习、神经网络、数值和优化方法、并行计算、统计数据库、统计系统），以及 2）统计软件和算法的开发、评估和验证。软件和算法可以随稿件提交，与在线文章一起存储。

II) 数据分析的统计方法 - 涉及生物统计学（临床试验、流行病学研究、统计遗传学或遗传/环境相互作用的设计和分析方法）、化学计量学、分类中应用的新颖和原始数据分析策略和方法的手稿、数据探索、密度估计、实验设计、环境计量学、教育、图像分析、营销、无模型数据探索、模式识别、心理测量学、统计物理学、图像处理、稳健程序。

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III) 特殊应用 - [...]

IV) 统计数据科学年鉴 [...]