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 three 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.Statistical methodology includes, but not limited to: bootstrapping, classification techniques, clinical trials, data exploration, density estimation, design of experiments, pattern recognition/image analysis, parametric and nonparametric methods, statistical genetics, Bayesian modeling, outlier detection, robust procedures, cross-validation, functional data, fuzzy statistical analysis, mixture models, model selection and assessment, nonlinear models, partial least squares, latent variable models, structural equation models, supervised learning, signal extraction and filtering, time-series modelling, longitudinal analysis, multilevel analysis and quality control.III) Special Applications - Manuscripts at the interface of statistics and computing (e.g., comparison of statistical methodologies, computer-assisted instruction for statistics, simulation experiments). Advanced statistical analysis with real applications (social sciences, marketing, psychometrics, chemometrics, signal processing, medical statistics, environmentrics, statistical physics).

計算統計和數據分析(CSDA)的官方出版物網絡計算和方法論的統計國際協會(CMStatistics)和統計計算機構間常設委員會,是一個國際期刊致力于傳播方法的研究和應用領域的計算統計和數據分析。該期刊由三個參考部分組成，分為下列主題領域:I)計算統計學-處理下列事宜的手稿:1)顯式電腦對統計方法的影響(例如,貝葉斯計算、生物信息學、計算機圖形學,計算機密集的推論方法、數據探索、數據挖掘、專家系統、啟發式知識基礎系統、機器學習、神經網絡、計算和優化方法,并行計算,統計數據庫,統計系統),以及2)開發、評估和統計軟件和算法的驗證。軟件和算法可以與手稿一起提交，并將與在線文章一起存儲。II)統計方法進行數據分析,處理小說手稿和原始數據分析策略和方法應用于生物統計學(為臨床試驗設計和分析方法,流行病學研究,統計遺傳學、或遺傳/環境交互),化學計量學、分類、數據探索、密度估計,實驗設計,environmetrics,教育、圖像分析、市場營銷、免費數據模型探索,模式識別,心理測量學，統計物理，圖像處理，魯棒程序。統計方法包括但不限于:引導、分類方法、臨床試驗、數據探索、密度估計,實驗的設計,模式識別/圖像分析、參數和非參數方法,統計遺傳學、貝葉斯建模、異常檢測、健壯的程序,交叉驗證,功能數據,模糊統計分析,混合模型,模型選擇和評估、非線性模型,偏最小二乘,潛變量模型、結構方程模型、監督學習,信號提取與濾波，時間序列建模，縱向分析，多級分析和質量控制。III)特殊用途-統計和計算界面的手稿(例如，統計方法的比較、統計的計算機輔助教學、模擬實驗)。具有實際應用的高級統計分析(社會科學、市場營銷、心理計量學、化學計量學、信號處理、醫學統計學、環境學、統計物理學)。