COMPUTATIONAL STATISTICS & DATA ANALYSIS SCIE

中文简介

计算统计和数据分析(CSDA)的官方出版物网络计算和方法论的统计国际协会(CMStatistics)和统计计算机构间常设委员会,是一个国际期刊致力于传播方法的研究和应用领域的计算统计和数据分析。该期刊由三个参考部分组成，分为下列主题领域:I)计算统计学-处理下列事宜的手稿:1)显式电脑对统计方法的影响(例如,贝叶斯计算、生物信息学、计算机图形学,计算机密集的推论方法、数据探索、数据挖掘、专家系统、启发式知识基础系统、机器学习、神经网络、计算和优化方法,并行计算,统计数据库,统计系统),以及2)开发、评估和统计软件和算法的验证。软件和算法可以与手稿一起提交，并将与在线文章一起存储。II)统计方法进行数据分析,处理小说手稿和原始数据分析策略和方法应用于生物统计学(为临床试验设计和分析方法,流行病学研究,统计遗传学、或遗传/环境交互),化学计量学、分类、数据探索、密度估计,实验设计,environmetrics,教育、图像分析、市场营销、免费数据模型探索,模式识别,心理测量学，统计物理，图像处理，鲁棒程序。统计方法包括但不限于:引导、分类方法、临床试验、数据探索、密度估计,实验的设计,模式识别/图像分析、参数和非参数方法,统计遗传学、贝叶斯建模、异常检测、健壮的程序,交叉验证,功能数据,模糊统计分析,混合模型,模型选择和评估、非线性模型,偏最小二乘,潜变量模型、结构方程模型、监督学习,信号提取与滤波，时间序列建模，纵向分析，多级分析和质量控制。III)特殊用途-统计和计算界面的手稿(例如，统计方法的比较、统计的计算机辅助教学、模拟实验)。具有实际应用的高级统计分析(社会科学、市场营销、心理计量学、化学计量学、信号处理、医学统计学、环境学、统计物理学)。

投稿网址：http://ees.elsevier.com/csda/default.asp?acw=3

英文简介

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).