JOURNAL OF STATISTICAL PLANNING AND INFERENCE
期刊信息导读
- JOURNAL OF STATISTICAL PLANNING AND INFERENCE基本信息
- JOURNAL OF STATISTICAL PLANNING AND INFERENCE中科院SCI期刊分区
- 历年JOURNAL OF STATISTICAL PLANNING AND INFERENCE影响因子趋势图
- JOURNAL OF STATISTICAL PLANNING AND INFERENCE期刊英文简介
- JOURNAL OF STATISTICAL PLANNING AND INFERENCE期刊中文简介
JOURNAL OF STATISTICAL PLANNING AND INFERENCE基本信息
简称:J STAT PLAN INFER
中文名称:统计规划和推理杂志
研究方向:数学
2018-2019最新影响因子:0.756
2022年6月28日更新影响因子:1.095
SCI类别:SCIE
是否OA开放访问:No
出版地:NETHERLANDS
出版周期:Monthly
创刊年份:1977
年文章数:76
涉及的研究方向:数学-统计学与概率论
通讯方式:ELSEVIER SCIENCE BV, PO BOX 211, AMSTERDAM, NETHERLANDS, 1000 AE
官方网站:http://www.journals.elsevier.com/journal-of-statistical-planning-and-inference/
投稿网址:http://ees.elsevier.com/jspi/default.asp?acw=d5
审稿速度:一般,3-6周
平均录用比例:容易
PMC链接:http://www.ncbi.nlm.nih.gov/nlmcatalog?term=0378-3758%5BISSN%5D
JOURNAL OF STATISTICAL PLANNING AND INFERENCE期刊英文简介
The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists.We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.
JOURNAL OF STATISTICAL PLANNING AND INFERENCE期刊中文简介
《统计规划与推断》杂志提供了自己作为一个多面和包罗万象的桥梁,连接统计和概率的经典方面,以及新兴的跨学科方面,这些方面有可能使这一学科发生革命性的变化。在保持我们在统计推理、设计、经典概率和大样本方法方面的传统优势的同时,我们还拥有更广泛的包容性和更广泛的范围,以跟上我们作为统计学家、数学家和科学家所面临的新问题。我们在统计、概率、离散数学、机器学习和生物信息学的所有分支中发表高质量的文章。我们也特别欢迎关于统计学、概率论、机器学习和一般生物统计学基本主题的好文章和最新的评论文章。同样欢迎给编辑们写一些深思熟虑的信件,需要解决的有趣问题,以及带有优雅或美丽元素的简短笔记。
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