找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Advances in Complex Data Modeling and Computational Methods in Statistics; Anna Maria Paganoni,Piercesare Secchi Book 2015 Springer Intern

[复制链接]
查看: 46553|回复: 49
发表于 2025-3-21 17:13:58 | 显示全部楼层 |阅读模式
期刊全称Advances in Complex Data Modeling and Computational Methods in Statistics
影响因子2023Anna Maria Paganoni,Piercesare Secchi
视频video
发行地址Offers numerous step-by-step tutorials to help the reader to learn quickly.A special chapter on next generation Flash prepares readers for the future.Includes suggestions on how to protect flash sites
学科分类Contributions to Statistics
图书封面Titlebook: Advances in Complex Data Modeling and Computational Methods in Statistics;  Anna Maria Paganoni,Piercesare Secchi Book 2015 Springer Intern
影响因子The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.
Pindex Book 2015
The information of publication is updating

书目名称Advances in Complex Data Modeling and Computational Methods in Statistics影响因子(影响力)




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics影响因子(影响力)学科排名




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics网络公开度




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics网络公开度学科排名




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics被引频次




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics被引频次学科排名




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics年度引用




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics年度引用学科排名




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics读者反馈




书目名称Advances in Complex Data Modeling and Computational Methods in Statistics读者反馈学科排名




单选投票, 共有 1 人参与投票
 

0票 0.00%

Perfect with Aesthetics

 

0票 0.00%

Better Implies Difficulty

 

1票 100.00%

Good and Satisfactory

 

0票 0.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 23:47:27 | 显示全部楼层
发表于 2025-3-22 04:02:24 | 显示全部楼层
Building Multiple Regression Models,e graphical model. The first model is used to infer conditional independence graphs. The latter model is applied to compute the relative importance or contribution of each predictor to the response variables. Recently, penalized likelihood approaches have also been proposed to estimate graph structu
发表于 2025-3-22 07:21:05 | 显示全部楼层
发表于 2025-3-22 09:05:06 | 显示全部楼层
发表于 2025-3-22 15:41:10 | 显示全部楼层
Building Multiple Regression Models, contribute, presenting a topic and generating a discussion. In this paper, we propose the BarCamp as an innovative way of producing and communicating statistical knowledge, and we describe the experiment held at Politecnico di Milano, entitled “Technology Foresight and Statistics for the Future”.
发表于 2025-3-22 19:13:40 | 显示全部楼层
Nonlinear Multiple Regression Models,d biochemical features of the nuclear DNA are used to investigate salient properties and determinants of change (mutations) in the human genome. The studies under review, all conducted by an interdisciplinary group of investigators at The Pennsylvania State University, required the use of a range of
发表于 2025-3-22 22:21:10 | 显示全部楼层
发表于 2025-3-23 01:35:54 | 显示全部楼层
Nonlinear Multiple Regression Models,everal canonical generalizations of non-Euclidean means. More involved data descriptors, for instance principal components generalize into even more complicated concepts. (Semi)-intrinsic statistical analysis allows to study inference on descriptors that can be represented as elements of another non
发表于 2025-3-23 08:41:36 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-18 16:12
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表