找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Data Science for Financial Econometrics; Nguyen Ngoc Thach,Vladik Kreinovich,Nguyen Duc Tru Book 2021 The Editor(s) (if applicable) and Th

[復(fù)制鏈接]
查看: 21879|回復(fù): 59
樓主
發(fā)表于 2025-3-21 17:47:19 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Science for Financial Econometrics
編輯Nguyen Ngoc Thach,Vladik Kreinovich,Nguyen Duc Tru
視頻videohttp://file.papertrans.cn/264/263118/263118.mp4
概述Presents recent findings and ideas on applying data science techniques to economic phenomena – and, in particular, financial phenomena.Inspires practitioners to learn how to apply various data science
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Data Science for Financial Econometrics;  Nguyen Ngoc Thach,Vladik Kreinovich,Nguyen Duc Tru Book 2021 The Editor(s) (if applicable) and Th
描述.This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.??.
出版日期Book 2021
關(guān)鍵詞Computational Intelligence; Intelligent Systems; Econometrics; Data Science; Probabilistic Methods; Econo
版次1
doihttps://doi.org/10.1007/978-3-030-48853-6
isbn_softcover978-3-030-48855-0
isbn_ebook978-3-030-48853-6Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書目名稱Data Science for Financial Econometrics影響因子(影響力)




書目名稱Data Science for Financial Econometrics影響因子(影響力)學(xué)科排名




書目名稱Data Science for Financial Econometrics網(wǎng)絡(luò)公開度




書目名稱Data Science for Financial Econometrics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Science for Financial Econometrics被引頻次




書目名稱Data Science for Financial Econometrics被引頻次學(xué)科排名




書目名稱Data Science for Financial Econometrics年度引用




書目名稱Data Science for Financial Econometrics年度引用學(xué)科排名




書目名稱Data Science for Financial Econometrics讀者反饋




書目名稱Data Science for Financial Econometrics讀者反饋學(xué)科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權(quán)限
沙發(fā)
發(fā)表于 2025-3-21 22:57:36 | 只看該作者
A QP Framework: A Contextual Representation of Agents’ Preferences in Investment Choicedescription of their vacillating ambiguity perception characterized by non-additive beliefs of agents. Some of the implications of non-classicality in beliefs for the composite market outcomes can also be modelled with the aid of QP. As a final step we also discuss the contributions of the growing b
板凳
發(fā)表于 2025-3-22 04:27:06 | 只看該作者
地板
發(fā)表于 2025-3-22 07:11:42 | 只看該作者
5#
發(fā)表于 2025-3-22 08:49:59 | 只看該作者
6#
發(fā)表于 2025-3-22 16:51:45 | 只看該作者
7#
發(fā)表于 2025-3-22 19:09:03 | 只看該作者
Impacts of Internal and External Macroeconomic Factors on Firm Stock Price in an Expansion Econometr
8#
發(fā)表于 2025-3-23 00:23:39 | 只看該作者
Andreas Richter,J?rg Stiller,Roger Grundmannwe explain the empirical success of these methods by showing that they are the only ones which are invariant with respect to natural transformations—like scaling which corresponds to selecting a different measuring?unit.
9#
發(fā)表于 2025-3-23 04:16:59 | 只看該作者
Alexey N. Volkov,Gerard M. O’Connorce in support of the selected model: weak, strong, very strong, or decisive. The corresponding strength levels are based on a heuristic scale proposed by Harold Jeffreys, one of the pioneers of the Bayes approach to statistics. In this paper, we propose a justification for this scale.
10#
發(fā)表于 2025-3-23 08:18:47 | 只看該作者
M. Hafez,K. Morinishi,J. Periauxs were not operating at an optimal scale or even close to optimal scale. The results also indicated that the number of employees input was used excessively in the sample MFIs. The findings of the present study would be useful for policymakers in improving the current levels of technical and scale efficiencies of MFIs.
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-28 13:05
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
林口县| 清镇市| 察哈| 如皋市| 桂阳县| 鹤壁市| 正阳县| 舒城县| 右玉县| 孝义市| 岳普湖县| 丘北县| 上杭县| 赤城县| 稷山县| 普格县| 磐安县| 金平| 林州市| 黄龙县| 资溪县| 平罗县| 阳春市| 临潭县| 南昌县| 板桥市| 衢州市| 全椒县| 双桥区| 塔城市| 宜章县| 那曲县| 苗栗县| 唐海县| 安图县| 蕲春县| 大邑县| 昭觉县| 重庆市| 临泽县| 巨鹿县|