Data science in science最新文献_第2页

The Effect: An Introduction to Research Design and Causality 效应:研究设计与因果关系导论

Data science in science Pub Date : 2023-02-23 DOI: 10.1080/26941899.2023.2167433

Y. Wang

引用次数: 42

Hybrid Forecasting for Functional Time Series of Dissolved Oxygen Profiles 溶解氧剖面函数时间序列的混合预测

Data science in science Pub Date : 2023-02-08 DOI: 10.1080/26941899.2022.2152401

Luke Durell, J. Scott, A. Hering

引用次数: 0

Learning from Lending in the Interbank Network 从银行间网络借贷中学习

Data science in science Pub Date : 2023-01-30 DOI: 10.1080/26941899.2022.2151949

P. Laux, Wei Qian, Haici Zhang

引用次数: 0

Learning Financial Networks with High-frequency Trade Data. 利用高频交易数据学习金融网络

Data science in science Pub Date : 2023-01-01 Epub Date: 2023-02-28 DOI: 10.1080/26941899.2023.2166624

Kara Karpman, Sumanta Basu, David Easley, Sanghee Kim

{"title":"Learning Financial Networks with High-frequency Trade Data.","authors":"Kara Karpman, Sumanta Basu, David Easley, Sanghee Kim","doi":"10.1080/26941899.2023.2166624","DOIUrl":"10.1080/26941899.2023.2166624","url":null,"abstract":"<p><p>Financial networks are typically estimated by applying standard time series analyses to price-based economic variables collected at low-frequency (e.g., daily or monthly stock returns or realized volatility). These networks are used for risk monitoring and for studying information flows in financial markets. High-frequency intraday trade data sets may provide additional insights into network linkages by leveraging high-resolution information. However, such data sets pose significant modeling challenges due to their asynchronous nature, complex dynamics, and nonstationarity. To tackle these challenges, we estimate financial networks using random forests, a state-of-the-art machine learning algorithm which offers excellent prediction accuracy without expensive hyperparameter optimization. The edges in our network are determined by using microstructure measures of one firm to forecast the sign of the change in a market measure such as the realized volatility of another firm. We first investigate the evolution of network connectivity in the period leading up to the U.S. financial crisis of 2007-09. We find that the networks have the highest density in 2007, with high degree connectivity associated with Lehman Brothers in 2006. A second analysis into the nature of linkages among firms suggests that larger firms tend to offer better predictive power than smaller firms, a finding qualitatively consistent with prior works in the market microstructure literature.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10798789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47504522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Functional Stochastic Volatility in Financial Option Surfaces 金融期权表面的功能随机波动率

Data science in science Pub Date : 2022-12-31 DOI: 10.1080/26941899.2022.2152764

Phillip A. Jang, Michael Jauch, D. Matteson

引用次数: 1

Non-Fungible Token Transactions: Data and Challenges 不可替代代币交易：数据与挑战

Data science in science Pub Date : 2022-10-13 DOI: 10.1080/26941899.2022.2151950

Jason B. Cho, Sven Serneels, D. Matteson

引用次数: 7

Regularized Predictive Models for Beef Eating Quality of Individual Meals 单餐牛肉食用质量的正则化预测模型

Data science in science Pub Date : 2022-07-05 DOI: 10.1080/26941899.2022.2151948

G. Tarr, I. Wilms

{"title":"Regularized Predictive Models for Beef Eating Quality of Individual Meals","authors":"G. Tarr, I. Wilms","doi":"10.1080/26941899.2022.2151948","DOIUrl":"https://doi.org/10.1080/26941899.2022.2151948","url":null,"abstract":"Faced with changing markets and evolving consumer demands, beef industries are investing in grading systems to maximise value extraction throughout their entire supply chain. The Meat Standards Australia (MSA) system is a customer-oriented total quality management system that stands out internationally by predicting quality grades of specific muscles processed by a designated cooking method. The model currently underpinning the MSA system requires laborious effort to estimate and its prediction performance may be less accurate in the presence of unbalanced data sets where many\"muscle x cook\"combinations have few observations and/or few predictors of palatability are available. This paper proposes a novel predictive method for beef eating quality that bridges a spectrum of muscle x cook-specific models. At one extreme, each muscle x cook combination is modelled independently; at the other extreme a pooled predictive model is obtained across all muscle x cook combinations. Via a data-driven regularization method, we cover all muscle x cook-specific models along this spectrum. We demonstrate that the proposed predictive method attains considerable accuracy improvements relative to independent or pooled approaches on unique MSA data sets.","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46893809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Science in Science: Special Issue on Data Science in Environmental and Climate Sciences 科学中的数据科学:环境与气候科学中的数据科学特刊

Data science in science Pub Date : 2022-06-13 DOI: 10.1080/26941899.2022.2081002

Marina Friedrich, E. Mahieu, Stephan Smeekes, Jakob Raymaekers, I. Wilms, D. Matteson

引用次数: 0

Data Science in Science: A New Journal with a Radically Collaborative Mission 科学中的数据科学:一种具有激进协作使命的新期刊

Data science in science Pub Date : 2022-04-15 DOI: 10.1080/26941899.2022.2043137

D. Matteson

引用次数: 1

Comparison of CYGNSS and Jason-3 Wind Speed Measurements via Gaussian Processes 基于高斯过程的CYGNSS和Jason-3风速测量的比较

Data science in science Pub Date : 2022-03-07 DOI: 10.1080/26941899.2023.2194349

William Bekerman, J. Guinness

{"title":"Comparison of CYGNSS and Jason-3 Wind Speed Measurements via Gaussian Processes","authors":"William Bekerman, J. Guinness","doi":"10.1080/26941899.2023.2194349","DOIUrl":"https://doi.org/10.1080/26941899.2023.2194349","url":null,"abstract":"Wind is a critical component of the Earth system and has unmistakable impacts on everyday life. The CYGNSS satellite mission improves observational coverage of ocean winds via a fleet of eight micro-satellites that use reflected GNSS signals to infer surface wind speed. We present analyses characterizing variability in wind speed measurements among the eight CYGNSS satellites and between antennas. In particular, we use a carefully constructed Gaussian process model that leverages comparisons between CYGNSS and Jason-3 during a one-year period from September 2019 to September 2020. The CYGNSS sensors exhibit a range of biases, most of them between -1.0 m/s and +0.2 m/s with respect to Jason-3, indicating that some CYGNSS sensors are biased with respect to one another and with respect to Jason-3. The biases between the starboard and port antennas within a CYGNSS satellite are smaller. Our results are consistent with, yet sharper than, a more traditional paired comparison analysis. We also explore the possibility that the bias depends on wind speed, finding some evidence that CYGNSS satellites have positive biases with respect to Jason-3 at low wind speeds. However, we argue that there are subtle issues associated with estimating wind speed-dependent biases, so additional careful statistical modeling and analysis is warranted.","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43811000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0