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Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data. 基于双向f散度的时间序列数据缺失值输入深度生成方法。
IF 0.9
Stats Pub Date : 2025-03-01 Epub Date: 2025-01-14 DOI: 10.3390/stats8010007
Wen-Shan Liu, Tong Si, Aldas Kriauciunas, Marcus Snell, Haijun Gong
{"title":"Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data.","authors":"Wen-Shan Liu, Tong Si, Aldas Kriauciunas, Marcus Snell, Haijun Gong","doi":"10.3390/stats8010007","DOIUrl":"10.3390/stats8010007","url":null,"abstract":"<p><p>Imputing missing values in high-dimensional time-series data remains a significant challenge in statistics and machine learning. Although various methods have been proposed in recent years, many struggle with limitations and reduced accuracy, particularly when the missing rate is high. In this work, we present a novel f-divergence-based bidirectional generative adversarial imputation network, tf-BiGAIN, designed to address these challenges in time-series data imputation. Unlike traditional imputation methods, tf-BiGAIN employs a generative model to synthesize missing values without relying on distributional assumptions. The imputation process is achieved by training two neural networks, implemented using bidirectional modified gated recurrent units, with f-divergence serving as the objective function to guide optimization. Compared to existing deep learning-based methods, tf-BiGAIN introduces two key innovations. First, the use of f-divergence provides a flexible and adaptable framework for optimizing the model across diverse imputation tasks, enhancing its versatility. Second, the use of bidirectional gated recurrent units allows the model to leverage both forward and backward temporal information. This bidirectional approach enables the model to effectively capture dependencies from both past and future observations, enhancing its imputation accuracy and robustness. We applied tf-BiGAIN to analyze two real-world time-series datasets, demonstrating its superior performance in imputing missing values and outperforming existing methods in terms of accuracy and robustness.</p>","PeriodicalId":93142,"journal":{"name":"Stats","volume":"8 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11793919/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257500","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
Exact Inference for Random Effects Meta-Analyses for Small, Sparse Data. 小型稀疏数据随机效应荟萃分析的精确推断。
IF 0.9
Stats Pub Date : 2025-03-01 Epub Date: 2025-01-07 DOI: 10.3390/stats8010005
Jessica Gronsbell, Zachary R McCaw, Timothy Regis, Lu Tian
{"title":"Exact Inference for Random Effects Meta-Analyses for Small, Sparse Data.","authors":"Jessica Gronsbell, Zachary R McCaw, Timothy Regis, Lu Tian","doi":"10.3390/stats8010005","DOIUrl":"10.3390/stats8010005","url":null,"abstract":"<p><p>Meta-analysis aggregates information across related studies to provide more reliable statistical inference and has been a vital tool for assessing the safety and efficacy of many high-profile pharmaceutical products. A key challenge in conducting a meta-analysis is that the number of related studies is typically small. Applying classical methods that are asymptotic in the number of studies can compromise the validity of inference, particularly when heterogeneity across studies is present. Moreover, serious adverse events are often rare and can result in one or more studies with no events in at least one study arm. Practitioners remove studies in which no events have occurred in one or both arms or apply arbitrary continuity corrections (e.g., adding one event to arms with zero events) to stabilize or define effect estimates in such settings, which can further invalidate subsequent inference. To address these significant practical issues, we introduce an exact inference method for random effects meta-analysis of a treatment effect in the two-sample setting with rare events, which we coin \"XRRmeta\". In contrast to existing methods, XRRmeta provides valid inference for meta-analysis in the presence of between-study heterogeneity and when the event rates, number of studies, and/or the within-study sample sizes are small. Extensive numerical studies indicate that XRRmeta does not yield overly conservative inference. We apply our proposed method to two real-data examples using our open-source R package.</p>","PeriodicalId":93142,"journal":{"name":"Stats","volume":"8 1","pages":""},"PeriodicalIF":0.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145139610","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
Investigating Risk Factors for Racial Disparity in E-Cigarette Use with PATH Study. 利用PATH研究调查电子烟使用中种族差异的危险因素。
IF 0.9
Stats Pub Date : 2024-09-01 Epub Date: 2024-06-21 DOI: 10.3390/stats7030037
Amy Liu, Kennedy Dorsey, Almetra Granger, Ty-Runet Bryant, Tung-Sung Tseng, Michael Celestin, Qingzhao Yu
{"title":"Investigating Risk Factors for Racial Disparity in E-Cigarette Use with PATH Study.","authors":"Amy Liu, Kennedy Dorsey, Almetra Granger, Ty-Runet Bryant, Tung-Sung Tseng, Michael Celestin, Qingzhao Yu","doi":"10.3390/stats7030037","DOIUrl":"10.3390/stats7030037","url":null,"abstract":"<p><strong>Background: </strong>Previous research has identified differences in e-cigarette use and socioeconomic factors between different racial groups However, there is little research examining specific risk factors contributing to the racial differences.</p><p><strong>Objective: </strong>This study sought to identify racial disparities in e-cigarette use and to determine risk factors that help explain these differences.</p><p><strong>Methods: </strong>We used Wave 5 (2018-2019) of the Adult Population Assessment of Tobacco and Health (PATH) Study. First, we conducted descriptive statistics of e-smoking across our risk factor variables. Next, we used multiple logistic regression to check the risk effects by adjusting all covariates. Finally, we conducted a mediation analysis to determine whether identified factors showed evidence of influencing the association between race and e-cigarette use. All analyses were performed in R or SAS. The R package mma was used for the mediation analysis.</p><p><strong>Results: </strong>Between Hispanic and non-Hispanic White populations, our potential risk factors collectively explain 17.5% of the racial difference, former cigarette smoking explains 7.6%, receiving e-cigarette advertising 2.6%, and perception of e-cigarette harm explains 27.8% of the racial difference. Between non-Hispanic Black and non-Hispanic White populations, former cigarette smoking, receiving e-cigarette advertising, and perception of e-cigarette harm explain 5.2%, 1.8%, and 6.8% of the racial difference, respectively. E-cigarette use is most prevalent in the non-Hispanic White population compared to non-Hispanic Black and Hispanic populations, which may be explained by former cigarette smoking, exposure to e-cigarette advertising, and e-cigarette harm perception.</p><p><strong>Conclusions: </strong>These findings suggest that racial differences in e-cigarette use may be reduced by increasing knowledge of the dangers associated with e-cigarette use and reducing exposure to e-cigarette advertisements. This comprehensive analysis of risk factors can be used to significantly guide smoking cessation efforts and address potential health burden disparities arising from differences in e-cigarette usage.</p>","PeriodicalId":93142,"journal":{"name":"Stats","volume":"7 3","pages":"613-626"},"PeriodicalIF":0.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030447","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
Doubly Robust Estimation and Semiparametric Efficiency in Generalized Partially Linear Models with Missing Outcomes. 缺失结果的广义部分线性模型的双鲁棒估计和半参数效率。
IF 0.9
Stats Pub Date : 2024-09-01 Epub Date: 2024-08-31 DOI: 10.3390/stats7030056
Lu Wang, Zhongzhe Ouyang, Xihong Lin
{"title":"Doubly Robust Estimation and Semiparametric Efficiency in Generalized Partially Linear Models with Missing Outcomes.","authors":"Lu Wang, Zhongzhe Ouyang, Xihong Lin","doi":"10.3390/stats7030056","DOIUrl":"10.3390/stats7030056","url":null,"abstract":"<p><p>We investigate a semiparametric generalized partially linear regression model that accommodates missing outcomes, with some covariates modeled parametrically and others nonparametrically. We propose a class of augmented inverse probability weighted (AIPW) kernel-profile estimating equations. The nonparametric component is estimated using AIPW kernel estimating equations, while parametric regression coefficients are estimated using AIPW profile estimating equations. We demonstrate the doubly robust nature of the AIPW estimators for both nonparametric and parametric components. Specifically, these estimators remain consistent if either the assumed model for the probability of missing data or that for the conditional mean of the outcome, given covariates and auxiliary variables, is correctly specified, though not necessarily both simultaneously. Additionally, the AIPW profile estimator for parametric regression coefficients is consistent and asymptotically normal under the semiparametric model defined by the generalized partially linear model on complete data, assuming that the missing data mechanism is missing at random. When both working models are correctly specified, this estimator achieves semiparametric efficiency, with its asymptotic variance reaching the efficiency bound. We validate our approach through simulations to assess the finite sample performance of the proposed estimators and apply the method to a study that investigates risk factors associated with myocardial ischemia.</p>","PeriodicalId":93142,"journal":{"name":"Stats","volume":"7 3","pages":"924-943"},"PeriodicalIF":0.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202384","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
Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness. 整合蛋白质组学分析和机器学习预测前列腺癌侵袭性。
IF 0.9
Stats Pub Date : 2024-09-01 Epub Date: 2024-08-21 DOI: 10.3390/stats7030053
Sheila M Valle Cortés, Jaileene Pérez Morales, Mariely Nieves Plaza, Darielys Maldonado, Swizel M Tevenal Baez, Marc A Negrón Blas, Cayetana Lazcano Etchebarne, José Feliciano, Gilberto Ruiz Deyá, Juan C Santa Rosario, Pedro Santiago Cardona
{"title":"Integrating Proteomic Analysis and Machine Learning to Predict Prostate Cancer Aggressiveness.","authors":"Sheila M Valle Cortés, Jaileene Pérez Morales, Mariely Nieves Plaza, Darielys Maldonado, Swizel M Tevenal Baez, Marc A Negrón Blas, Cayetana Lazcano Etchebarne, José Feliciano, Gilberto Ruiz Deyá, Juan C Santa Rosario, Pedro Santiago Cardona","doi":"10.3390/stats7030053","DOIUrl":"10.3390/stats7030053","url":null,"abstract":"<p><p>Prostate cancer (PCa) poses a significant challenge because of the difficulty in identifying aggressive tumors, leading to overtreatment and missed personalized therapies. Although only 8% of cases progress beyond the prostate, the accurate prediction of aggressiveness remains crucial. Thus, this study focused on studying retinoblastoma phosphorylated at Serine 249 (Phospho-Rb S249), N-cadherin, β-catenin, and E-cadherin as biomarkers for identifying aggressive PCa using a logistic regression model and a classification and regression tree (CART). Using immunohistochemistry (IHC), we targeted the expression of these biomarkers in PCa tissues and correlated their expression with clinicopathological data of the tumor. The results showed a negative correlation between E-cadherin and β-catenin with aggressive tumor behavior, whereas Phospho-Rb S249 and N-cadherin positively correlated with increased tumor aggressiveness. Furthermore, patients were stratified based on Gleason scores and E-cadherin staining patterns to evaluate their capability for early identification of aggressive PCa. Our findings suggest that the classification tree is the most effective method for measuring the utility of these biomarkers in clinical practice, incorporating β-catenin, tumor grade, and Gleason grade as relevant determinants for identifying patients with Gleason scores ≥ 4 + 3. This study could potentially benefit patients with aggressive PCa by enabling early disease detection and closer monitoring.</p>","PeriodicalId":93142,"journal":{"name":"Stats","volume":"7 3","pages":"875-893"},"PeriodicalIF":0.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12494234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145234320","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
Assessing Spillover Effects of Medications for Opioid Use Disorder on HIV Risk Behaviors among a Network of People Who Inject Drugs. 评估阿片类药物使用障碍对注射毒品人群网络中艾滋病毒风险行为的溢出效应。
IF 0.9
Stats Pub Date : 2024-06-01 Epub Date: 2024-06-19 DOI: 10.3390/stats7020034
Joseph Puleo, Ashley Buchanan, Natallia Katenka, M Elizabeth Halloran, Samuel R Friedman, Georgios Nikolopoulos
{"title":"Assessing Spillover Effects of Medications for Opioid Use Disorder on HIV Risk Behaviors among a Network of People Who Inject Drugs.","authors":"Joseph Puleo, Ashley Buchanan, Natallia Katenka, M Elizabeth Halloran, Samuel R Friedman, Georgios Nikolopoulos","doi":"10.3390/stats7020034","DOIUrl":"10.3390/stats7020034","url":null,"abstract":"<p><p>People who inject drugs (PWID) have an increased risk of HIV infection partly due to injection behaviors often related to opioid use. Medications for opioid use disorder (MOUD) have been shown to reduce HIV infection risk, possibly by reducing injection risk behaviors. MOUD may benefit individuals who do not receive it themselves but are connected through social, sexual, or drug use networks with individuals who are treated. This is known as spillover. Valid estimation of spillover in network studies requires considering the network's community structure. Communities are groups of densely connected individuals with sparse connections to other groups. We analyzed a network of 277 PWID and their contacts from the Transmission Reduction Intervention Project. We assessed the effect of MOUD on reductions in injection risk behaviors and the possible benefit for network contacts of participants treated with MOUD. We identified communities using modularity-based methods and employed inverse probability weighting with community-level propensity scores to adjust for measured confounding. We found that MOUD may have beneficial spillover effects on reducing injection risk behaviors. The magnitudes of estimated effects were sensitive to the community detection method. Careful consideration should be paid to the significance of community structure in network studies evaluating spillover.</p>","PeriodicalId":93142,"journal":{"name":"Stats","volume":"7 2","pages":"549-575"},"PeriodicalIF":0.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12165006/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303849","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
Precise Tensor Product Smoothing via Spectral Splines 通过光谱样条实现精确的张量乘积平滑化
Stats Pub Date : 2024-01-10 DOI: 10.3390/stats7010003
Nathaniel E. Helwig
{"title":"Precise Tensor Product Smoothing via Spectral Splines","authors":"Nathaniel E. Helwig","doi":"10.3390/stats7010003","DOIUrl":"https://doi.org/10.3390/stats7010003","url":null,"abstract":"Tensor product smoothers are frequently used to include interaction effects in multiple nonparametric regression models. Current implementations of tensor product smoothers either require using approximate penalties, such as those typically used in generalized additive models, or costly parameterizations, such as those used in smoothing spline analysis of variance models. In this paper, I propose a computationally efficient and theoretically precise approach for tensor product smoothing. Specifically, I propose a spectral representation of a univariate smoothing spline basis, and I develop an efficient approach for building tensor product smooths from marginal spectral spline representations. The developed theory suggests that current tensor product smoothing methods could be improved by incorporating the proposed tensor product spectral smoothers. Simulation results demonstrate that the proposed approach can outperform popular tensor product smoothing implementations, which supports the theoretical results developed in the paper.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"59 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139440934","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
Predicting Random Walks and a Data-Splitting Prediction Region 预测随机行走和数据分割预测区域
Stats Pub Date : 2024-01-08 DOI: 10.3390/stats7010002
Mulubrhan G. Haile, Lingling Zhang, David J. Olive
{"title":"Predicting Random Walks and a Data-Splitting Prediction Region","authors":"Mulubrhan G. Haile, Lingling Zhang, David J. Olive","doi":"10.3390/stats7010002","DOIUrl":"https://doi.org/10.3390/stats7010002","url":null,"abstract":"Perhaps the first nonparametric, asymptotically optimal prediction intervals are provided for univariate random walks, with applications to renewal processes. Perhaps the first nonparametric prediction regions are introduced for vector-valued random walks. This paper further derives nonparametric data-splitting prediction regions, which are underpinned by very simple theory. Some of the prediction regions can be used when the data distribution does not have first moments, and some can be used for high-dimensional data, where the number of predictors is larger than the sample size. The prediction regions can make use of many estimators of multivariate location and dispersion.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"53 36","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139447266","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
The Mediating Impact of Innovation Types in the Relationship between Innovation Use Theory and Market Performance 创新类型在创新使用理论与市场绩效关系中的中介影响
Stats Pub Date : 2023-12-30 DOI: 10.3390/stats7010001
Shieh-Liang Chen, Kuo-Liang Chen
{"title":"The Mediating Impact of Innovation Types in the Relationship between Innovation Use Theory and Market Performance","authors":"Shieh-Liang Chen, Kuo-Liang Chen","doi":"10.3390/stats7010001","DOIUrl":"https://doi.org/10.3390/stats7010001","url":null,"abstract":"The ultimate goal of innovation is to improve performance. But if people’s needs and uses are ignored, innovation will only be a formality. In the past, research on innovation mostly focused on technology, processes, business models, services, and organizations. The measurement of innovation focuses on capabilities, processes, results, and methods, but there has always been a lack of pre-innovation measurements and tools. This study is the first to use the innovation use theory proposed by Christensen et al. combined with innovation types, and it uses the measurement focus on the early stage of innovation as a post-innovation performance prediction. This study collected 590 valid samples and used SPSS and the four-step BK method to conduct regression analysis and mediation tests. The empirical results obtained the following: (1) a confirmed model and scale of the innovation use theory; (2) that three constructs of innovation use theory have an impact on market performance; and (3) that innovation types acting as mediators will improve market performance. This study establishes an academic model of the innovation use theory to provide a clear scale tool for subsequent research. In practice, it can first measure the direction of innovation and performance prediction, providing managers with a reference when developing new products and applying market strategies.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139138519","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
Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices 比特币和以太坊波动率指数的跃迁-稳健实现-GARCH-MIDAS-X 估计器
Stats Pub Date : 2023-12-12 DOI: 10.3390/stats6040082
Julien Chevallier, Bilel Sanhaji
{"title":"Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices","authors":"Julien Chevallier, Bilel Sanhaji","doi":"10.3390/stats6040082","DOIUrl":"https://doi.org/10.3390/stats6040082","url":null,"abstract":"In this paper, we conducted an empirical investigation of the realized volatility of cryptocurrencies using an econometric approach. This work’s two main characteristics are: (i) the realized volatility to be forecast filters jumps, and (ii) the benefit of using various historical/implied volatility indices from brokers as exogenous variables was explicitly considered. We feature a jump-robust extension of the REGARCH-MIDAS-X model incorporating realized beta GARCH processes and MIDAS filters with monthly, daily, and hourly components. First, we estimated six jump-robust estimators of realized volatility for Bitcoin and Ethereum that were retained as the dependent variable. Second, we inserted ten Bitcoin and Ethereum volatility indices gathered from various exchanges as an exogenous variable, each at a time. Third, we explored their forecasting ability based on the MSE and QLIKE statistics. Our sample spanned the period from May 2018 to January 2023. The main result featured the best predictors among the volatility indices for Bitcoin and Ethereum derived from 30-day implied volatility. The significance of the findings could mostly be attributable to the ability of our new model to incorporate financial and technological variables directly into the specification of the Bitcoin and Ethereum volatility dynamics.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"3 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007733","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
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