Journal of data science : JDS最新文献_第8页

Flowthrough Centrality: A Stable Node Centrality Measure 流动中心性:一个稳定的节点中心性度量

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1081

Charles F. Mann, M. McGee, E. Olinick, D. Matula

引用次数: 0

Inference for Optimal Differential Privacy Procedures for Frequency Tables 频率表的最优微分隐私过程的推理

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1044

Chengcheng Li, Na Wang, Gongjun Xu

{"title":"Inference for Optimal Differential Privacy Procedures for Frequency Tables","authors":"Chengcheng Li, Na Wang, Gongjun Xu","doi":"10.6339/22-jds1044","DOIUrl":"https://doi.org/10.6339/22-jds1044","url":null,"abstract":"When releasing data to the public, a vital concern is the risk of exposing personal information of the individuals who have contributed to the data set. Many mechanisms have been proposed to protect individual privacy, though less attention has been dedicated to practically conducting valid inferences on the altered privacy-protected data sets. For frequency tables, the privacy-protection-oriented perturbations often lead to negative cell counts. Releasing such tables can undermine users’ confidence in the usefulness of such data sets. This paper focuses on releasing one-way frequency tables. We recommend an optimal mechanism that satisfies ϵ-differential privacy (DP) without suffering from having negative cell counts. The procedure is optimal in the sense that the expected utility is maximized under a given privacy constraint. Valid inference procedures for testing goodness-of-fit are also developed for the DP privacy-protected data. In particular, we propose a de-biased test statistic for the optimal procedure and derive its asymptotic distribution. In addition, we also introduce testing procedures for the commonly used Laplace and Gaussian mechanisms, which provide a good finite sample approximation for the null distributions. Moreover, the decaying rate requirements for the privacy regime are provided for the inference procedures to be valid. We further consider common users’ practices such as merging related or neighboring cells or integrating statistical information obtained across different data sources and derive valid testing procedures when these operations occur. Simulation studies show that our inference results hold well even when the sample size is relatively small. Comparisons with the current field standards, including the Laplace, the Gaussian (both with/without post-processing of replacing negative cell counts with zeros), and the Binomial-Beta McClure-Reiter mechanisms, are carried out. In the end, we apply our method to the National Center for Early Development and Learning’s (NCEDL) multi-state studies data to demonstrate its practical applicability.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320068","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

Comparison of Methods for Imputing Social Network Data 社会网络数据输入方法的比较

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1045

Ziqian Xu, Jiarui Hai, Yutong Yang, Zhiyong Zhang

{"title":"Comparison of Methods for Imputing Social Network Data","authors":"Ziqian Xu, Jiarui Hai, Yutong Yang, Zhiyong Zhang","doi":"10.6339/22-jds1045","DOIUrl":"https://doi.org/10.6339/22-jds1045","url":null,"abstract":"Social network data often contain missing values because of the sensitive nature of the information collected and the dependency among the network actors. As a response, network imputation methods including simple ones constructed from network structural characteristics and more complicated model-based ones have been developed. Although past studies have explored the influence of missing data on social networks and the effectiveness of imputation procedures in many missing data conditions, the current study aims to evaluate a more extensive set of eight network imputation techniques (i.e., null-tie, Reconstruction, Preferential Attachment, Constrained Random Dot Product Graph, Multiple Imputation by Bayesian Exponential Random Graph Models or BERGMs, k-Nearest Neighbors, Random Forest, and Multiple Imputation by Chained Equations) under more practical conditions through comprehensive simulation. A factorial design for missing data conditions is adopted with factors including missing data types, missing data mechanisms, and missing data proportions, which are applied to generated social networks with varying numbers of actors based on 4 different sets of coefficients in ERGMs. Results show that the effectiveness of imputation methods differs by missing data types, missing data mechanisms, the evaluation criteria used, and the complexity of the social networks. More complex methods such as the BERGMs have consistently good performances in recovering missing edges that should have been present. While simpler methods like Reconstruction work better in recovering network statistics when the missing proportion of present edges is low, the BERGMs work better when more present edges are missing. The BERGMs also work well in recovering ERGM coefficients when the networks are complex and the missing data type is actor non-response. In conclusion, researchers analyzing social networks with incomplete data should identify the network structures of interest and the potential missing data types before selecting appropriate imputation methods.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71320102","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: 1

Computational Challenges of t and Related Copulas t和相关copula的计算挑战

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1034

Erik Hintz, M. Hofert, C. Lemieux

引用次数: 1

Clustering US States by Time Series of COVID-19 New Case Counts in the Early Months with Non-Negative Matrix Factorization 用非负矩阵分解法根据COVID-19早期新病例数时间序列聚类美国各州

Journal of data science : JDS Pub Date : 2022-01-01 DOI: 10.6339/22-jds1036

Jianmin Chen, Panpan Zhang

引用次数: 0

Is the Group Structure Important in Grouped Functional Time Series? 分组函数时间序列中的分组结构重要吗？

Journal of data science : JDS Pub Date : 2021-11-08 DOI: 10.6339/21-jds1031

Yang Yang, H. Shang

引用次数: 2

Privacy-Preserving Inference on the Ratio of Two Gaussians Using Sums 基于和的两个Gaussian比率的保密推理

Journal of data science : JDS Pub Date : 2021-10-28 DOI: 10.6339/22-jds1050

Jingang Miao, Yiming Paul Li

{"title":"Privacy-Preserving Inference on the Ratio of Two Gaussians Using Sums","authors":"Jingang Miao, Yiming Paul Li","doi":"10.6339/22-jds1050","DOIUrl":"https://doi.org/10.6339/22-jds1050","url":null,"abstract":"The ratio of two Gaussians is useful in many contexts of statistical inference. We discuss statistically valid inference of the ratio under Differential Privacy (DP). We use the delta method to derive the asymptotic distribution of the ratio estimator and use the Gaussian mechanism to provide (epsilon, delta)-DP guarantees. Like many statistics, quantities involved in the inference of a ratio can be re-written as functions of sums, and sums are easy to work with for many reasons. In the context of DP, the sensitivity of a sum is easy to calculate. We focus on getting the correct coverage probability of 95% confidence intervals (CIs) of the DP ratio estimator. Our simulations show that the no-correction method, which ignores the DP noise, gives CIs that are too narrow to provide proper coverage for small samples. In our specific simulation scenario, the coverage of 95% CIs can be as low as below 10%. We propose two methods to mitigate the under-coverage issue, one based on Monte Carlo simulation and the other based on analytical correction. We show that the CIs of our methods have much better coverage with reasonable privacy budgets. In addition, our methods can handle weighted data, when the weights are fixed and bounded.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42315729","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

On a Weibull-Distributed Error Component of a Multiplicative Error Model Under Inverse Square Root Transformation 方根反变换下乘法误差模型的威布尔分布误差分量

Journal of data science : JDS Pub Date : 2021-10-12 DOI: 10.11648/J.IJDSA.20210704.12

C. U. Onyemachi, S. Onyeagu, Samuel Ademola Phillips, Jamiu Adebowale Oke, Callistus Ezekwe Ugwo

{"title":"On a Weibull-Distributed Error Component of a Multiplicative Error Model Under Inverse Square Root Transformation","authors":"C. U. Onyemachi, S. Onyeagu, Samuel Ademola Phillips, Jamiu Adebowale Oke, Callistus Ezekwe Ugwo","doi":"10.11648/J.IJDSA.20210704.12","DOIUrl":"https://doi.org/10.11648/J.IJDSA.20210704.12","url":null,"abstract":"We first consider the Multiplicative Error Model (MEM) introduced in financial econometrics by Engle (2002) as a general class of time series model for positive-valued random variables, which are decomposed into the product of their conditional mean and a positive-valued error term. Considering the possibility that the error component of a MEM can be a Weibull distribution and the need for data transformation as a popular remedial measure to stabilize the variance of a data set prior to statistical modeling, this paper investigates the impact of the inverse square root transformation (ISRT) on the mean and variance of a Weibull-distributed error component of a MEM. The mean and variance of the Weibull distribution and those of the inverse square root transformed distribution are calculated for σ=6, 7,.., 99, 100 with the corresponding values of n for which the mean of the untransformed distribution is equal to one. The paper concludes that the inverse square root would yield better results when using MEM with a Weibull-distributed error component and where data transformation is deemed necessary to stabilize the variance of the data set.","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"7 1","pages":"109"},"PeriodicalIF":0.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42564890","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

Additive-Multiplicative Rates Model for Recurrent Event Data with Intermittently Observed Time-Dependent Covariates. 具有间断性观测时变协变量的重复事件数据的加乘率模型。

Journal of data science : JDS Pub Date : 2021-10-01 Epub Date: 2021-11-04 DOI: 10.6339/21-jds1027

Tianmeng Lyu, Xianghua Luo, Yifei Sun

{"title":"Additive-Multiplicative Rates Model for Recurrent Event Data with Intermittently Observed Time-Dependent Covariates.","authors":"Tianmeng Lyu, Xianghua Luo, Yifei Sun","doi":"10.6339/21-jds1027","DOIUrl":"https://doi.org/10.6339/21-jds1027","url":null,"abstract":"<p><p>Regression methods, including the proportional rates model and additive rates model, have been proposed to evaluate the effect of covariates on the risk of recurrent events. These two models have different assumptions on the form of the covariate effects. A more flexible model, the additive-multiplicative rates model, is considered to allow the covariates to have both additive and multiplicative effects on the marginal rate of recurrent event process. However, its use is limited to the cases where the time-dependent covariates are monitored continuously throughout the follow-up time. In practice, time-dependent covariates are often only measured intermittently, which renders the current estimation method for the additive-multiplicative rates model inapplicable. In this paper, we propose a semiparametric estimator for the regression coefficients of the additive-multiplicative rates model to allow intermittently observed time-dependent covariates. We present the simulation results for the comparison between the proposed method and the simple methods, including last covariate carried forward and linear interpolation, and apply the proposed method to an epidemiologic study aiming to evaluate the effect of time-varying streptococcal infections on the risk of pharyngitis among school children. The R package implementing the proposed method is available at www.github.com/TianmengL/rectime.</p>","PeriodicalId":73699,"journal":{"name":"Journal of data science : JDS","volume":"19 4","pages":"615-633"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9232183/pdf/nihms-1761398.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40398395","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: 1

Sequence Mutations of Genes Pertaining to Malignancy in Cancer 恶性肿瘤相关基因的序列突变

Journal of data science : JDS Pub Date : 2021-07-30 DOI: 10.6339/jds.201310_11(4).0004

Nardnisa Sintupisut, Chen-Hsiang Yeang

引用次数: 0