Pub Date : 2022-04-03DOI: 10.1080/07350015.2022.2041423
Peter Macdonald, E. Levina, Ji Zhu
We congratulate the authors on an interesting paper and on making an important contribution to the network analysis community through compiling a large new dataset which will spur further work on multilayer, dynamic and other complex network settings. This discussion focuses on the paper’s particular methods and applications in dynamic network analysis. Complexity of dynamic network data leads to many necessary analyst choices in both data processing and network modeling. Where possible, we will compare the choices made in this paper with other possibilities from recent literature on dynamic network analysis. One of the important points of the paper is that much of our network data has always been dynamic. For instance, communication networks consisting of sent and received E-mails come with time stamps, whether we choose to incorporate them or not. Developing statistical methods that take advantage of this time varying structure will lead to greater efficiency, novel insights, and generally allow us to take full advantage of rich modern datasets like the one featured in this paper.
{"title":"Discussion of “Co-citation and Co-authorship Networks of Statisticians” by Pengsheng Ji, Jiashun Jin, Zheng Tracy Ke, and Wanshan Li","authors":"Peter Macdonald, E. Levina, Ji Zhu","doi":"10.1080/07350015.2022.2041423","DOIUrl":"https://doi.org/10.1080/07350015.2022.2041423","url":null,"abstract":"We congratulate the authors on an interesting paper and on making an important contribution to the network analysis community through compiling a large new dataset which will spur further work on multilayer, dynamic and other complex network settings. This discussion focuses on the paper’s particular methods and applications in dynamic network analysis. Complexity of dynamic network data leads to many necessary analyst choices in both data processing and network modeling. Where possible, we will compare the choices made in this paper with other possibilities from recent literature on dynamic network analysis. One of the important points of the paper is that much of our network data has always been dynamic. For instance, communication networks consisting of sent and received E-mails come with time stamps, whether we choose to incorporate them or not. Developing statistical methods that take advantage of this time varying structure will lead to greater efficiency, novel insights, and generally allow us to take full advantage of rich modern datasets like the one featured in this paper.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41739556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-03DOI: 10.1080/07350015.2022.2055356
D. Donoho
I salute the authors for their gift to the world of this new dataset! They have clearly invested plenty of time, effort, and IQ points in the study of the statistics literature as a bibliometric laboratory, and our field will grow and develop because of this dataset, as well as methodology the authors developed and/or fine-tuned with those data. Strikingly, the article also conveys a great deal of enthusiasm for the data! This seems such a departure from the pattern of many articles in statistics today. The enthusiastic spirit reminds me of some classic work by great figures in the history of statistics, who often were fascinated by new kinds of data which were just becoming available in their day, and who were inspired by the new data to invent fundamental new statistical tools and mathematical machinery. Francis Galton was interested in the relationships between father’s height and son’s height, himself compiling an extensive bivariate dataset of such heights, leading to the invention of the bivariate normal distribution and the correlation coefficient. Time and time again, new types of data came first, new types of models and methodology later. Indeed, this seems almost inevitable. As new technologies come onstream, new kinds of measurements become available, and new settings for data analysis and statistical inference emerge. This is plain to see in recent decades, where computational biology produced gene expression data, DNA sequence data, SNP data, and RNA-Seq data, each new data type leading to interesting methodological challenges and scientific progress. For me, each effort by a statistics researcher to understand a newly available type of data enlarges our field; it should be a primary part of the career of statisticians to cultivate an interest in cultivating new types of datasets, so that new methodology can be discovered and developed.
{"title":"Data Come First: Discussion of “Co-citation and Co-authorship Networks of Statisticians”","authors":"D. Donoho","doi":"10.1080/07350015.2022.2055356","DOIUrl":"https://doi.org/10.1080/07350015.2022.2055356","url":null,"abstract":"I salute the authors for their gift to the world of this new dataset! They have clearly invested plenty of time, effort, and IQ points in the study of the statistics literature as a bibliometric laboratory, and our field will grow and develop because of this dataset, as well as methodology the authors developed and/or fine-tuned with those data. Strikingly, the article also conveys a great deal of enthusiasm for the data! This seems such a departure from the pattern of many articles in statistics today. The enthusiastic spirit reminds me of some classic work by great figures in the history of statistics, who often were fascinated by new kinds of data which were just becoming available in their day, and who were inspired by the new data to invent fundamental new statistical tools and mathematical machinery. Francis Galton was interested in the relationships between father’s height and son’s height, himself compiling an extensive bivariate dataset of such heights, leading to the invention of the bivariate normal distribution and the correlation coefficient. Time and time again, new types of data came first, new types of models and methodology later. Indeed, this seems almost inevitable. As new technologies come onstream, new kinds of measurements become available, and new settings for data analysis and statistical inference emerge. This is plain to see in recent decades, where computational biology produced gene expression data, DNA sequence data, SNP data, and RNA-Seq data, each new data type leading to interesting methodological challenges and scientific progress. For me, each effort by a statistics researcher to understand a newly available type of data enlarges our field; it should be a primary part of the career of statisticians to cultivate an interest in cultivating new types of datasets, so that new methodology can be discovered and developed.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47873137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-31DOI: 10.1080/07350015.2022.2058949
Sílvia Gonçalves, Ulrich Hounyo, Andrew J. Patton, Kevin Sheppard
Abstract This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital (CPU cycles) for labor (keeping track of derivatives). We show the consistency of the bootstrap distribution and consistency of bootstrap variance estimators, thereby justifying the use of bootstrap percentile intervals and bootstrap standard errors.
{"title":"Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models","authors":"Sílvia Gonçalves, Ulrich Hounyo, Andrew J. Patton, Kevin Sheppard","doi":"10.1080/07350015.2022.2058949","DOIUrl":"https://doi.org/10.1080/07350015.2022.2058949","url":null,"abstract":"Abstract This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital (CPU cycles) for labor (keeping track of derivatives). We show the consistency of the bootstrap distribution and consistency of bootstrap variance estimators, thereby justifying the use of bootstrap percentile intervals and bootstrap standard errors.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48606696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-28DOI: 10.1080/07350015.2022.2058000
Cem Çakmakl i, Hamza Demircan
Abstract We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants’ predictions, often used as a measure of “ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.
{"title":"Using Survey Information for Improving the Density Nowcasting of U.S. GDP","authors":"Cem Çakmakl i, Hamza Demircan","doi":"10.1080/07350015.2022.2058000","DOIUrl":"https://doi.org/10.1080/07350015.2022.2058000","url":null,"abstract":"Abstract We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants’ predictions, often used as a measure of “ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45575238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Confidence sets are of key importance in high-dimensional statistical inference. Under case–control study, a popular response-selective sampling design in medical study or econometrics, we consider the confidence intervals and statistical tests for single or low-dimensional parameters in high-dimensional logistic regression model. The asymptotic properties of the resulting estimators are established under mild conditions. We also study statistical tests for testing more general and complex hypotheses of the high-dimensional parameters. The general testing procedures are proved to be asymptotically exact and have satisfactory power. Numerical studies including extensive simulations and a real data example confirm that the proposed method performs well in practical settings.
{"title":"Post-selection Inference of High-dimensional Logistic Regression Under Case–Control Design","authors":"Yuanyuan Lin, Jinhan Xie, Ruijian Han, Niansheng Tang","doi":"10.1080/07350015.2022.2050245","DOIUrl":"https://doi.org/10.1080/07350015.2022.2050245","url":null,"abstract":"Abstract Confidence sets are of key importance in high-dimensional statistical inference. Under case–control study, a popular response-selective sampling design in medical study or econometrics, we consider the confidence intervals and statistical tests for single or low-dimensional parameters in high-dimensional logistic regression model. The asymptotic properties of the resulting estimators are established under mild conditions. We also study statistical tests for testing more general and complex hypotheses of the high-dimensional parameters. The general testing procedures are proved to be asymptotically exact and have satisfactory power. Numerical studies including extensive simulations and a real data example confirm that the proposed method performs well in practical settings.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46605875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-10DOI: 10.1080/07350015.2022.2051520
Mingjing Chen
Abstract To extract the common factors from grouped data, multilevel factor models have been put forward in the literature, and methods based on iterative principal component analysis (PCA) and canonical correlation analysis (CCA) have been proposed for estimation purpose. While iterative PCA requires iteration and is hence time-consuming, CCA can only deal with two groups of data. Herein, we develop two new methods to address these problems. We first extract the factors within groups and then project the estimated group factors into the space spanned by them in a circular manner. We propose two projection processes to estimate the common factors and determine the number of them. The new methods do not require iteration and are thus computationally efficient. They can estimate the common factors for multiple groups of data in a uniform way, regardless of whether the number of groups is large or small. They not only overcome the drawbacks of CCA but also nest the CCA method as a special case. Finally, we theoretically and numerically study the consistency properties of these new methods and apply them to studying international business cycles and the comovements of retail prices.
{"title":"Circularly Projected Common Factors for Grouped Data","authors":"Mingjing Chen","doi":"10.1080/07350015.2022.2051520","DOIUrl":"https://doi.org/10.1080/07350015.2022.2051520","url":null,"abstract":"Abstract To extract the common factors from grouped data, multilevel factor models have been put forward in the literature, and methods based on iterative principal component analysis (PCA) and canonical correlation analysis (CCA) have been proposed for estimation purpose. While iterative PCA requires iteration and is hence time-consuming, CCA can only deal with two groups of data. Herein, we develop two new methods to address these problems. We first extract the factors within groups and then project the estimated group factors into the space spanned by them in a circular manner. We propose two projection processes to estimate the common factors and determine the number of them. The new methods do not require iteration and are thus computationally efficient. They can estimate the common factors for multiple groups of data in a uniform way, regardless of whether the number of groups is large or small. They not only overcome the drawbacks of CCA but also nest the CCA method as a special case. Finally, we theoretically and numerically study the consistency properties of these new methods and apply them to studying international business cycles and the comovements of retail prices.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41253312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-25DOI: 10.1080/07350015.2022.2139267
Rub'en Loaiza-Maya, D. Nibbering
Abstract The multinomial probit model is often used to analyze choice behavior. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice datasets. This article proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for the unnormalized posterior density and an adequate choice of variational family. Both are challenging to specify in a multinomial probit, which has a posterior that requires identifying restrictions and is augmented with a large set of latent utilities. We employ a spherical transformation on the covariance matrix of the latent utilities to construct an unnormalized augmented posterior that identifies the parameters, and use the conditional posterior of the latent utilities as part of the variational family. The proposed method is faster than MCMC, and can be made scalable to both a large number of choice alternatives and a large number of observations. The accuracy and scalability of our method is illustrated in numerical experiments and real purchase data with one million observations.
{"title":"Fast Variational Bayes Methods for Multinomial Probit Models","authors":"Rub'en Loaiza-Maya, D. Nibbering","doi":"10.1080/07350015.2022.2139267","DOIUrl":"https://doi.org/10.1080/07350015.2022.2139267","url":null,"abstract":"Abstract The multinomial probit model is often used to analyze choice behavior. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice datasets. This article proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for the unnormalized posterior density and an adequate choice of variational family. Both are challenging to specify in a multinomial probit, which has a posterior that requires identifying restrictions and is augmented with a large set of latent utilities. We employ a spherical transformation on the covariance matrix of the latent utilities to construct an unnormalized augmented posterior that identifies the parameters, and use the conditional posterior of the latent utilities as part of the variational family. The proposed method is faster than MCMC, and can be made scalable to both a large number of choice alternatives and a large number of observations. The accuracy and scalability of our method is illustrated in numerical experiments and real purchase data with one million observations.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45179652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-24DOI: 10.1080/07350015.2022.2044829
Kerem Tuzcuoglu
Abstract Modeling and estimating autocorrelated discrete data can be challenging. In this article, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve a much lower order of integration. However, parameter identification might potentially become problematic since the information employed in lower dimensional distributions may not be rich enough for identification. Therefore, we characterize types of composite likelihoods that are valid for this model and study conditions under which the parameters can be identified. Moreover, we provide consistency and asymptotic normality results for two different composite likelihood estimators and conduct Monte Carlo studies to assess their finite-sample performances. Finally, we apply our method to analyze corporate bond ratings. Supplementary materials for this article are available online.
{"title":"Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects","authors":"Kerem Tuzcuoglu","doi":"10.1080/07350015.2022.2044829","DOIUrl":"https://doi.org/10.1080/07350015.2022.2044829","url":null,"abstract":"Abstract Modeling and estimating autocorrelated discrete data can be challenging. In this article, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve a much lower order of integration. However, parameter identification might potentially become problematic since the information employed in lower dimensional distributions may not be rich enough for identification. Therefore, we characterize types of composite likelihoods that are valid for this model and study conditions under which the parameters can be identified. Moreover, we provide consistency and asymptotic normality results for two different composite likelihood estimators and conduct Monte Carlo studies to assess their finite-sample performances. Finally, we apply our method to analyze corporate bond ratings. Supplementary materials for this article are available online.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47497050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-22DOI: 10.1080/07350015.2022.2044335
X. Zhu, E. Kolaczyk
We thank the authors for their new contribution to a high quality dataset and interesting findings from the modeling and analysis of the co-citation and co-authorship networks of statisticians. Leveraging this dataset, there are lots of additional questions that might be answered, and analyses done. Network motif analysis is one such, with roots in the triad census of traditional social network analysis (Wasserman and Faust 1994, chap. 14.2.1) and first introduced in its modern form by Milo et al. (2002) in systems biology. It has since been applied to various scientific domains, for example, social science, neuroscience, to study network structures and the underlying complex systems (see Stone, Simberloff, and Artzy-Randrup (2019) for a survey article). While the notion of network motif was originally defined for static networks as small subgraph patterns occurring frequently in a given network, several ways have been proposed to extend it to dynamic networks consisting of a set of vertices and a collection of timestamped edges. One widely used one is from Paranjape, Benson, and Leskovec (2017), where temporal motifs are defined as an ordered sequence of timestamped edges among a subset of nodes conforming to a specified pattern as well as a specified duration of time δ in which the edges must occur. In contrast to their static counterparts, such temporal motifs take into account not only subgraph isomorphism but also edge ordering and duration, which can be regarded as the simple building blocks for temporal structures of dynamic networks. There are a few works in the literature on motif analysis for journal citation networks (Wu, Han, and Li 2008; Zeng and Rong 2021) and author collaboration networks (Chakraborty, Ganguly, and Mukherjee 2015), but none of them seem to be from the perspective of temporal motifs. In this discussion, we construct temporal citation networks among statisticians using the publication data provided in the article, and focus on analyzing the frequency and distribution of temporal motifs in such dynamic networks. This analysis provides initial insights into the temporal patterns of citing behaviors among authors of various statistics journals from 1975 to 2015.
{"title":"Discussion of “Co-citation and Co-authorship Networks of Statisticians”","authors":"X. Zhu, E. Kolaczyk","doi":"10.1080/07350015.2022.2044335","DOIUrl":"https://doi.org/10.1080/07350015.2022.2044335","url":null,"abstract":"We thank the authors for their new contribution to a high quality dataset and interesting findings from the modeling and analysis of the co-citation and co-authorship networks of statisticians. Leveraging this dataset, there are lots of additional questions that might be answered, and analyses done. Network motif analysis is one such, with roots in the triad census of traditional social network analysis (Wasserman and Faust 1994, chap. 14.2.1) and first introduced in its modern form by Milo et al. (2002) in systems biology. It has since been applied to various scientific domains, for example, social science, neuroscience, to study network structures and the underlying complex systems (see Stone, Simberloff, and Artzy-Randrup (2019) for a survey article). While the notion of network motif was originally defined for static networks as small subgraph patterns occurring frequently in a given network, several ways have been proposed to extend it to dynamic networks consisting of a set of vertices and a collection of timestamped edges. One widely used one is from Paranjape, Benson, and Leskovec (2017), where temporal motifs are defined as an ordered sequence of timestamped edges among a subset of nodes conforming to a specified pattern as well as a specified duration of time δ in which the edges must occur. In contrast to their static counterparts, such temporal motifs take into account not only subgraph isomorphism but also edge ordering and duration, which can be regarded as the simple building blocks for temporal structures of dynamic networks. There are a few works in the literature on motif analysis for journal citation networks (Wu, Han, and Li 2008; Zeng and Rong 2021) and author collaboration networks (Chakraborty, Ganguly, and Mukherjee 2015), but none of them seem to be from the perspective of temporal motifs. In this discussion, we construct temporal citation networks among statisticians using the publication data provided in the article, and focus on analyzing the frequency and distribution of temporal motifs in such dynamic networks. This analysis provides initial insights into the temporal patterns of citing behaviors among authors of various statistics journals from 1975 to 2015.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48692748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-22DOI: 10.1080/07350015.2022.2044828
J. Loyal, Yuguo Chen
We want to congratulate the authors on a fascinating article containing an insightful analysis and their hard work curating the high-quality co-citation and co-authorship networks. These datasets alone are a valuable contribution to the statistics profes-sion, which will undoubtedly inspire future data science projects and advances in methodology. In fact, we are eager to use these networks in our own classrooms and research. Furthermore, the authors use these networks to tackling exciting questions in network science that go beyond the familiar problems of edge imputation and predicting node labels. In doing so, the authors perform a terrific analysis accompanied by exciting new methodology. This analysis serves as a great first step in understanding these networks, and the ideas initiated in this article will certainly stimulate many further research questions. For how do influence the research Or,
{"title":"Discussion of “Co-citation and Co-authorship Networks of Statisticians”","authors":"J. Loyal, Yuguo Chen","doi":"10.1080/07350015.2022.2044828","DOIUrl":"https://doi.org/10.1080/07350015.2022.2044828","url":null,"abstract":"We want to congratulate the authors on a fascinating article containing an insightful analysis and their hard work curating the high-quality co-citation and co-authorship networks. These datasets alone are a valuable contribution to the statistics profes-sion, which will undoubtedly inspire future data science projects and advances in methodology. In fact, we are eager to use these networks in our own classrooms and research. Furthermore, the authors use these networks to tackling exciting questions in network science that go beyond the familiar problems of edge imputation and predicting node labels. In doing so, the authors perform a terrific analysis accompanied by exciting new methodology. This analysis serves as a great first step in understanding these networks, and the ideas initiated in this article will certainly stimulate many further research questions. For how do influence the research Or,","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46485523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：