Abstract In this article, we propose a robust approach against heteroscedasticity, error serial correlation and slope heterogeneity in linear models with interactive effects for large panel data. First, consistency and asymptotic normality of the pooled iterated principal component (IPC) estimator for random coefficient and homogeneous slope models are established. Then, we prove the asymptotic validity of the associated Wald test for slope parameter restrictions based on the panel heteroscedasticity and autocorrelation consistent (PHAC) variance matrix estimator for both random coefficient and homogeneous slope models, which does not require the Newey-West type time-series parameter truncation. These results asymptotically justify the use of the same pooled IPC estimator and the PHAC standard error for both homogeneous-slope and heterogeneous-slope models. This robust approach can significantly reduce the model selection uncertainty for applied researchers. In addition, we propose a Lagrange Multiplier (LM) test for correlated random coefficients with covariates. This test has nontrivial power against correlated random coefficients, but not for random coefficients and homogeneous slopes. The LM test is important because the IPC estimator becomes inconsistent with correlated random coefficients. The finite sample evidence and an empirical application support the reliability and the usefulness of our robust approach.
{"title":"A robust approach to heteroskedasticity, error serial correlation and slope heterogeneity in linear models with interactive effects for large panel data","authors":"Guowei Cui, Kazuhiko Hayakawa, Shuichi Nagata, Takashi Yamagata","doi":"10.1080/07350015.2022.2077349","DOIUrl":"https://doi.org/10.1080/07350015.2022.2077349","url":null,"abstract":"Abstract In this article, we propose a robust approach against heteroscedasticity, error serial correlation and slope heterogeneity in linear models with interactive effects for large panel data. First, consistency and asymptotic normality of the pooled iterated principal component (IPC) estimator for random coefficient and homogeneous slope models are established. Then, we prove the asymptotic validity of the associated Wald test for slope parameter restrictions based on the panel heteroscedasticity and autocorrelation consistent (PHAC) variance matrix estimator for both random coefficient and homogeneous slope models, which does not require the Newey-West type time-series parameter truncation. These results asymptotically justify the use of the same pooled IPC estimator and the PHAC standard error for both homogeneous-slope and heterogeneous-slope models. This robust approach can significantly reduce the model selection uncertainty for applied researchers. In addition, we propose a Lagrange Multiplier (LM) test for correlated random coefficients with covariates. This test has nontrivial power against correlated random coefficients, but not for random coefficients and homogeneous slopes. The LM test is important because the IPC estimator becomes inconsistent with correlated random coefficients. The finite sample evidence and an empirical application support the reliability and the usefulness of our robust approach.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"1 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59995703","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 : 2022-05-11DOI: 10.1080/07350015.2022.2075370
Gustav Alfelt, Taras Bodnar, F. Javed, J. Tyrcha
Abstract Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in nonsingular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by construction. Motivated by this situation, we introduce the Singular Conditional Autoregressive Wishart (SCAW) model to capture the temporal dynamics of time series of singular realized covariance matrices, extending the rich literature on econometric Wishart time series models to the singular case. This model is furthermore developed by covariance targeting adapted to matrices and a sector wise BEKK-specification, allowing excellent scalability to large and extremely large portfolio sizes. Finally, the model is estimated to a 20-year long time series containing 50 stocks and to a 10-year long time series containing 300 stocks, and evaluated using out-of-sample forecast accuracy. It outperforms the benchmark models with high statistical significance and the parsimonious specifications perform better than the baseline SCAW model, while using considerably less parameters.
{"title":"Singular Conditional Autoregressive Wishart Model for Realized Covariance Matrices","authors":"Gustav Alfelt, Taras Bodnar, F. Javed, J. Tyrcha","doi":"10.1080/07350015.2022.2075370","DOIUrl":"https://doi.org/10.1080/07350015.2022.2075370","url":null,"abstract":"Abstract Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in nonsingular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by construction. Motivated by this situation, we introduce the Singular Conditional Autoregressive Wishart (SCAW) model to capture the temporal dynamics of time series of singular realized covariance matrices, extending the rich literature on econometric Wishart time series models to the singular case. This model is furthermore developed by covariance targeting adapted to matrices and a sector wise BEKK-specification, allowing excellent scalability to large and extremely large portfolio sizes. Finally, the model is estimated to a 20-year long time series containing 50 stocks and to a 10-year long time series containing 300 stocks, and evaluated using out-of-sample forecast accuracy. It outperforms the benchmark models with high statistical significance and the parsimonious specifications perform better than the baseline SCAW model, while using considerably less parameters.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"833 - 845"},"PeriodicalIF":3.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42036077","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 : 2022-05-06DOI: 10.1080/07350015.2022.2074426
Shuyuan Wu, Danyang Huang, Hansheng Wang
Abstract We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed network structure without a central master. This greatly enhances the reliability of the entire algorithm. Those nice properties inspire us to carefully study the NGD method both theoretically and numerically. Theoretically, we start with a classical linear regression model. We find that both the learning rate and the network structure play significant roles in determining the NGD estimator’s statistical efficiency. The resulting NGD estimator can be statistically as efficient as the global estimator, if the learning rate is sufficiently small and the network structure is weakly balanced, even if the data are distributed heterogeneously. Those interesting findings are then extended to general models and loss functions. Extensive numerical studies are presented to corroborate our theoretical findings. Classical deep learning models are also presented for illustration purpose.
{"title":"Network Gradient Descent Algorithm for Decentralized Federated Learning","authors":"Shuyuan Wu, Danyang Huang, Hansheng Wang","doi":"10.1080/07350015.2022.2074426","DOIUrl":"https://doi.org/10.1080/07350015.2022.2074426","url":null,"abstract":"Abstract We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed network structure without a central master. This greatly enhances the reliability of the entire algorithm. Those nice properties inspire us to carefully study the NGD method both theoretically and numerically. Theoretically, we start with a classical linear regression model. We find that both the learning rate and the network structure play significant roles in determining the NGD estimator’s statistical efficiency. The resulting NGD estimator can be statistically as efficient as the global estimator, if the learning rate is sufficiently small and the network structure is weakly balanced, even if the data are distributed heterogeneously. Those interesting findings are then extended to general models and loss functions. Extensive numerical studies are presented to corroborate our theoretical findings. Classical deep learning models are also presented for illustration purpose.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"806 - 818"},"PeriodicalIF":3.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45116396","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 : 2022-04-22DOI: 10.1080/07350015.2022.2067546
Y. Kaddoura, J. Westerlund
Abstract In this article, we propose a new estimator of panel data models with random interactive effects and multiple structural breaks that is suitable when the number of time periods, T, is fixed and only the number of cross-sectional units, N, is large. This is done by viewing the determination of the breaks as a shrinkage problem, and to estimate both the regression coefficients, and the number of breaks and their locations by applying a version of the Lasso approach. We show that with probability approaching one the approach can correctly determine the number of breaks and the dates of these breaks, and that the estimator of the regime-specific regression coefficients is consistent and asymptotically normal. We also provide Monte Carlo results suggesting that the approach performs very well in small samples, and empirical results suggesting that while the coefficients of the controls are breaking, the coefficients of the main deterrence regressors in a model of crime are not.
{"title":"Estimation of Panel Data Models with Random Interactive Effects and Multiple Structural Breaks when T is Fixed","authors":"Y. Kaddoura, J. Westerlund","doi":"10.1080/07350015.2022.2067546","DOIUrl":"https://doi.org/10.1080/07350015.2022.2067546","url":null,"abstract":"Abstract In this article, we propose a new estimator of panel data models with random interactive effects and multiple structural breaks that is suitable when the number of time periods, T, is fixed and only the number of cross-sectional units, N, is large. This is done by viewing the determination of the breaks as a shrinkage problem, and to estimate both the regression coefficients, and the number of breaks and their locations by applying a version of the Lasso approach. We show that with probability approaching one the approach can correctly determine the number of breaks and the dates of these breaks, and that the estimator of the regime-specific regression coefficients is consistent and asymptotically normal. We also provide Monte Carlo results suggesting that the approach performs very well in small samples, and empirical results suggesting that while the coefficients of the controls are breaking, the coefficients of the main deterrence regressors in a model of crime are not.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"778 - 790"},"PeriodicalIF":3.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48853636","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 : 2022-04-19DOI: 10.1080/07350015.2022.2067545
Lars Spreng, G. Urga
Abstract In this article, we propose an intersection-union test for multivariate forecast accuracy based on the combination of a sequence of univariate tests. The testing framework evaluates a global null hypothesis of equal predictive ability using any number of univariate forecast accuracy tests under arbitrary dependence structures, without specifying the underlying multivariate distribution. An extensive Monte Carlo simulation exercise shows that our proposed test has very good size and power properties under several relevant scenarios, and performs well in both low- and high-dimensional settings. We illustrate the empirical validity of our testing procedure using a large dataset of 84 daily exchange rates running from January 1, 2011 to April 1, 2021. We show that our proposed test addresses inconclusive results that often arise in practice.
{"title":"Combining p-values for Multivariate Predictive Ability Testing","authors":"Lars Spreng, G. Urga","doi":"10.1080/07350015.2022.2067545","DOIUrl":"https://doi.org/10.1080/07350015.2022.2067545","url":null,"abstract":"Abstract In this article, we propose an intersection-union test for multivariate forecast accuracy based on the combination of a sequence of univariate tests. The testing framework evaluates a global null hypothesis of equal predictive ability using any number of univariate forecast accuracy tests under arbitrary dependence structures, without specifying the underlying multivariate distribution. An extensive Monte Carlo simulation exercise shows that our proposed test has very good size and power properties under several relevant scenarios, and performs well in both low- and high-dimensional settings. We illustrate the empirical validity of our testing procedure using a large dataset of 84 daily exchange rates running from January 1, 2011 to April 1, 2021. We show that our proposed test addresses inconclusive results that often arise in practice.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"765 - 777"},"PeriodicalIF":3.0,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44254877","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 : 2022-04-08DOI: 10.1080/07350015.2022.2063132
Simon C. Smith
Abstract Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. To this end, we develop a new Bayesian methodology to estimate and formally test panel regression models in the presence of multiple breaks and unobserved regime-specific grouped heterogeneity. In an empirical application to forecasting inflation rates across 20 U.S. industries, our method generates significantly more accurate forecasts relative to a range of popular methods.
{"title":"Structural Breaks in Grouped Heterogeneity","authors":"Simon C. Smith","doi":"10.1080/07350015.2022.2063132","DOIUrl":"https://doi.org/10.1080/07350015.2022.2063132","url":null,"abstract":"Abstract Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. To this end, we develop a new Bayesian methodology to estimate and formally test panel regression models in the presence of multiple breaks and unobserved regime-specific grouped heterogeneity. In an empirical application to forecasting inflation rates across 20 U.S. industries, our method generates significantly more accurate forecasts relative to a range of popular methods.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"41 1","pages":"752 - 764"},"PeriodicalIF":3.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48059904","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: 10.1080/07350015.2022.2055358
Pengsheng Ji, Jiashun Jin, Z. Ke, Wanshan Li
We thank David Donoho for very encouraging comments. As always, his penetrating vision and deep thoughts are extremely stimulating. We are glad that he summarizes a major philosophical difference between statistics in earlier years (e.g., the time of Francis Galton) and statistics in our time by just a few words: data-first versus model-first. We completely agree with his comment that “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”; these are exactly the motivations underlying our (several-year) efforts in collecting, cleaning, and analyzing a large-scale high-quality dataset. We would like to add that both traditions have strengths, and combining the strengths of two sides may greatly help statisticians deal with the so-called crisis of the 21st century in statistics we face today. Let us explain the crisis above first. In the model-first tradition, with a particular application problem in mind, we propose a model, develop a method and justify its optimality by some hard-to-prove theorems, and find a dataset to support the approach. In this tradition, we put a lot of faith on our model and our theory: we hope the model is adequate, and we hope our optimality theory warrants the superiority of our method over others. Modern machine learning literature (especially the recent development of deep learning) provides a different approach to justifying the “superiority” of an approach; we compare the proposed approach with existing approaches by the real data results over a dozen of benchmark datasets. To choose an algorithm for their dataset, a practitioner does not necessarily need warranties from a theorem; a superior performance over many benchmark datasets says it all. To some theoretical statisticians, this is rather disappointing, as they come from a long
{"title":"Rejoinder: “Co-citation and Co-authorship Networks of Statisticians”","authors":"Pengsheng Ji, Jiashun Jin, Z. Ke, Wanshan Li","doi":"10.1080/07350015.2022.2055358","DOIUrl":"https://doi.org/10.1080/07350015.2022.2055358","url":null,"abstract":"We thank David Donoho for very encouraging comments. As always, his penetrating vision and deep thoughts are extremely stimulating. We are glad that he summarizes a major philosophical difference between statistics in earlier years (e.g., the time of Francis Galton) and statistics in our time by just a few words: data-first versus model-first. We completely agree with his comment that “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”; these are exactly the motivations underlying our (several-year) efforts in collecting, cleaning, and analyzing a large-scale high-quality dataset. We would like to add that both traditions have strengths, and combining the strengths of two sides may greatly help statisticians deal with the so-called crisis of the 21st century in statistics we face today. Let us explain the crisis above first. In the model-first tradition, with a particular application problem in mind, we propose a model, develop a method and justify its optimality by some hard-to-prove theorems, and find a dataset to support the approach. In this tradition, we put a lot of faith on our model and our theory: we hope the model is adequate, and we hope our optimality theory warrants the superiority of our method over others. Modern machine learning literature (especially the recent development of deep learning) provides a different approach to justifying the “superiority” of an approach; we compare the proposed approach with existing approaches by the real data results over a dozen of benchmark datasets. To choose an algorithm for their dataset, a practitioner does not necessarily need warranties from a theorem; a superior performance over many benchmark datasets says it all. To some theoretical statisticians, this is rather disappointing, as they come from a long","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"40 1","pages":"499 - 504"},"PeriodicalIF":3.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41393404","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: 10.1080/07350015.2022.2037432
Haolei Weng, Yang Feng
Abstract We congratulate the authors for their stimulating and thought-provoking work on network data analysis. In the article, the authors not only introduce a new large-scale and high-quality publication dataset that will surely become an important benchmark for further network research, but also present novel statistical methods and modeling which lead to very interesting findings about the statistics community. There is much material for thought and exploration. In this discussion, we will focus on the cocitation networks, and discuss a few points for the coauthorship networks toward the end.
{"title":"Discussion of “Cocitation and Coauthorship Networks of Statisticians”","authors":"Haolei Weng, Yang Feng","doi":"10.1080/07350015.2022.2037432","DOIUrl":"https://doi.org/10.1080/07350015.2022.2037432","url":null,"abstract":"Abstract We congratulate the authors for their stimulating and thought-provoking work on network data analysis. In the article, the authors not only introduce a new large-scale and high-quality publication dataset that will surely become an important benchmark for further network research, but also present novel statistical methods and modeling which lead to very interesting findings about the statistics community. There is much material for thought and exploration. In this discussion, we will focus on the cocitation networks, and discuss a few points for the coauthorship networks toward the end.","PeriodicalId":50247,"journal":{"name":"Journal of Business & Economic Statistics","volume":"40 1","pages":"486 - 490"},"PeriodicalIF":3.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48242562","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: 10.1080/07350015.2022.2041423
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":"40 1","pages":"492 - 493"},"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":"40 1","pages":"491 - 491"},"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}
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：