Self-exciting point processes allow modeling the temporal location of an event of interest, considering the history provided by previously observed events. This family of point processes is commonly used in several areas such as criminology, economics, or seismology, among others. The standard formulation of the self-exciting process implies assuming that the underlying stochastic process is dependent on its previous history over the entire period under analysis. In this paper, we consider the possibility of modeling a point pattern through a point process whose structure is not necessarily of self-exciting type at every instant or temporal interval. Specifically, we propose a mixture point process model that allows the point process to be either self-exciting or homogeneous Poisson, depending on the instant within the study period. The performance of this model is evaluated both through a simulation study and a case study. The results indicate that the model is able to detect the presence of instants in time, referred to as change points, where the nature of the process varies.
{"title":"Point process modeling through a mixture of homogeneous and self-exciting processes","authors":"Álvaro Briz-Redón, Jorge Mateu","doi":"10.1111/stan.12334","DOIUrl":"https://doi.org/10.1111/stan.12334","url":null,"abstract":"Self-exciting point processes allow modeling the temporal location of an event of interest, considering the history provided by previously observed events. This family of point processes is commonly used in several areas such as criminology, economics, or seismology, among others. The standard formulation of the self-exciting process implies assuming that the underlying stochastic process is dependent on its previous history over the entire period under analysis. In this paper, we consider the possibility of modeling a point pattern through a point process whose structure is not necessarily of self-exciting type at every instant or temporal interval. Specifically, we propose a mixture point process model that allows the point process to be either self-exciting or homogeneous Poisson, depending on the instant within the study period. The performance of this model is evaluated both through a simulation study and a case study. The results indicate that the model is able to detect the presence of instants in time, referred to as change points, where the nature of the process varies.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"289 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139483210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider inference about the parameter that determines the distribution of the data. In frequentist inference a very important and useful idea is that data reduction to a sufficient statistic does not lose any information about this parameter. We recall two justifications for this idea in frequentist inference. We then examine the extent to which these justifications carry over to conditional frequentist inference inference, which consists of carrying out frequentist inference conditional on an ancillary statistic. This examination shows that, in the context of conditional frequentist inference, first reducing data to a sufficient statistic is not always justified, so we should first condition on an ancillary statistic. Finally, we describe two types of practically-important statistical models that illustrate this finding.
{"title":"The concept of sufficiency in conditional frequentist inference","authors":"Paul Kabaila, A. H. Welsh","doi":"10.1111/stan.12333","DOIUrl":"https://doi.org/10.1111/stan.12333","url":null,"abstract":"We consider inference about the parameter that determines the distribution of the data. In frequentist inference a very important and useful idea is that data reduction to a sufficient statistic does not lose any information about this parameter. We recall two justifications for this idea in frequentist inference. We then examine the extent to which these justifications carry over to conditional frequentist inference inference, which consists of carrying out frequentist inference conditional on an ancillary statistic. This examination shows that, in the context of conditional frequentist inference, first reducing data to a sufficient statistic is not always justified, so we should first condition on an ancillary statistic. Finally, we describe two types of practically-important statistical models that illustrate this finding.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"10 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Collapsibility is a practical and useful technique for dimension reduction in multidimensional contingency tables. In this paper, we consider marginal log‐linear models for studying collapsibility and related aspects in such tables. These models generalize ordinary log‐linear and multivariate logistic models, besides several others. First, we obtain some characteristic properties of marginal log‐linear parameters. Then we define collapsibility and strict collapsibility of these parameters in a general sense. Several necessary and sufficient conditions for collapsibility and strict collapsibility are derived based on simple functions of only the cell probabilities, which are easily verifiable. These include results for an arbitrary set of marginal log‐linear parameters having some common effects. The connections of strict collapsibility to various forms of independence of the variables are explored. We analyze some real‐life datasets to illustrate the above results on collapsibility and strict collapsibility. Finally, we obtain a result relating parameters with the same effect, but different margins for an arbitrary table, and demonstrate smoothness of marginal log‐linear models under collapsibility conditions. This article is protected by copyright. All rights reserved.
{"title":"Marginal Log‐linear Parameters and their Collapsibility for Categorical Data","authors":"S. Ghosh, P. Vellaisamy","doi":"10.1111/stan.12332","DOIUrl":"https://doi.org/10.1111/stan.12332","url":null,"abstract":"Collapsibility is a practical and useful technique for dimension reduction in multidimensional contingency tables. In this paper, we consider marginal log‐linear models for studying collapsibility and related aspects in such tables. These models generalize ordinary log‐linear and multivariate logistic models, besides several others. First, we obtain some characteristic properties of marginal log‐linear parameters. Then we define collapsibility and strict collapsibility of these parameters in a general sense. Several necessary and sufficient conditions for collapsibility and strict collapsibility are derived based on simple functions of only the cell probabilities, which are easily verifiable. These include results for an arbitrary set of marginal log‐linear parameters having some common effects. The connections of strict collapsibility to various forms of independence of the variables are explored. We analyze some real‐life datasets to illustrate the above results on collapsibility and strict collapsibility. Finally, we obtain a result relating parameters with the same effect, but different margins for an arbitrary table, and demonstrate smoothness of marginal log‐linear models under collapsibility conditions. This article is protected by copyright. All rights reserved.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"11 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134993188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This note develops a generalized ‐variate hazard function for censored data in survival analysis. It introduces a generalized recursive formula, extending the bivariate and trivariate cases introduced by Clayton and Cuzick (1985, Journal of the Royal Statistical Society: Series A (General) , 148(2):82–108) and Kittani (1995, Journal of Mathematical Sciences , 67–74), respectively. The newly developed function is explicitly specified by association parameters and marginal hazard functions.
本文发展了生存分析中截尾数据的广义变量风险函数。它引入了一个广义的递归公式，扩展了Clayton和Cuzick (1985, Journal of Royal Statistical Society: Series a (General)， 148(2): 82-108)和Kittani (1995, Journal of Mathematical Sciences, 67-74)分别介绍的二元和三变量情况。该函数由关联参数和边际风险函数明确表示。
{"title":"Generalized K‐Variate Proportional Hazard Function For Censored Survival Data","authors":"Hilmi Fadel Kittani","doi":"10.1111/stan.12327","DOIUrl":"https://doi.org/10.1111/stan.12327","url":null,"abstract":"This note develops a generalized ‐variate hazard function for censored data in survival analysis. It introduces a generalized recursive formula, extending the bivariate and trivariate cases introduced by Clayton and Cuzick (1985, Journal of the Royal Statistical Society: Series A (General) , 148(2):82–108) and Kittani (1995, Journal of Mathematical Sciences , 67–74), respectively. The newly developed function is explicitly specified by association parameters and marginal hazard functions.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"74 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135088197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this paper we study how the expectations of power means behave asymptotically as some relevant parameter approaches infinity and how to approximate them accurately for general non‐negative continuous probability distributions. We derive approximation formulae for such expectations as distribution mean increases, and apply them to some commonly used distributions in statistics and financial mathematics. By numerical computations we demonstrate the accuracy of the proposed formulae which behave well even for smaller sample sizes. Furthermore, analysis of behaviour depending on sample size contributes to interesting connections with the power mean of probability distribution. This article is protected by copyright. All rights reserved.
{"title":"Asymptotic approximations of expectations of power means","authors":"Tomislav Buri, Lenka Mihokovi","doi":"10.1111/stan.12331","DOIUrl":"https://doi.org/10.1111/stan.12331","url":null,"abstract":"Abstract In this paper we study how the expectations of power means behave asymptotically as some relevant parameter approaches infinity and how to approximate them accurately for general non‐negative continuous probability distributions. We derive approximation formulae for such expectations as distribution mean increases, and apply them to some commonly used distributions in statistics and financial mathematics. By numerical computations we demonstrate the accuracy of the proposed formulae which behave well even for smaller sample sizes. Furthermore, analysis of behaviour depending on sample size contributes to interesting connections with the power mean of probability distribution. This article is protected by copyright. All rights reserved.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"45 s155","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An Intrinsic Gaussian Markov Random Field (IGMRF) can be used to induce conditional dependence in Bayesian hierarchical models. IGMRFs have both a precision matrix, which defines the neighborhood structure of the model, and a precision, or scaling, parameter. Previous studies have shown the importance of selecting the prior for this scaling parameter appropriately for different types of IGMRF, as it can have a substantial impact on posterior estimates. Here, we focus on cases in one and two dimensions, where tuning of the prior is achieved by mapping it to the marginal SD of an IGMRF of corresponding dimensionality. We compare the effects of scaling various IGMRFs, including an application to real two‐dimensional blood pressure data using MCMC methods.
{"title":"Scaling priors for Intrinsic Gaussian Markov Random Fields applied to blood pressure data","authors":"Maria‐Zafeiria Spyropoulou, James Bentham","doi":"10.1111/stan.12330","DOIUrl":"https://doi.org/10.1111/stan.12330","url":null,"abstract":"An Intrinsic Gaussian Markov Random Field (IGMRF) can be used to induce conditional dependence in Bayesian hierarchical models. IGMRFs have both a precision matrix, which defines the neighborhood structure of the model, and a precision, or scaling, parameter. Previous studies have shown the importance of selecting the prior for this scaling parameter appropriately for different types of IGMRF, as it can have a substantial impact on posterior estimates. Here, we focus on cases in one and two dimensions, where tuning of the prior is achieved by mapping it to the marginal SD of an IGMRF of corresponding dimensionality. We compare the effects of scaling various IGMRFs, including an application to real two‐dimensional blood pressure data using MCMC methods.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135585243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the main objectives of the time series analysis is forecasting, so both Machine Learning methods and statistical methods have been proposed in the literature. In this study, we compare the forecasting performance of some of these approaches. In addition to traditional forecasting methods, which are the Naive and Seasonal Naive Methods, S/ARIMA, Exponential Smoothing, TBATS, Bayesian Exponential Smoothing Models with Trend Modifications and STL Decomposition, the forecasts are also obtained using seven different machine learning methods, which are Random Forest, Support Vector Regression, XGBoosting, BNN, RNN, LSTM, and FFNN, and the hybridization of both statistical time series and machine learning methods. The data set is selected proportionally from various time domains in M4 Competition data set. Thereby, we aim to create a forecasting guide by considering different preprocessing approaches, methods, and data sets having various time domains. After the experiment, the performance and impact of all methods are discussed. Therefore, most of the best models are mainly selected from machine learning methods for forecasting. Moreover, the forecasting performance of the model is affected by both the time frequency and forecast horizon. Lastly, the study suggests that the hybrid approach is not always the best model for forecasting. Hence, this study provides guidelines to understand which method will perform better at different time series frequencies.
{"title":"Forecasting Performance of Machine Learning, Time Series and Hybrid Methods for Low and High Frequency Time Series","authors":"Ozancan Ozdemir, Ceylan Yozgatlıgil","doi":"10.1111/stan.12326","DOIUrl":"https://doi.org/10.1111/stan.12326","url":null,"abstract":"One of the main objectives of the time series analysis is forecasting, so both Machine Learning methods and statistical methods have been proposed in the literature. In this study, we compare the forecasting performance of some of these approaches. In addition to traditional forecasting methods, which are the Naive and Seasonal Naive Methods, S/ARIMA, Exponential Smoothing, TBATS, Bayesian Exponential Smoothing Models with Trend Modifications and STL Decomposition, the forecasts are also obtained using seven different machine learning methods, which are Random Forest, Support Vector Regression, XGBoosting, BNN, RNN, LSTM, and FFNN, and the hybridization of both statistical time series and machine learning methods. The data set is selected proportionally from various time domains in M4 Competition data set. Thereby, we aim to create a forecasting guide by considering different preprocessing approaches, methods, and data sets having various time domains. After the experiment, the performance and impact of all methods are discussed. Therefore, most of the best models are mainly selected from machine learning methods for forecasting. Moreover, the forecasting performance of the model is affected by both the time frequency and forecast horizon. Lastly, the study suggests that the hybrid approach is not always the best model for forecasting. Hence, this study provides guidelines to understand which method will perform better at different time series frequencies.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"180 S455","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This note presents a refined local approximation for the logarithm of the ratio between the negative multinomial probability mass function and a multivariate normal density, both having the same mean–covariance structure. This approximation, which is derived using Stirling's formula and a meticulous treatment of Taylor expansions, yields an upper bound on the Hellinger distance between the jittered negative multinomial distribution and the corresponding multivariate normal distribution. Upper bounds on the Le Cam distance between negative multinomial and multivariate normal experiments ensue.
{"title":"Asymptotic comparison of negative multinomial and multivariate normal experiments","authors":"Christian Genest, Frédéric Ouimet","doi":"10.1111/stan.12328","DOIUrl":"https://doi.org/10.1111/stan.12328","url":null,"abstract":"This note presents a refined local approximation for the logarithm of the ratio between the negative multinomial probability mass function and a multivariate normal density, both having the same mean–covariance structure. This approximation, which is derived using Stirling's formula and a meticulous treatment of Taylor expansions, yields an upper bound on the Hellinger distance between the jittered negative multinomial distribution and the corresponding multivariate normal distribution. Upper bounds on the Le Cam distance between negative multinomial and multivariate normal experiments ensue.","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"36 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136019643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In May 2023 a new editorial team, consisting of Edwin van den Heuvel, Veronica Vinciotti and myself, Ernst Wit, currently with the help of Casper Albers, have taken over from our predecessors, Nan van Geloven, Marijtje van Duijn, and Miroslav Ristic. We have immediately moved to a new format, of which we have informed you in the previous issue of Statistica Neerlandica, consisting of a fast-turnaround system with the aim of going from first submission to actual publication in two (!) months' time. Despite the efforts required on the part of the editors, associate editors and the publisher Wiley, the system seems to be working as intended. After the first half year, we are seeing the first fruits of this new approach. The first publications of the new regime are already available online, well within the 2-month target, and the number of quality submissions to Statistica Neerlandica is up. Clearly, we need to see how the system will develop in the future, but the first signs are encouraging. Besides the fast-turnaround system, we also want to diversify our offerings in the journal to better connect to the statistical society and the readers of Statistica Neerlandica. For eight decades we have only had the research paper, in which statistical researchers presented their research in a typical 20-page (or so) format. This trusted article type will obviously stay, as it a universal scientific currency with value for researchers and readers alike. From now on, however, we will also accept three new article types, in particular, Short Notes, Tutorials and State-of-the-Art reviews. Short Notes are short contributions that present a single, original, and significant discovery for rapid dissemination. They are aimed at informing colleagues about a finding, useful for the application, computation, methodology or theory of statistics. They should not be longer than 6–7 pages (2,500 words). These short notes can avoid long introductions or conclusions, and they do not need a lot of contextualization. They are little statistical gems, nice insights, useful mathematical tools that can aid the rest of the statistical community. Tutorials are aimed at introducing a general area of statistical theory, statistical methodology, statistical computing, statistical software, or an important application area of statistics. The tutorial should act as an introduction to those not already familiar with the area, and as a review to others. The mathematical level of each depends upon the topic. In all cases, however, the tutorial will strive for the broadest possible audience of researchers who comprise the readership of Statistica Neerlandica. Whereas tutorials are introductory, State-of-the-Art reviews focus on the latest developments. State-of-the-Art reviews aim at bringing an interested statistical audience up-to-date in an important and topical subject in the field of statistics with a focus on summarizing the latest advances. Although they present advanced material, th
2023年5月，一个由Edwin van den Heuvel, Veronica Vinciotti和我，Ernst Wit组成的新编辑团队，目前在Casper Albers的帮助下，接替了我们的前任Nan van Geloven, Marijtje van Duijn和Miroslav Ristic。我们已经立即移动到一个新的格式，其中我们已经通知你在前一个问题的统计荷兰，包括一个快速周转系统，从第一次提交到实际出版的目的在两个(!)个月的时间。尽管编辑、副编辑和出版商Wiley需要付出努力，但这个系统似乎正在按计划工作。上半年过去了，我们看到了这种新方法的初步成果。新制度的第一批出版物已经在网上发布，远远超过了2个月的目标，向荷兰统计局提交的高质量报告数量也在增加。显然，我们需要观察这个系统未来将如何发展，但最初的迹象令人鼓舞。除了快速周转系统，我们还希望使我们在期刊上的产品多样化，以便更好地与统计学会和《荷兰统计》的读者建立联系。80年来，我们只有研究论文，其中统计研究人员以典型的20页(左右)格式展示他们的研究。这种值得信赖的文章类型显然会继续存在，因为它是一种普遍的科学货币，对研究人员和读者都有价值。但是，从现在开始，我们还将接受3种新的文章类型，特别是Short Notes, tutorial和state -of-the- review。短文是一篇简短的文章，它提出了一个单一的、原创的、重要的发现，以便迅速传播。它们的目的是通知同事一个对应用、计算、方法或统计学理论有用的发现。简历不应超过6-7页(2500字)。这些简短的笔记可以避免冗长的介绍或结论，也不需要大量的语境化。它们是小小的统计瑰宝，有很好的见解，有用的数学工具，可以帮助统计社区的其他人。教程旨在介绍统计理论、统计方法、统计计算、统计软件或统计的重要应用领域的一般领域。对于那些不熟悉该领域的人来说，教程应该是一个介绍，同时也是对其他人的一种回顾。每个人的数学水平取决于主题。然而，在所有情况下，本教程都将争取尽可能广泛的研究人员读者，这些研究人员包括《荷兰统计》的读者。教程是介绍性的，而最新的评论则侧重于最新的发展。最新的评论旨在使有兴趣的统计读者了解统计领域中一个重要和热门主题的最新情况，重点是总结最新进展。虽然它们提供了先进的材料，但它们应该是专门和感兴趣的统计学家，而不仅仅是专家可以访问的。它们应该为感兴趣的研究人员提供一种方法，使他们能够迅速掌握某一统计主题的最新技术。因此，最先进的文章应该限制在大约12-13页(5000字)。在我们努力使《荷兰统计》再次成为Q1期刊的过程中，我们邀请社区向我们提供建议，向我们反馈我们的工作情况，当然，还请向期刊提交手稿，可以是研究论文，也可以是三种新格式之一:简短说明、教程或最新评论。我们期待着收到更多高质量的提交。
{"title":"New article types in <i>Statistica Neerlandica</i>","authors":"Ernst‐Jan Camiel Wit","doi":"10.1111/stan.12324","DOIUrl":"https://doi.org/10.1111/stan.12324","url":null,"abstract":"In May 2023 a new editorial team, consisting of Edwin van den Heuvel, Veronica Vinciotti and myself, Ernst Wit, currently with the help of Casper Albers, have taken over from our predecessors, Nan van Geloven, Marijtje van Duijn, and Miroslav Ristic. We have immediately moved to a new format, of which we have informed you in the previous issue of Statistica Neerlandica, consisting of a fast-turnaround system with the aim of going from first submission to actual publication in two (!) months' time. Despite the efforts required on the part of the editors, associate editors and the publisher Wiley, the system seems to be working as intended. After the first half year, we are seeing the first fruits of this new approach. The first publications of the new regime are already available online, well within the 2-month target, and the number of quality submissions to Statistica Neerlandica is up. Clearly, we need to see how the system will develop in the future, but the first signs are encouraging. Besides the fast-turnaround system, we also want to diversify our offerings in the journal to better connect to the statistical society and the readers of Statistica Neerlandica. For eight decades we have only had the research paper, in which statistical researchers presented their research in a typical 20-page (or so) format. This trusted article type will obviously stay, as it a universal scientific currency with value for researchers and readers alike. From now on, however, we will also accept three new article types, in particular, Short Notes, Tutorials and State-of-the-Art reviews. Short Notes are short contributions that present a single, original, and significant discovery for rapid dissemination. They are aimed at informing colleagues about a finding, useful for the application, computation, methodology or theory of statistics. They should not be longer than 6–7 pages (2,500 words). These short notes can avoid long introductions or conclusions, and they do not need a lot of contextualization. They are little statistical gems, nice insights, useful mathematical tools that can aid the rest of the statistical community. Tutorials are aimed at introducing a general area of statistical theory, statistical methodology, statistical computing, statistical software, or an important application area of statistics. The tutorial should act as an introduction to those not already familiar with the area, and as a review to others. The mathematical level of each depends upon the topic. In all cases, however, the tutorial will strive for the broadest possible audience of researchers who comprise the readership of Statistica Neerlandica. Whereas tutorials are introductory, State-of-the-Art reviews focus on the latest developments. State-of-the-Art reviews aim at bringing an interested statistical audience up-to-date in an important and topical subject in the field of statistics with a focus on summarizing the latest advances. Although they present advanced material, th","PeriodicalId":51178,"journal":{"name":"Statistica Neerlandica","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135995322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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