Journal Of Statistics Research Articles

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 3

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 3

The Swiss Journal of Economics and Statistics at 160

The Swiss Journal of Economics and Statistics at 160

Pentagoning the Circle with Straightedge & Compass

This study idea came from the exact solution “Circling the Regular Pentagon with Straightedge & compass in Euclidean Geometry”, published by the Scholars Journal of Physics, Mathematics and Statistics on 22/08/2024 [1]. In this research, the ANALYSIS method is adopted to prove the process of solving this new challenge problem, which has not existed in the Mathematics field till today. The process is an inverse/converse solution solving the inverse problem “Circling the Regular Pentagon with Straightedge & compass in Euclidean Geometry” problem, using a straightedge & a compass. I hereby commit that this is my own personal research project.

On some publications of Sir David Cox

AbstractSir David Cox published four papers in the Scandinavian Journal of Statistics and two in the Scandinavian Actuarial Journal. This note provides some brief summaries of these papers.

Mapping socio-economic status using mixed data: a hierarchical Bayesian approach

Abstract We propose a Bayesian hierarchical model to estimate a socio-economic status (SES) index based on mixed dichotomous and continuous variables. In particular, we extend Quinn’s ([2004]. Bayesian factor analysis for mixed ordinal and continuous responses. Political Analysis, 12(4), 338–353. https://doi.org/10.1093/pan/mph022) and Schliep and Hoeting’s ([2013]. Multilevel latent Gaussian process model for mixed discrete and continuous multivariate response data. Journal of Agricultural, Biological, and Environmental Statistics, 18(4), 492–513. https://doi.org/10.1007/s13253-013-0136-z) factor analysis models for mixed dichotomous and continuous variables by allowing a spatial hierarchical structure of key parameters of the model. Unlike most SES assessment models proposed in the literature, the hierarchical nature of this model enables the use of census observations at the household level without needing to aggregate any information a priori. Therefore, it better accommodates the variability of the SES between census tracts and the number of households per area. The proposed model is used in the estimation of a socio-economic index using 10% of the 2010 Ghana census in the Greater Accra Metropolitan area. Out of the 20 observed variables, the number of people per room, access to water piping and flushable toilets differentiated high and low SES areas the best.

A Conversation With Marc Hallin

SummaryMarc Hallin was born in Ghent, Belgium, on 23 April 1949. He holds a Licence en Sciences mathématiques (1971), a Licence en Sciences actuarielles (1972), and a Doctorat en Sciences (1976) from the Université libre de Bruxelles. He then rose through the professorial ranks at the same institution, being successively Premier Assistant (1977–1978), Chargé de Cours associé (1978–1984), Chargé de Cours (1984–1988), Professeur ordinaire (1988–2009), and Professeur ordinaire émérite upon retirement in 2009. Throughout his career, he supervised 25 PhD students and held invited positions at many institutions of high standing in Austria, Belgium, England, France, Hong Kong, Italy, Portugal, Spain, Switzerland, and the USA (most notably Princeton). A renown expert in time series analysis, econometrics, and non‐parametric inference, Marc is the author or coauthor of over 250 research papers, for which he received numerous awards, including the Medal of the Faculty of Mathematics and Physics of Charles University in Prague (2006), a Humboldt Forschungspreis from the Alexander von Humboldt Foundation (2012), the Pierre‐Simon de Laplace Award of the Société française de Statistique (2022), and the Gottfried E. Noether Distinguished Scholar Award of the American Statistical Association (2022). He gave several distinguished lecture series, including the 2017 Hermann Otto Hirschfeld Lecture Series at the Humboldt Universität zu Berlin, and the 2018 Mahalanobis Memorial Lecture at the Indian Statistical Institute. Over the years, he co‐edited a dozen books and proceedings, and served on the editorial boards of several journals, including the Journal of Time Series Analysis (1994–2009), the Journal of Econometrics (2013–2019), the Journal of Business and Economic Statistics (2018–), and the Theory and Methods Section of the Journal of the American Statistical Association (2005–). He is a Fellow of the Institute of Mathematical Statistics (1990) and the American Statistical Association (1997), as well as a member of the Classe des Sciences of the Royal Academy of Belgium (1999). Marc has been a member of the International Statistical Institute since 1985 and was (co‐) Editor‐in‐Chief of the International Statistical Review from 2010 to 2015.

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 2

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 2

Corrigendum to “Shannon Entropy and Mutual Information for Multivariate Skew‐Elliptical Distributions” published in Scandinavian Journal of Statistics (2013), vol. 40, pp. 42–62

Corrigendum to “Shannon Entropy and Mutual Information for Multivariate Skew‐Elliptical Distributions” published in Scandinavian Journal of Statistics (2013), vol. 40, pp. 42–62

Educational Attainment, Household Income and Wealth Accumulation in Vietnam

This study examines the effects of educational attainment and household income on wealth accumulation in Vietnam using various Vietnam Household Living Standards Surveys (VHLSSs) in 2008, 2018 and 2020. We use the threshold regression to separate households into different groups depending on their assets. We find that owning a university degree is optimal to maximize wealth accumulation for households in 2008. However, post-graduate qualifications are needed for wealth accumulation in 2018 and 2020. For those household heads without a university degree, owning advanced training certificates provides the best opportunity to accumulate household wealth in all three surveys. Besides, our empirical results confirm that household income played no role in wealth accommodation in 2008. Interestingly, household income positively contributes to wealth accumulation for households with assets above the threshold of VND 634.40 m in 2020. However, the effect is negative for households with assets below the threshold of VND 440.25 m in 2018. We also find that households with widowed or divorced household heads are associated with lower wealth accumulation. Another interesting finding is that living in urban areas reduces wealth accumulation for households whose assets are below VND 440.25 m in 2018. These findings largely remain unchanged when Lewbel’s (Lewbel, Journal of Business and Economic Statistics 30:67–80, 2012) instrument variable estimator is used to ensure the robustness of the empirical results.

A Bayesian method for addressing multinomial misclassification with applications for alcohol epidemiological modeling

In this article, we describe a new command, bamm, that implements a Bayesian method for addressing misclassification in multinomial data; see Swartz et al. (2004, Canadian Journal of Statistics 32: 285–302). We also describe a postestimation command, bammdx, that was developed to provide additional estimation diagnostics. We describe the method and the new commands and then present results from both a simulation study demonstrating bamm’s performance under a known misclassification data-generating process and an empirical example from alcohol epidemiology modeling.

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 1

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 13, No. 1

Automated detection of edge clusters via an overfitted mixture prior

AbstractMost community detection methods focus on clustering actors with common features in a network. However, clustering edges offers a more intuitive way to understand the network structure in many real-life applications. Among the existing methods for network edge clustering, the majority are algorithmic, with the exception of the latent space edge clustering (LSEC) model proposed by Sewell (Journal of Computational and Graphical Statistics, 30(2), 390–405, 2021). LSEC was shown to have good performance in simulation and real-life data analysis, but fitting this model requires prior knowledge of the number of clusters and latent dimensions, which are often unknown to researchers. Within a Bayesian framework, we propose an extension to the LSEC model using a sparse finite mixture prior that supports automated selection of the number of clusters. We refer to our proposed approach as the automated LSEC or aLSEC. We develop a variational Bayes generalized expectation-maximization approach and a Hamiltonian Monte Carlo-within Gibbs algorithm for estimation. Our simulation study showed that aLSEC reduced run time by 10 to over 100 times compared to LSEC. Like LSEC, aLSEC maintains a computational cost that grows linearly with the number of actors in a network, making it scalable to large sparse networks. We developed the R package aLSEC which implements the proposed methodology.

Journal of Statistics and Data Science Education 2023 Associate Editors

Journal of Statistics and Data Science Education 2023 Associate Editors

Model averaged tail area confidence intervals in nested linear regression models

SummaryThe performance, in terms of coverage and expected length, of the model averaged tail area (MATA) confidence interval, proposed by Turek & Fletcher (2012, Computational Statistics & Data Analysis, 56, 2809–2815), depends greatly on the data‐based model weights used in its construction. We generalise the computationally convenient exact formulae due to Kabaila, Welsh & Abeysekera (2016, Scandinavian Journal of Statistics, 43, 35–48) for the coverage and expected length of this confidence interval for two nested linear regression models to the case of two or more nested linear regression models. This permits the numerical assessment of the performance, in terms of coverage probability and scaled expected length, of the MATA confidence interval for any given data‐based model weights in the context of three or more nested linear regression models. We illustrate this numerical assessment of performance of the MATA confidence interval, for model weights based on any given Generalised Information Criterion, in the context of three nested linear regression models using the real life ‘Cholesterol’ data. This provides a very informative further exploration of the influence of these model weights on the performance of this confidence interval.

Chilean Journal of Statistics: Introducing a new issue and new editors

Chilean Journal of Statistics: Introducing a new issue and new editors

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 5

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 5

Greenland, S. (2023). Divergence vs. decision P‐values: A distinction worth making in theory and keeping in practice. Scandinavian Journal of Statistics, 50, 1–35, https://onlinelibrary.wiley.com/doi/10.1111/sjos.12625

Greenland, S. (2023). Divergence vs. decision P‐values: A distinction worth making in theory and keeping in practice. <i>Scandinavian Journal of Statistics</i>, 50, 1–35, https://onlinelibrary.wiley.com/doi/10.1111/sjos.12625

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 4

Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 4

Statistics for ecologists: A frequentist and bayesian treatment of modern regression models By JohnFieberg (Ed.). 2022. An open‐source online textbook. pp. 526. $0.00 online: (https://fw8051statistics4ecologists.netlify.app/)

The Journal of Wildlife ManagementEarly View e22446 BOOK REVIEW Statistics for Ecologists: A Frequentist and Bayesian Treatment of Modern Regression ModelsBy John Fieberg (Ed.). 2022. An open-source online textbook. pp. 526. $0.00 online: (https://fw8051statistics4ecologists.netlify.app/) Conner A. Ties, Corresponding Author Conner A. Ties [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorBenjamin W. Hoose, Corresponding Author Benjamin W. Hoose [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorJustinn J. Jones, Corresponding Author Justinn J. Jones [email protected] Department of Ecology and Conservation Biology, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorLindsay A. Martinez, Corresponding Author Lindsay A. Martinez [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorJared Schlottman, Corresponding Author Jared Schlottman [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this author Conner A. Ties, Corresponding Author Conner A. Ties [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorBenjamin W. Hoose, Corresponding Author Benjamin W. Hoose [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: benjamin.hoose[email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorJustinn J. Jones, Corresponding Author Justinn J. Jones [email protected] Department of Ecology and Conservation Biology, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorLindsay A. Martinez, Corresponding Author Lindsay A. Martinez [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this authorJared Schlottman, Corresponding Author Jared Schlottman [email protected] Department of Rangeland, Wildlife, and Fisheries Management, Texas A&M University, College Station, Texas, 77840 USA Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected] Email: [email protected]Search for more papers by this author First published: 20 June 2023 https://doi.org/10.1002/jwmg.22446Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issuee22446 RelatedInformation

Posw: A command for the stepwise Neyman-orthogonal estimator

Inference for structural and treatment parameters while having high-dimensional covariates in the model is increasingly common. The Neyman-orthogonal (NO) estimators in Belloni, Chernozhukov, and Wei (2016, Journal of Business and Economic Statistics 34: 606–619) produce valid inferences for the parameters of interest while using generalized linear model lasso methods to select the covariates. Drukker and Liu (2022, Econometric Reviews 41: 1047–1076) extended the estimators in Belloni, Chernozhukov, and Wei (2016) by using a Bayesian information criterion stepwise method and a testing-stepwise method as the covariate selector. Drukker and Liu (2022) found a family of data-generating processes for which the NO estimator based on Bayesian information criterion stepwise produces much more reliable inferences than the lasso-based NO estimator. In this article, we describe the implementation of posw, a command for the stepwise-based NO estimator for the high-dimensional linear, logit, and Poisson models.