Review of "“Evidence-Based Statistics" by Cahusac

In press, Journal of the Royal Statistical Society: Series A (Statistics in Society) Review of "“Evidence-Based Statistics: An Introduction to the Evidential Approach - from Likelihood Principle to Statistical Practice”; Cahusac, Peter "

A Bibliometric Analysis of the 35th anniversary of the paper "The Statistical Analysis of Compositional Data" by John Aitchison (1982)

This study presents a comprehensive bibliometric analysis of the paper published by John Aitchison in the Journal of the Royal Statistical Society. Series B (Methodological) in 1982. Having recently reached the milestone of 35 years since its publication, this pioneering paper was the first to illustrate the use of the methodology "Compositional Data Analysis" or "CoDA". By October 2019, this paper had received over 780 citations, making it the most widely cited and influential article among those using said methodology. The bibliometric approach used in this study encompasses a wide range of techniques, including a specific analysis of the main authors and institutions to have cited Aitchison' paper. The VOSviewer software was also used for the purpose of developing network maps for said publication. Specifically, the techniques used were co-citations and bibliographic coupling. The results clearly show the significant impact the paper has had on scientific research, having been cited by authors and institutions that publish all around the world.

Spatial survival modelling of business re-opening after Katrina: Survival modelling compared to spatial probit modelling of re-opening within 3, 6 or 12 months

Zhou and Hanson; Zhou and Hanson; Zhou and Hanson ( 2015 , Nonparametric Bayesian Inference in Biostatistics, pages 215–46. Cham: Springer; 2018, Journal of the American Statistical Association, 113, 571–81; 2020, spBayesSurv: Bayesian Modeling and Analysis of Spatially Correlated Survival Data. R package version 1.1.4) and Zhou et al. (2020, Journal of Statistical Software, Articles, 92, 1–33) present methods for estimating spatial survival models using areal data. This article applies their methods to a dataset recording New Orleans business decisions to re-open after Hurricane Katrina; the data were included in LeSage et al. (2011b , Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27). In two articles ( LeSage etal., 2011a , Significance, 8, 160—63; 2011b, Journal of the Royal Statistical Society: Series A (Statistics in Society), 174, 1007—27), spatial probit models are used to model spatial dependence in this dataset, with decisions to re-open aggregated to the first 90, 180 and 360 days. We re-cast the problem as one of examining the time-to-event records in the data, right-censored as observations ceased before 175 businesses had re-opened; we omit businesses already re-opened when observations began on Day 41. We are interested in checking whether the conclusions about the covariates using aspatial and spatial probit models are modified when applying survival and spatial survival models estimated using MCMC and INLA. In general, we find that the same covariates are associated with re-opening decisions in both modelling approaches. We do however find that data collected from three streets differ substantially, and that the streets are probably better handled separately or that the street effect should be included explicitly.

Share it! Guide and rationale for deposit for Open transparent replicable scientific research

Open Science advocates recommend deposit of stimuli, data and code sufficient to support all assertions in a scientific Ms. Most ‘respectable’ journals and funding bodies have endorsed Open Science. i.e. they ‘talk the talk’. Nevertheless, most published Mss. do not ‘walk the walk’ by following the Open Science guidelines. Professional statisticians, e.g. the America Statistical Association, The Royal Statistical Society provide guidance on inferential statistics reporting that proscribes null-hypothesis statistical tests. This guidance is also widely ignored. The purpose of this Ms. is to increase the proportion of Mss. following open science practices by providing guides to transparent reporting that are easily useable by authors and reviewers. The Ms. comprises the guides themselves, already public, and a rationale as to why recommendations have been chosen, together with suggestions to promote open science practices. The guides are unique in including, in a single document, the three main phases for the conduction of replicable science: planning and execution, Ms. generation and publication; and deposit of supplementary materials. A main aim of the Ms. is to subject the guidance and justifications to peer review.

Model Selection for Univariable Fractional Polynomials

Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429–467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp and fp_select to select such models with real data.

A Brief History of Probability Theory from 1810 to 1940

This chapter charts the origins and consolidation of the English statistical school from the 1860s to the 1930s, with a focus on the school’s chief figures: Francis Galton (1822–1911), Karl Pearson (1857–1936), and Ronald Aylmer Fisher (1890–1962). It begins with a historical overview of the rise of statistics as a study, taking into account the founding of the Statistical Society of London, which became the Royal Statistical Society. It then examines the contributions of Galton, Pearson, and Fisher to the development of modern statistics. It also considers the role played by other figures in the conception of statistics as a branch of applied mathematics in Britain, including Harold Jeffreys. The chapter concludes by discussing the English statistical school’s demise, along with the advance of statistical theory in the United States.