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A comparison of prior elicitation aggregation using the classical method and SHELF J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210507
Cameron J. Williams, Kevin J. Wilson, Nina WilsonSubjective Bayesian prior distributions elicited from experts can be aggregated together to form group priors. This paper compares aggregated priors formed by equal weight aggregation, the classical method and the Sheffield elicitation framework to each other and individual expert priors, using an expert elicitation carried out for a clinical trial. Aggregation methods and individual expert prior distributions

Testing by betting: A strategy for statistical and scientific communication J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210505
Glenn ShaferThe most widely used concept of statistical inference—the p‐value—is too complicated for effective communication to a wide audience. This paper introduces a simpler way of reporting statistical evidence: report the outcome of a bet against the null hypothesis. This leads to a new role for likelihood, to alternatives to power and confidence, and to a framework for meta‐analysis that accommodates both

Seconder of the vote of thanks to Glenn Shafer and contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210505
Frank P. A. CoolenProfessor Shafer’s paper, as so much of his work, has taught me interesting new aspects of statistical inference with substantial historical context. Testing a probability distribution by betting is simple and powerful, and the betting interpretation is natural for sequential testing of hypotheses. The fact that the betting score is a likelihood ratio and the implied targets are very interesting, as

Vladimir Vovk’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210505
Vladimir VovkGlenn Shafer’s paper is a powerful appeal for a wider use of betting ideas and intuitions in statistics. He admits that p‐values will never be completely replaced by betting scores, and I discuss it further in Vovk (2020a) (Appendix A) (one of the two online appendices that I have prepared to meet the word limit). Both p‐values and betting scores generalise Cournot’s principle (Shafer, 2007), but they

Priyantha Wijayatunga’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210505
Priyantha WijayatungaHere I make two points on hypothesis testing. The p‐value is the conditional probability of seeing the test statistic having realizations the same or more extreme from what already available data have shown, given the null hypothesis H0. Since the word ‘conditional’ is often omitted in text books, many practitioners believe that the p‐value is just an unconditional probability, thus tend to misuse

The effect of the Brexit referendum result on subjective well‐being* J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210315
Georgios Kavetsos, Ichiro Kawachi, Ilias Kyriopoulos, Sotiris VandorosWe study the effect of the Brexit referendum result on subjective well‐being in the United Kingdom. Using a quasi‐experimental design, we find that the referendum’s outcome led to an overall decrease in subjective well‐being in the United Kingdom compared to a control group. The effect is driven by individuals who hold an overall positive image of the European Union and shows little signs of adaptation

Missing, presumed different: Quantifying the risk of attrition bias in education evaluations J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210310
Ben Weidmann, Luke MiratrixWe estimate the magnitude of attrition bias for 10 randomized controlled trials (RCTs) in education. We make use of a unique feature of administrative school data in England that allows us to analyse post‐test academic outcomes for nearly all students, including those who originally dropped out of the RCTs we analyse. We find that the typical magnitude of attrition bias is 0.015 effect size units (ES)

Health aid, governance and infant mortality J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210323
Chris Doucouliagos, Jack Hennessy, Debdulal MallickWe investigate the impact of health aid on infant mortality conditional on the quality of governance. Our analysis applies instrumental variable estimation with health aid instrumented by donor government fractionalization interacted with the probability of allocating health aid to a recipient country. Using panel data for 96 recipient countries for the 2002–2015 period, we find that the effectiveness

Tobias (Toby) Lewis 1918‐2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210301
Vic Barnett, Kevin McConwayToby Lewis, who died in London on 20 November 2020 at the age of 102, will be remembered as a statistician who made wide‐ranging contributions to statistical methodology, as a gifted teacher who inspired those he taught and supervised in their research, and as a warm and generous friend to professional and social colleagues alike. Many of his past students went on to become his lifelong friends. He

When zero may not be zero: A cautionary note on the use of inter‐rater reliability in evaluating grant peer review J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210420
Elena A. Erosheva, Patrícia Martinková, Carole J. LeeConsiderable attention has focused on studying reviewer agreement via inter‐rater reliability (IRR) as a way to assess the quality of the peer review process. Inspired by a recent study that reported an IRR of zero in the mock peer review of top‐quality grant proposals, we use real data from a complete range of submissions to the National Institutes of Health and to the American Institute of Biological

Removing the influence of group variables in high‐dimensional predictive modelling J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210415
Emanuele Aliverti, Kristian Lum, James E. Johndrow, David B. DunsonIn many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise undesirable correlations. Such correlations may arise from a variety of sources, including batch effects, systematic measurement errors or sampling bias. Without explicit adjustment, machine learning algorithms trained using

Two‐phase sampling designs for data validation in settings with covariate measurement error and continuous outcome J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210415
Gustavo Amorim, Ran Tao, Sarah Lotspeich, Pamela A. Shaw, Thomas Lumley, Bryan E. ShepherdMeasurement errors are present in many data collection procedures and can harm analyses by biasing estimates. To correct for measurement error, researchers often validate a subsample of records and then incorporate the information learned from this validation sample into estimation. In practice, the validation sample is often selected using simple random sampling (SRS). However, SRS leads to inefficient

John Haigh 1941–2021 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210415
Charles M. GoldieJohn Haigh, who died on 9 March 2021 aged 79, was the pre‐eminent populariser of probability for his time. Through many publications and media appearances, he perfected the rare art of explaining subtle concepts and calculations to those who would claim no mathematical knowledge. Though his audiences may not have realised it, his standards were high; he never shielded them from a calculation they could

Modified Poisson regression analysis of grouped and right‐censored counts J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210408
Qiang Fu, Tian‐Yi Zhou, Xin GuoGrouped and right‐censored (GRC) counts are widely used in criminology, demography, epidemiology, marketing, sociology, psychology and other related disciplines to study behavioural and event frequencies, especially when sensitive research topics or individuals with possibly lower cognitive capacities are at stake. Yet, the co‐existence of grouping and right‐censoring poses major difficulties in regression

The design of replication studies J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210331
Larry V. Hedges, Jacob M. SchauerEmpirical evaluations of replication have become increasingly common, but there has been no unified approach to doing so. Some evaluations conduct only a single replication study while others run several, usually across multiple laboratories. Designing such programs has largely contended with difficult issues about which experimental components are necessary for a set of studies to be considered replications

Modelling non‐linear age‐period‐cohort effects and covariates, with an application to English obesity 2001–2014 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210323
Zoë Fannon, Christiaan Monden, Bent NielsenWe develop an age‐period‐cohort model for repeated cross‐section data with individual covariates, which identifies the non‐linear effects of age, period and cohort. This is done for both continuous and binary dependent variables. The age, period and cohort effects in the model are represented by a parametrization with freely varying parameters that separates the identified non‐linear effects and the

A simple framework to identify optimal cost‐effective risk thresholds for a single screen: Comparison to Decision Curve Analysis J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210323
Hormuzd A. Katki, Ionut BebuDecision curve analysis (DCA) is a popular approach for assessing biomarkers and risk models, but does not require costs and thus cannot identify optimal risk thresholds for actions. Full decision analyses can identify optimal thresholds, but typically used methods are complex and often difficult to understand. We develop a simple framework to calculate the incremental net benefit for a single‐time

Pension eligibility rules and the local causal effect of retirement on cognitive functioning* J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210323
Eduardo FéWe propose an identification framework to evaluate the exclusion restriction in a fuzzy regression discontinuity setting, by adopting results from the literature on partial identification with invalid instrumental variables. With this framework, we provide new estimates of the effect of retirement on cognitive functioning and the first empirical analysis of the validity of an age‐based instrumental

Estimating event‐rates from unreliable historical records J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210223
Jonathan RougierIt is natural, when contemplating an historical record of events, to base a simple estimator of the event‐rate on that recent part of the record where the recording probability is thought to be effectively 1. After all, this avoids the downward bias which would be incurred by ‘overshooting’ into a time where the recording probability was less than 1. However, there is a trade‐off, because overshooting

Spatio‐temporal mixed membership models for criminal activity J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210127
Seppo Virtanen, Mark GirolamiWe suggest a probabilistic approach to study crime data in London and highlight the benefits of defining a statistical joint crime distribution model which provides insights into urban criminal activity. This is achieved by developing a hierarchical mixture model for observations, crime occurrences over a geographical study area, that are grouped according to multiple time stamps and crime categories

On probability distributions of the time deviation law of container liner ships under interference uncertainty J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201117
Yunting Song, Nuo WangContainer liner shipping is a kind of transportation mode that is operated according to a schedule. Although the goal is to operate container liner ships on time, the actual arrival time and handling time often deviate from the schedule due to uncertain factors. The identification of a proper probability distribution to describe time deviation law will have a significant impact on accurately recognizing

Mervyn Stone, 1932–2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201206
Rex GalbraithMervyn Stone died on 19 September 2020, aged 87. He was a brilliant mathematician, professor of probability and statistics, and thinker. He was elected to the Royal Statistical Society in 1955; served on the Series B Editorial Panel (1966–1969) and as the Editor of Series B (1975–1977); on the Research Section Committee (1974–1977), the Conference Committee (1977–1978) and as a Member of Council (1976–1980)

Flavia Jolliffe 1942–2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201204
Gerald GoodallFlavia Jolliffe was born Flavia Rosamund Savigear on 22 July 1942, and died on 6 August 2020. She had a long career in academe and gave extensive service to the statistics community in general and to the Royal Statistical Society in particular. Her early career was at Imperial College but she soon moved to the Department of Economics and Social Statistics at Southampton, and afterwards to the Department

Personalised need of care in an ageing society: The making of a prediction tool based on register data J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210116
Marvin N. Wright, Sasmita Kusumastuti, Laust H. Mortensen, Rudi G. J. Westendorp, Thomas A. GerdsDanish municipalities monitor older persons who are at high risk of declining health and would later need home care services. However, there is no established strategy yet on how to accurately identify those who are at high risk. Therefore, there is great potential to optimise the municipalities’ prevention strategies. Denmark’s comprehensive set of electronic population registers provide longitudinal

Nowcasting monthly GDP with big data: A model averaging approach J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210112
Tommaso Proietti, Alessandro GiovannelliGross domestic product (GDP) is the most comprehensive and authoritative measure of economic activity. The macroeconomic literature has focused on nowcasting and forecasting this measure at the monthly frequency, using related high‐frequency indicators. We address the issue of estimating monthly GDP using a large‐dimensional set of monthly indicators, by pooling the disaggregate estimates arising from

Functional ANOVA modelling of pedestrian counts on streets in three European cities J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210109
David Bolin, Vilhelm Verendel, Meta Berghauser Pont, Ioanna Stavroulaki, Oscar Ivarsson, Erik HåkanssonThe relation between pedestrian flows, the structure of the city and the street network is of central interest in urban research. However, studies of this have traditionally been based on small data sets and simplistic statistical methods. Because of a recent large‐scale cross‐country pedestrian survey, there is now enough data available to study this in greater detail than before, using modern statistical

Leveraging auxiliary information on marginal distributions in nonignorable models for item and unit nonresponse J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210109
Olanrewaju Akande, Gabriel Madson, D. Sunshine Hillygus, Jerome P. ReiterOften, government agencies and survey organizations know the population counts or percentages for some of the variables in a survey. These may be available from auxiliary sources, for example administrative databases or other high‐quality surveys. We present and illustrate a model‐based framework for leveraging such auxiliary marginal information when handling unit and item nonresponse. We show how

Exploiting new forms of data to study the private rented sector: Strengths and limitations of a database of rental listings J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210109
Mark Livingston, Francesca Pannullo, Adrian W. Bowman, E. Marian Scott, Nick BaileyReviews of official statistics for UK housing have noted that developments have not kept pace with real‐world change, particularly the rapid growth of private renting. This paper examines the potential value of big data in this context. We report on the construction of a dataset from the on‐line adverts of one national lettings agency, describing the content of the dataset and efforts to validate it

Sample size determination for risk‐based tax auditing J. R. Stat. Soc. A (IF 2.21) Pub Date : 20210105
Petros Dellaportas, Evangelos Ioannidis, Christos KotsogiannisA modern system of Revenue Administration requires an effective and efficient management of compliance which in turn requires a well‐designed taxpayers audit strategy. The selection of taxpayers to be audited by Revenue Authorities is a non‐standard sample size determination problem, involving an initial random sample from the population and, based on the statistical information derived from it, a

Consistent aggregation with superlative and other price indices J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201205
Ludwig von Auer, Jochen WengenrothVarious fields of economic analysis (e.g. growth and productivity) and economic policy (e.g. monetary and social policy) rely on accurate measures of price change. Unfortunately, the price index formulae that most price statisticians consider as particularly accurate—the superlative indices of Fisher, Törnqvist, and Walsh—are believed to violate the property of consistency in aggregation. This property

Proxy expenditure weights for Consumer Price Index: Audit sampling inference for big‐data statistics J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201125
Li‐Chun ZhangPurchase data from retail chains can provide proxy measures of private household expenditure on items that are the most troublesome to collect in the traditional expenditure survey. Due to the inevitable coverage and selection errors, bias must exist in these proxy measures. Moreover, given the sheer amount of data, the bias completely dominates the variance. To investigate the potential of replacing

A seasonal dynamic measurement model for summer learning loss J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201123
Daniel McNeish, Denis DumasResearch conducted in US schools shows summer learning loss in test scores. If this summer loss is not incorporated into models of student ability growth, assumptions will be violated because fall scores will be overestimated and spring scores will be underestimated, which can be particularly problematic when evaluating teacher or school effectiveness. Statistical methods for summer loss have remained

Specification and testing of hierarchical ordered response models with anchoring vignettes J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201112
William H. Greene, Mark N. Harris, Rachel J. Knott, Nigel RiceCollection and analysis of self‐reported information on an ordered Likert scale is ubiquitous across the social sciences. Inference from such analyses is valid where the response scale employed means the same thing to all individuals. That is, if there is no differential item functioning (DIF) present in the data. A priori this is unlikely to hold across all individuals and cohorts in any sample of

A double machine learning approach to estimate the effects of musical practice on student’s skills J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201112
Michael C. KnausThis study investigates the dose–response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically relevant questions that arise for these new methods: (i) How to investigate sensitivity of estimates to tuning

Linkage‐data linear regression J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201111
Li‐Chun Zhang, Tiziana TuotoData linkage is increasingly being used to combine data from different sources with the aim of identifying and bringing together records from separate files, which correspond to the same entities. Usually, data linkage is not a trivial procedure and linkage errors, false and missed links, are unavoidable. In these cases, standard statistical techniques may produce misleading inference. In this paper

Synthetic microdata for establishment surveys under informative sampling J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201110
Hang J. Kim, Jörg Drechsler, Katherine J. ThompsonMany agencies are investigating whether releasing synthetic microdata could be a viable dissemination strategy for highly sensitive data, such as business data, for which disclosure avoidance regulations otherwise prohibit the release of public use microdata. However, existing methods assume that the original data either cover the entire population or comprise a simple random sample, which limits the

Estimating causal moderation effects with randomized treatments and non‐randomized moderators J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201110
Kirk BansakResearchers are often interested in analysing conditional treatment effects. One variant of this is ‘causal moderation’, which implies that intervention upon a third (moderator) variable would alter the treatment effect. This study considers the conditions under which causal moderation can be identified and presents a generalized framework for estimating causal moderation effects given randomized treatments

A dynamic factor model approach to incorporate Big Data in state space models for official statistics J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201109
Caterina Schiavoni, Franz Palm, Stephan Smeekes, Jan van den BrakelIn this paper we consider estimation of unobserved components in state space models using a dynamic factor approach to incorporate auxiliary information from high‐dimensional data sources. We apply the methodology to unemployment estimation as done by Statistics Netherlands, who uses a multivariate state space model to produce monthly figures for unemployment using series observed with the labour force

Beyond generalization of the ATE: Designing randomized trials to understand treatment effect heterogeneity J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201104
Elizabeth TiptonResearchers conducting randomized trials have increasingly shifted focus from the average treatment effect to understanding moderators of treatment effects. Current methods for exploring moderation focus on model selection and hypothesis tests. At the same time, recent developments in the design of randomized trials have argued for the need for population‐based recruitment in order to generalize well

A dynamic separable network model with actor heterogeneity: An application to global weapons transfers* J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201104
Michael Lebacher, Paul W. Thurner, Göran KauermannIn this paper, we analyse the network of international major conventional weapons (MCW) transfers from 1950 to 2016, based on data from the Stockholm International Peace Research Institute (SIPRI). The dataset consists of yearly bilateral arms transfers between pairs of countries, which allows us to conceive of the individual relationships as part of an overall trade network. For the analysis, we extend

Ranking, and other properties, of elite swimmers using extreme value theory J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201104
Harry Spearing, Jonathan Tawn, David Irons, Tim Paulden, Grace BennettThe International Swimming Federation (FINA) uses a very simple points system with the aim to rank swimmers across all swimming events. The points acquired is a function of the ratio of the recorded time and the current world record for that event. With some world records considered ‘better’ than others however, bias is introduced between events, with some being much harder to attain points where the

Interviewer effects and the measurement of financial literacy J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201104
Thomas F. Crossley, Tobias Schmidt, Panagiota Tzamourani, Joachim K. WinterIn this paper, we ask whether interviewers influence the answers to a standard set of survey questions on financial literacy. We study data from Germany's wealth survey, the Panel on Household Finances (PHF). We have access to extensive auxiliary data, including interviewer identifiers, background characteristics of interviewers and measures of interviewer activity through the survey. We find that

Do coefficients of variation of response propensities approximate non‐response biases during survey data collection? J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201103
Jamie C. Moore, Gabriele B. Durrant, Peter W. F. SmithWe evaluate the utility of coefficients of variation of response propensities (CVs) as measures of risks of survey variable non‐response biases when monitoring survey data collection. CVs quantify variation in sample response propensities estimated given a set of auxiliary attribute covariates observed for all subjects. If auxiliary covariates and survey variables are correlated, low levels of propensity

Quantifying longevity gaps using micro‐level lifetime data J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201103
Frank van Berkum, Katrien Antonio, Michel VellekoopUsing flexible Poisson regressions, we analyse a huge micro‐level lifetime dataset from a Dutch pension fund, including categorical, continuous and spatial risk factors collected on participants in the fund. The availability of granular lifetime data allows us to quantify the longevity gap between the national population and the fund on the one hand, and between participants within the fund on the

A Bayesian structural time series analysis of the effect of basic income on crime: Evidence from the Alaska Permanent Fund* J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201103
Richard DorsettThis paper examines the impact of Alaska’s Permanent Fund Dividend on crime. The Dividend has been payable annually to state residents since 1982 and is the world’s longest‐running example of a basic income. Initially universal, from 1989 onwards eligibility was withdrawn from an increasing proportion of those in prison. A Bayesian structural time series estimator is used to simulate Alaskan crime

The effects of health on the extensive and intensive margins of labour supply J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201102
Lixin CaiUsing the first seven waves of the Understanding Society data, this study estimates the effects of health on the extensive and intensive margins of the labour supply of the UK workers. Earlier studies on the effects of health on labour supply tend to focus on a binary measure of labour force participation or early retirement. The results show that health affects both the margins of labour supply for

Did you conduct a sensitivity analysis? A new weighting‐based approach for evaluations of the average treatment effect for the treated J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201102
Guanglei Hong, Fan Yang, Xu QinIn non‐experimental research, a sensitivity analysis helps determine whether a causal conclusion could be easily reversed in the presence of hidden bias. A new approach to sensitivity analysis on the basis of weighting extends and supplements propensity score weighting methods for identifying the average treatment effect for the treated (ATT). In its essence, the discrepancy between a new weight that

Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting* J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201101
Bent Jesper Christensen, Nabanita Datta Gupta, Paolo Santucci de MagistrisUsing annual data from 1978 through 2016, and monthly data from January 2005 through November 2017 from Denmark, we provide a precise estimate of the upper bound on the potential impact of the adoption of wind energy on the reduction of CO 2 emissions from energy production. We separate causal impacts from endogenous effects in regressions using instrumental variables including average wind speed,

Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201101
Kamaryn T. Tanner, Linda D. Sharples, Rhian M. Daniel, Ruth H. KeoghDynamic prediction models provide predicted survival probabilities that can be updated over time for an individual as new measurements become available. Two techniques for dynamic survival prediction with longitudinal data dominate the statistical literature: joint modelling and landmarking. There is substantial interest in the use of machine learning methods for prediction; however, their use in the

Flexible instrumental variable distributional regression J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200816
Guillermo Briseño Sanchez, Maike Hohberg, Andreas Groll, Thomas KneibWe tackle two limitations of standard instrumental variable regression in experimental and observational studies: restricted estimation to the conditional mean of the outcome and the assumption of a linear relationship between regressors and outcome. More flexible regression approaches that solve these limitations have already been developed but have not yet been adopted in causality analysis. The

Bayesian econometric modelling of observational data for cost‐effectiveness analysis: establishing the value of negative pressure wound therapy in the healing of open surgical wounds J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200805
Pedro Saramago, Karl Claxton, Nicky J. Welton, Marta SoaresIn the absence of evidence from randomized controlled trials on the relative effectiveness of treatments, cost‐effectiveness analyses increasingly use observational data instead. Treatment assignment is not, however, randomized, and naive estimates of the treatment effect may be biased. To deal with this bias, one may need to adjust for observed and unobserved confounders. In this work we explore and

Assessing causal effects of extra compulsory learning on college students’ academic performances J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200826
Federica Licari, Alessandra MatteiIn Italian state universities candidate freshmen must take an entrance examination. Candidates who obtain a test score less than or equal to a pre‐fixed threshold may enrol at the university but must comply with an additional compulsory educational obligation, called obblighi formativi aggiuntivi (OFA). The OFA assignment rule appeals to a (sharp) regression discontinuity design with the entrance examination

Is being an only child harmful to psychological health?: evidence from an instrumental variable analysis of China's one‐child policy J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200812
Shuxi Zeng, Fan Li, Peng DingThe paper evaluates the effects of being an only child in a family on psychological health, leveraging data on the one‐child policy in China. We use an instrumental variable approach to address the potential unmeasured confounding between the fertility decision and psychological health, where the instrumental variable is an index of the intensity of the implementation of the policy. We establish an

Christopher John Skinner, 1953–2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201005
Ray Chambers, Ian Diamond, Tim Holt, Jouni Kuha, Danny Pfeffermann, Natalie Shlomo, Pedro Nascimento Silva, Paul Smith, David Steel, Fiona SteeleChris Skinner was born in Penge, South London, on March 12th, 1953, the elder son of Richard and Daphne Skinner. His father worked for Lloyds of London, and his mother worked at the family‐run furniture store, Edginton's, in Penge. He showed an early aptitude for mathematics at St Dunstan's College, Catford, gaining a scholarship to Trinity College, Cambridge, and graduating with first‐class Honours

Gerald Joseph Goodhardt, 1930–2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201005
Chris ChatfieldGerald Goodhardt was a distinguished marketing scientist who was also a fine statistician. He was always determined to keep one foot firmly in both camps, and he was for example Chairman of the Market Research Society (1973–1974) as well as being Honorary Secretary of the Royal Statistical Society (RSS) (1982–1988). His research contributions were mainly in the area of consumer purchasing behaviour

Willem van Zwet, 1934–2020 J. R. Stat. Soc. A (IF 2.21) Pub Date : 20201005
P. J. Bickel, N. I. FisherWillem van Zwet passed away on July 2nd, 2020, after a short illness. He was an outstanding researcher, teacher and educator in mathematical statistics and probability, the father of statistics in the Netherlands, and the leading figure in opening up dialogue with colleagues in eastern Europe. His lasting contributions to the profession through his research and his work with professional societies

Causal inference, social networks and chain graphs J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200718
Elizabeth L. Ogburn, Ilya Shpitser, Youjin LeeTraditionally, statistical inference and causal inference on human subjects rely on the assumption that individuals are independently affected by treatments or exposures. However, recently there has been increasing interest in settings, such as social networks, where individuals may interact with one another such that treatments may spill over from the treated individual to their social contacts and

Simple rules to guide expert classifications J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200527
Jongbin Jung, Connor Concannon, Ravi Shroff, Sharad Goel, Daniel G. GoldsteinJudges, doctors and managers are among those decision makers who must often choose a course of action under limited time, with limited knowledge and without the aid of a computer. Because data‐driven methods typically outperform unaided judgements, resource‐constrained practitioners can benefit from simple, statistically derived rules that can be applied mentally. In this work, we formalize long‐standing

A similarity‐based approach for macroeconomic forecasting J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200608
Y. Dendramis, G. Kapetanios, M. MarcellinoIn the aftermath of the recent financial crisis there has been considerable focus on methods for predicting macroeconomic variables when their behaviour is subject to abrupt changes, associated for example with crisis periods. We propose similarity‐based approaches as a way to handle parameter instability and apply them to macroeconomic forecasting. The rationale is that clusters of past data that

Forecasting of cohort fertility under a hierarchical Bayesian approach J. R. Stat. Soc. A (IF 2.21) Pub Date : 20200417
Joanne Ellison, Erengul Dodd, Jonathan J. ForsterFertility projections are a key determinant of population forecasts, which are widely used by government policy makers and planners. In keeping with the recent literature, we propose an intuitive and transparent hierarchical Bayesian model to forecast cohort fertility. Using Hamiltonian Monte Carlo methods and a data set from the human fertility database, we obtain fertility forecasts for 30 countries