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The one-sayers model for the Extended Crosswise design J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-02-12 Maarten J L F Cruyff, Khadiga H A Sayed, Andrea Petróczi, Peter G M van der Heijden
Abstract The Extended Crosswise design is a randomized response design characterized by a sensitive and an innocuous question and two sub-samples with complementary randomization probabilities of the innocuous question. The response categories are ‘One’ with two different answers and ‘Two’ with two answers that are the same. Due to the complementary randomization probabilities, ‘One’ is the incriminating
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On partial likelihood J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-30 N Reid
Abstract Partial likelihood, introduced in Cox (1975, Partial likelihood. Biometrika, 62(2),269–276), formalizes the construction of the inference function developed in Cox (1972, Regression models and life-tables (with discussion). Journal of the Royal Statistical Society Series B, 34(2),187–220) and referred there to as a conditional likelihood. Partial likelihood can also be viewed as a version
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The Effect: An Introduction to Research Design and Causality J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-25 Egor Bronnikov
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Never Waste a Good Crisis: Lessons Learned from Data Fraud and Questionable Research Practices J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-25 Catherine Saunders
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Causal inference over stochastic networks J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-25 Duncan A Clark, Mark S Handcock
Abstract Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal inference when the actors are connected via an underlying network structure. Our key contribution is a model for causality when the underlying network
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Using proxy pattern-mixture models to explain bias in estimates of COVID-19 vaccine uptake from two large surveys J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-24 Rebecca R Andridge
Abstract Recently, attention was drawn to the failure of two very large internet-based probability surveys to correctly estimate COVID-19 vaccine uptake in the U.S. in early 2021. Both the Delphi-Facebook COVID-19 Trends and Impact Survey (CTIS) and Census Household Pulse Survey (HPS) overestimated uptake substantially, by 17 and 14 percentage points in May 2021, respectively. These surveys had large
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Grace periods in comparative effectiveness studies of sustained treatments J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-23 Kerollos Nashat Wanis, Aaron L Sarvet, Lan Wen, Jason P Block, Sheryl L Rifas-Shiman, James M Robins, Jessica G Young
Abstract Researchers are often interested in estimating the effect of sustained use of a treatment on a health outcome. However, adherence to strict treatment protocols can be challenging for individuals in practice and, when non-adherence is expected, estimates of the effect of sustained use may not be useful for decision making. As an alternative, more relaxed treatment protocols which allow for
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Relaxing the exclusion restriction in shift-share instrumental variable estimation J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-23 Nicolas Apfel
Abstract Many economic studies use shift-share instruments to estimate causal effects. Often, all shares need to fulfil an exclusion restriction, making the identifying assumption strict. This paper proposes to use methods that relax the exclusion restriction by selecting valid shares. I apply the methods to estimate the effect of immigration on wages. The coefficient becomes much lower and often changes
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Modelling urban/rural fractions in low- and middle-income countries J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-20 Yunhan Wu, Jon Wakefield
Abstract In low- and middle-income countries, household surveys are the most reliable data source to examine health and demographic indicators at the subnational level, an exercise in small area estimation. Model-based unit-level models are favoured for producing the subnational estimates at fine scale, such as the admin-2 level. Typically, the surveys employ stratified 2-stage cluster sampling with
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Statistics did not prove that the Huanan Seafood Wholesale Market was the early epicentre of the COVID-19 pandemic J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-16 Dietrich Stoyan, Sung Nok Chiu
Abstract In a recent prominent study, Worobey et al. (2022. The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic. Science, 377(6609), 951–959) purported to demonstrate statistically that the Huanan Seafood Wholesale Market was the epicentre of the early COVID-19 epidemic. We show that this statistical conclusion is invalid on two grounds: (a) The assumption
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The Energy of Data and Distance Correlation J. R. Stat. Soc. A (IF 2.0) Pub Date : 2024-01-03 Li-Pang Chen
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The Big R-Book: From Data Science to Learning Machines and Big Data J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-12-23
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Model-Assisted Bayesian Designs for Dose Finding and Optimization J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-12-18 Amit K Chowdhry
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Incorporating testing volume into estimation of effective reproduction number dynamics J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-12-13 Isaac H Goldstein, Jon Wakefield, Volodymyr M Minin
Abstract Branching process inspired models are widely used to estimate the effective reproduction number—a useful summary statistic describing an infectious disease outbreak—using counts of new cases. Case data is a real-time indicator of changes in the reproduction number, but is challenging to work with because cases fluctuate due to factors unrelated to the number of new infections. We develop a
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Variable selection in latent variable models via knockoffs: an application to international large-scale assessment in education J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-12-12 Zilong Xie, Yunxiao Chen, Matthias von Davier, Haolei Weng
Abstract International large-scale assessments (ILSAs) play an important role in educational research and policy making. They collect valuable data on education quality and performance development across many education systems, giving countries the opportunity to share techniques, organisational structures, and policies that have proven efficient and successful. To gain insights from ILSA data, we
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Innovative Methods for Rare Disease Drug Development J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-12-12 Amit K Chowdhry
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Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-12-12 Briana J K Stephenson, Stephanie M Wu, Francesca Dominici
Abstract Dietary assessments provide the snapshots of population-based dietary habits. Questions remain about how generalisable those snapshots are in national survey data, where certain subgroups are sampled disproportionately. We propose a Bayesian overfitted latent class model to derive dietary patterns, accounting for survey design and sampling variability. Compared to standard approaches, our
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Colin Lingwood Mallows: a great ambidextrous statistician, 1930–2023 J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-12-05 Siddhartha Dalal, James Landwehr
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Incorporating short data into large mixed-frequency vector autoregressions for regional nowcasting J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-22 Gary Koop, Stuart McIntyre, James Mitchell, Aubrey Poon, Ping Wu
Abstract Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging
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Real-time forecasting within soccer matches through a Bayesian lens J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-18 Chinmay Divekar, Soudeep Deb, Rishideep Roy
Abstract This article employs a Bayesian methodology to predict the results of soccer matches in real-time. Using sequential data of various events throughout the match, we utilise a multinomial probit regression in a novel framework to estimate the time-varying impact of covariates and to forecast the outcome. English Premier League data from eight seasons are used to evaluate the efficacy of our
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Risk Measures and Insurance Solvency Benchmarks J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-16 Sebastian Dietz
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A Bayesian approach to estimate annual bilateral migration flows for South America using census data J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-15 Andrea Aparicio Castro, Arkadiusz Wiśniowski, Francisco Rowe
AbstractCensuses are an important source of international migration flow data. However, their use is limited since they indirectly reflect migration, capturing migrant transitions over long intervals rather than migration events, whilst also underestimating the number of infants and deaths. Censuses also neglect migration of those who are native-born when they only include questions on country of birth
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Exploring Modeling with Data and Differential Equations Using R J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-14 Stanley E Lazic
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Representative pure risk estimation by using data from epidemiologic studies, surveys, and registries: estimating risks for minority subgroups J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-11 Lingxiao Wang, Yan Li, Barry I Graubard, Hormuzd A Katki
Abstract Representative risk estimation is fundamental to clinical decision-making. However, risks are often estimated from non-representative epidemiologic studies, which usually under-represent minorities. Model-based methods use population registries to improve external validity of risk estimation but assume hazard ratios are generalisable from samples to the target finite population. ‘Pseudoweighting’
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Where the bee sucks: a dynamic Bayesian network approach to decision support for pollinator abundance strategies J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-07 Martine J Barons, Aditi Shenvi
Abstract For policymakers wishing to make evidence-based decisions, one of the challenges is how to combine the relevant information and evidence in a coherent and defensible manner in order to formulate and evaluate candidate policies. Policymakers often need to rely on experts with disparate fields of expertise when making policy choices in complex, multi-faceted, dynamic environments such as those
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Time Series for Data Sciences: Analysis and Forecasting J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-07 Anoop Chaturvedi
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A groupwise approach for inferring heterogeneous treatment effects in causal inference J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-11-06 Chan Park, Hyunseung Kang
Abstract Recently, there has been great interest in estimating the conditional average treatment effect using flexible machine learning methods. However, in practice, investigators often have working hypotheses about effect heterogeneity across pre-defined subgroups of study units, which we call the groupwise approach. The paper compares two modern ways to estimate groupwise treatment effects, a non-parametric
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A Bayesian change-point detection approach to the economic evaluation of risky projects: an application to healthcare technology assessment J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-10-24 Daniele Bregantini, Laetitia H M Schmitt, Jacco J J Thijssen
Abstract We propose a Bayesian hypothesis testing framework that allows for the assessment of evidence collected during a clinical trial about the cost-effectiveness of a healthcare technology. The model exploits a Bayesian updating rule that makes the link between the evidence collected in clinical research and the expected payoffs of adoption to the healthcare system. The framework takes into account
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Sanmitra Ghosh's contribution to the Discussion of ‘The Second Discussion Meeting on Statistical aspects of the Covid-19 Pandemic’ J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-10-20 Sanmitra Ghosh
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Introduction to Statistics and Data Analysis (with Exercises, Solutions and Applications in R) J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-10-18 Anoop Chaturvedi
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John Dunne and Li-Chun Zhang's reply to the Discussion of ‘A system of population estimates compiled from administrative data only' J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-09-14 John Dunne, Li-Chun Zhang
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Perspectives in Sustainable Equity Investing J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-09-11 Suryakumar Murugaiah
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A dynamic social relations model for clustered longitudinal dyadic data with continuous or ordinal responses J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-09-05 Rebecca Pillinger, Fiona Steele, George Leckie, Jennifer Jenkins
Abstract Social relations models allow the identification of cluster, actor, partner, and relationship effects when analysing clustered dyadic data on interactions between individuals or other units of analysis. We propose an extension of this model which handles longitudinal data and incorporates dynamic structure, where the response may be continuous, binary, or ordinal. This allows the disentangling
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Fifty years with the Cox proportional hazards model: history, influence, and future J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-09-05 Per Kragh Andersen
Abstract A review is given of paper by the author previously published in the Journal of the Indian Institute of Science.
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Measuring Social Inclusion in Europe: a non-additive approach with the expert-preferences of public policy planners J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-09-05 Ludovico Carrino, Luca Farnia, Silvio Giove
Abstract This paper introduces a normative, expert-informed, time-dependent index of Social Inclusion for European administrative regions in five countries, using longitudinal data from Eurostat. Our contribution is twofold: first, our indicator is based on a non-additive aggregation operator (the Choquet Integral), which allows us to model many preferences’ structures and to overcome the limitations
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Dr Arun Chind’s contribution to the discussion of ‘A system of population estimates compiled from administrative data only’ by Dunne and Zhang J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-29 Arun Chind
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Learning Microeconometrics with R J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-22 Morteza Aalabaf-Sabaghi
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Measurement Models for Psychological Attributes J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-22 Andrew McCulloch
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Number Savvy: From the Invention of Numbers to the Future of Data J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-22 Adelson Piñón
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Longitudinal analysis of exchanges of support between parents and children in the UK J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-22 Fiona Steele, Siliang Zhang, Emily Grundy, Tania Burchardt
Abstract We consider how exchanges of support between parents and adult children vary by demographic and socio-economic characteristics and examine evidence for reciprocity in transfers and substitution between practical and financial support. Using data from the UK Household Longitudinal Study 2011–19, repeated measures of help given and received are analysed jointly using multivariate random effects
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Handbook of Regression Modeling in People Analytics, With Examples in R and Python J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-22 Adelson Piñón
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The Psychometrics of Standard Setting J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-22 Andrew McCulloch
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Do economic incentives promote physical activity? Evidence from the London Congestion Charge J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-21 Ryota Nakamura, Andrea Albanese, Emma Coombes, Marc Suhrcke
Abstract This study investigates the impact of economic incentives on travel-related physical activity, leveraging the London Congestion Charge’s disincentivising of sedentary travel modes via increasing the cost of private car use within Central London. The scheme imposes charges on most types of cars entering, exiting, and operating within the Central London area, while individuals living inside
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Handbook of Statistical Methods for Randomized Controlled Trials J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-19 Dougal Hutchison
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Big Data and Social Science Data Science Methods and Tools for Research and Practice J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-14 V Kalyani
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Data Science Ethics: Concepts, Techniques and Cautionary Tales J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-14 R Allan Reese
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Multivariate spatial modelling for predicting missing HIV prevalence rates among key populations J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-10 Zhou Lan, Le Bao
Abstract Ending the HIV/AIDS pandemic is among the sustainable development goals for the next decade. To overcome the problem caused by the imbalances between the need for care and the limited resources, we shall improve our understanding of the local HIV epidemics, especially for key populations at high risk of HIV infection. However, HIV prevalence rates for key populations have been difficult to
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Sarah Henry and Katie O’Farrell’s contribution to the discussion of ‘A system of population estimates compiled from administrative data only’ by Dunne and Zhang J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-10 Sarah Henry, Katie O’Farrell
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Heterogeneity in the US gender wage gap J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-08 Philipp Bach, Victor Chernozhukov, Martin Spindler
Abstract As a measure of gender inequality, the gender wage gap has come to play an important role both in academic research and the public debate. In 2016, the majority of full-time employed women in the United States earned significantly less than comparable men. The extent to which women were affected by gender inequality in earnings, however, depended greatly on socio-economic characteristics,
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A celebration of 50 years of the Cox model in memory of Sir David Cox J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-07 Anthony J Lawrance
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A non-parametric panel model for climate data with seasonal and spatial variation J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-03 Jiti Gao, Oliver Linton, Bin Peng
Abstract We consider a panel data model that allows for heterogeneous time trends at different locations. The model is well suited to identifying trends in climate data recorded at multiple stations. We propose a new estimation method for the model and derive an asymptotic theory for the proposed estimation method. For inferential purposes, we develop a bootstrap method for the case where weak correlation
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Wasserstein barycenter for link prediction in temporal networks J. R. Stat. Soc. A (IF 2.0) Pub Date : 2023-08-02 Alessandro Spelta, Nicolò Pecora
Abstract We propose a flexible link forecast methodology for weighted temporal networks. Our probabilistic model estimates the evolving link dynamics among a set of nodes through Wasserstein barycentric coordinates arising within the optimal transport theory. Optimal transport theory is employed to interpolate among network evolution sequences and to compute the probability distribution of forthcoming