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Multiscale null hypothesis testing for network‐valued data: Analysis of brain networks of patients with autism J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20210122
Ilenia Lovato; Alessia Pini; Aymeric Stamm; Maxime Taquet; Simone VantiniNetworks are a natural way of representing the human brain for studying its structure and function and, as such, have been extensively used. In this framework, case–control studies for understanding autism pertain to comparing samples of healthy and autistic brain networks. In order to understand the biological mechanisms involved in the pathology, it is key to localize the differences on the brain

Long‐term trend analysis of extreme coastal sea levels with changepoint detection J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20210122
Mintaek Lee; Jaechoul LeeSea level rise can bring disastrous outcomes to people living in coastal regions by increasing flood risk or inducing stronger storm surges. We study long‐term linear trends in monthly maximum sea levels by applying extreme value methods. The monthly maximum sea levels are extracted from multiple tide gauges around the coastal regions of the world over a period of as long as 169 years. Due to instrument

Inferring bivariate association from respondent‐driven sampling data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20210121
Dongah Kim; Krista J. Gile; Honoria Guarino; Pedro Mateu‐GelabertRespondent‐driven sampling (RDS) is an effective method of collecting data from many hard‐to‐reach populations. Valid statistical inference for these data relies on many strong assumptions. In standard samples, we assume observations from pairs of individuals are independent. In RDS, this assumption is violated by the sampling dependence between individuals. We propose a method to semi‐parametrically

Bayesian varying coefficient model with selection: An application to functional mapping J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201120
Benjamin Heuclin; Frédéric Mortier; Catherine Trottier; Marie DenisHow does the genetic architecture of quantitative traits evolve over time? Answering this question is crucial for many applied fields such as human genetics and plant or animal breeding. In the last decades, high‐throughput genome techniques have been used to better understand links between genetic information and quantitative traits. Recently, high‐throughput phenotyping methods are also being used

Stacked inverse probability of censoring weighted bagging: A case study in the InfCareHIV Register J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201122
Pablo Gonzalez Ginestet; Ales Kotalik; David M. Vock; Julian Wolfson; Erin E. GabrielWe propose an inverse probability of censoring weighted (IPCW) bagging (bootstrap aggregation) pre‐processing that enables the application of any machine learning procedure for classification to be used to predict the cause‐specific cumulative incidence, properly accounting for right‐censored observations and competing risks. We consider the IPCW area under the time‐dependent ROC curve (IPCW‐AUC) as

A non‐parametric Hawkes process model of primary and secondary accidents on a UK smart motorway J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201111
Kieran Kalair; Colm Connaughton; Pierfrancesco Alaimo Di LoroA self‐exciting spatiotemporal point process is fitted to incident data from the UK National Traffic Information Service to model the rates of primary and secondary accidents on the M25 motorway in a 12‐month period during 2017–2018. This process uses a background component to represent primary accidents, and a self‐exciting component to represent secondary accidents. The background consists of periodic

Quantifying the trendiness of trends J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201120
Andreas Kryger Jensen; Claus Thorn EkstrømNews media often report that the trend of some public health outcome has changed. These statements are frequently based on longitudinal data, and the change in trend is typically found to have occurred at the most recent data collection time point—if no change had occurred the story is less likely to be reported. Such claims may potentially influence public health decisions on a national level.

M‐quantile regression for multivariate longitudinal data with an application to the Millennium Cohort Study J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201125
Marco Alfò; Maria Francesca Marino; Maria Giovanna Ranalli; Nicola Salvati; Nikos TzavidisMotivated by the analysis of data from the UK Millennium Cohort Study on emotional and behavioural disorders, we develop an M‐quantile regression model for multivariate longitudinal responses. M‐quantile regression is an appealing alternative to standard regression models; it combines features of quantile and expectile regression and it may produce a detailed picture of the conditional response variable

Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201111
Edgar Santos‐Fernandez; Erin E. Peterson; Julie Vercelloni; Em Rushworth; Kerrie MengersenMany research domains use data elicited from ‘citizen scientists’ when a direct measure of a process is expensive or infeasible. However, participants may report incorrect estimates or classifications due to their lack of skill. We demonstrate how Bayesian hierarchical models can be used to learn about latent variables of interest, while accounting for the participants’ abilities. The model is described

A Bayesian approach for determining player abilities in football J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201125
Gavin A. Whitaker; Ricardo Silva; Daniel Edwards; Ioannis KosmidisWe consider the task of determining a football player’s ability for a given event type, for example, scoring a goal. We propose an interpretable Bayesian model which is fit using variational inference methods. We implement a Poisson model to capture occurrences of event types, from which we infer player abilities. Our approach also allows the visualisation of differences between players, for a specific

Sequential aggregation of probabilistic forecasts—Application to wind speed ensemble forecasts J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201122
Michaël Zamo; Liliane Bel; Olivier MestreIn numerical weather prediction (NWP), the uncertainty about the future state of the atmosphere is described by a set of forecasts (called an ensemble). All ensembles have deficiencies that can be corrected via statistical post‐processing methods. Several ensembles, based on different NWP models, exist and may be corrected using different statistical methods. These raw or post‐processed ensembles can

Recurrent events modelling of haemophilia bleeding events J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20210107
Andrew C. Titman; Martin J. Wolfsegger; Thomas F. JakiA pharmacokinetic–pharmacodynamic (PK‐PD) approach is developed for modelling the recurrent bleeding events in patients with severe haemophilia to investigate the relationship between factor VIII plasma activity level and the instantaneous risk of a bleed. The model incorporates patient‐level pharmacokinetic (PK) information obtained through measurements taken prior to the study which are used to fit

Bayesian semi‐parametric G‐computation for causal inference in a cohort study with MNAR dropout and death J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20210106
Maria Josefsson; Michael J. DanielsCausal inference with observational longitudinal data and time‐varying exposures is often complicated by time‐dependent confounding and attrition. The G‐computation formula is one approach for estimating a causal effect in this setting. The parametric modelling approach typically used in practice relies on strong modelling assumptions for valid inference and moreover depends on an assumption of missing

Future proofing a building design using history matching inspired level‐set techniques J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201219
Evan Baker; Peter Challenor; Matt EamesHow can one design a building that will be sufficiently protected against overheating and sufficiently energy efficient, whilst considering the expected increases in temperature due to climate change? We successfully manage to address this question—greatly reducing a large set of initial candidate building designs down to a small set of acceptable buildings. We do this using a complex computer model

Robust estimation for small domains in business surveys J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201211
Paul A. Smith; Chiara Bocci; Nikos Tzavidis; Sabine Krieg; Marc J. E. SmeetsSmall area (or small domain) estimation is still rarely applied in business statistics, because of challenges arising from the skewness and variability of variables such as turnover. We examine a range of small area estimation methods as the basis for estimating the activity of industries within the retail sector in the Netherlands. We use tax register data and a sampling procedure which replicates

Finite mixtures of semiparametric Bayesian survival kernel machine regressions: Application to breast cancer gene pathway subgroup analysis J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201201
Lin Zhang; Inyoung KimA gene pathway is defined as a set of genes that functionally work together to regulate a certain biological process. Gene pathway expression data, which is a special case of highly correlated high‐dimensional data, exhibits the ‘small n and large p’ problem. Pathway analysis can take into account the dependency structures among genes and the possibility that several moderately regulated genes may

Threshold‐based subgroup testing in logistic regression models in two‐phase sampling designs J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201128
Ying Huang; Juhee Cho; Youyi FongThe effect of treatment on binary disease outcome can differ across subgroups characterised by other covariates. Testing for the existence of subgroups that are associated with heterogeneous treatment effects can provide valuable insight regarding the optimal treatment recommendation in practice. Our research in this paper is motivated by the question of whether host genetics could modify a vaccine's

Quantile‐frequency analysis and spectral measures for diagnostic checks of time series with nonlinear dynamics J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201122
Ta‐Hsin LiNonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena in the frequency domain. The quantile periodogram is constructed from trigonometric quantile regression of time series data at different frequencies and quantile levels, enabling the quantile‐frequency analysis (QFA) of nonlinear

Modelling time‐varying mobility flows using function‐on‐function regression: Analysis of a bike sharing system in the city of Milan J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201108
Agostino Torti; Alessia Pini; Simone VantiniIn today's world, bike sharing systems are becoming increasingly common in all main cities around the world. To understand the spatiotemporal patterns of how people move by bike through the city of Milan, we apply functional data analysis to study the flows of a bike sharing mobility network. We introduce a complete pipeline to properly analyse and model functional data through a concurrent functional‐on‐functional

Functional ensemble survival tree: Dynamic prediction of Alzheimer’s disease progression accommodating multiple time‐varying covariates J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201107
Shu Jiang; Yijun Xie; Graham A. ColditzWith the exponential growth in data collection, multiple time‐varying biomarkers are commonly encountered in clinical studies, along with a rich set of baseline covariates. This paper is motivated by addressing a critical issue in the field of Alzheimer’s disease (AD) in which we aim to predict the time for AD conversion in people with mild cognitive impairment to inform prevention and early treatment

Random effects dynamic panel models for unequally spaced multivariate categorical repeated measures: an application to child–parent exchanges of support J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20201107
Fiona Steele; Emily GrundyExchanges of practical or financial help between people living in different households are a major component of intergenerational exchanges within families and an increasingly important source of support for individuals in need. Using longitudinal data, bivariate dynamic panel models can be applied to study the effects of changes in individual circumstances on help given to and received from non‐coresident

Linear mixed effects models for non‐Gaussian continuous repeated measurement data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200909
Özgür Asar; David Bolin; Peter J. Diggle; Jonas WallinWe consider the analysis of continuous repeated measurement outcomes that are collected longitudinally. A standard framework for analysing data of this kind is a linear Gaussian mixed effects model within which the outcome variable can be decomposed into fixed effects, time invariant and time‐varying random effects, and measurement noise. We develop methodology that, for the first time, allows any

Burglary in London: insights from statistical heterogeneous spatial point processes J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200805
Jan Povala; Seppo Virtanen; Mark GirolamiTo obtain operational insights regarding the crime of burglary in London, we consider the estimation of the effects of covariates on the intensity of spatial point patterns. Inspired by localized properties of criminal behaviour, we propose a spatial extension to mixtures of generalized linear models from the mixture modelling literature. The Bayesian model proposed is a finite mixture of Poisson generalized

Sensitivity analysis for publication bias in meta‐analyses J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200828
Maya B. Mathur; Tyler J. VanderWeeleWe propose sensitivity analyses for publication bias in meta‐analyses. We consider a publication process such that ‘statistically significant’ results are more likely to be published than negative or “non‐significant” results by an unknown ratio, η. Our proposed methods also accommodate some plausible forms of selection based on a study's standard error. Using inverse probability weighting and robust

A hierarchical mixed effect hurdle model for spatiotemporal count data and its application to identifying factors impacting health professional shortages J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200808
Soutik Ghosal; Timothy S. Lau; Jeremy Gaskins; Maiying KongCount data are common in many fields such as public health. Hurdle models have been developed to model count data when the zero count could be either inflated or deflated. However, when data are repeatedly collected over time and spatially correlated, it is very challenging to model the data appropriately. For example, to study health professional shortage areas, the number of primary care physicians

Landmark proportional subdistribution hazards models for dynamic prediction of cumulative incidence functions J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200805
Qing Liu; Gong Tang; Joseph P. Costantino; Chung‐Chou H. ChangAn individualized dynamic risk prediction model that incorporates all available information collected over the follow‐up can be used to choose an optimal treatment strategy in realtime, although existing methods have not been designed to handle competing risks. In this study, we developed a landmark proportional subdistribution hazard (PSH) model and a comprehensive supermodel for dynamic risk prediction

Nested g‐computation: a causal approach to analysis of censored medical costs in the presence of time‐varying treatment J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200825
Andrew J. Spieker; Emily M. Ko; Jason A. Roy; Nandita MitraRising medical costs are an emerging challenge in policy decisions and resource allocation planning. When cumulative medical cost is the outcome, right censoring induces informative missingness due to heterogeneity in cost accumulation rates across subjects. Inverse weighting approaches have been developed to address the challenge of informative cost trajectories in mean cost estimation, though these

One‐class classification with application to forensic analysis J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200826
Francesca Fortunato; Laura Anderlucci; Angela MontanariThe analysis of broken glass is forensically important to reconstruct the events of a criminal act. In particular, the comparison between the glass fragments found on a suspect (recovered cases) and those collected at the crime scene (control cases) may help the police to identify the offender(s) correctly. The forensic issue can be framed as a one‐class classification problem. One‐class classification

Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200815
Mahesh Karra; David Canning; Ryoko SatoIn public use data sets, it is desirable not to report a respondent's location precisely to protect subject confidentiality. However, the direct use of perturbed location data to construct explanatory exposure variables for regression models will generally make naive estimates of all parameters biased and inconsistent. We propose an approach where a perturbation vector, consisting of a random distance

Bayesian analysis of tests with unknown specificity and sensitivity J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200813
Andrew Gelman; Bob CarpenterWhen testing for a rare disease, prevalence estimates can be highly sensitive to uncertainty in the specificity and sensitivity of the test. Bayesian inference is a natural way to propagate these uncertainties, with hierarchical modelling capturing variation in these parameters across experiments. Another concern is the people in the sample not being representative of the general population. Statistical

A calibrated sensitivity analysis for matched observational studies with application to the effect of second‐hand smoke exposure on blood lead levels in children J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200828
Bo Zhang; Dylan S. SmallWe conducted a matched observational study to investigate the causal relationship between second‐hand smoke and blood lead levels in children. Our first analysis that assumes no unmeasured confounding suggests evidence of a detrimental effect of second‐hand smoke. However, unmeasured confounding is a concern in our study as in other observational studies of second‐hand smoke's effects. A sensitivity

A Bayesian quest for finding a unified model for predicting volleyball games J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200901
Leonardo Egidi; Ioannis NtzoufrasVolleyball is a team sport with unique and specific characteristics. We introduce a new two‐level hierarchical Bayesian model which accounts for these volleyball‐specific characteristics. In the first level, we model the set outcome with a simple logistic regression model. Conditionally on the winner of the set, in the second level, we use a truncated negative binomial distribution for the points earned

Markov switching modelling of shooting performance variability and teammate interactions in basketball J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200827
Marco Sandri; Paola Zuccolotto; Marica ManiseraIn basketball, measures of individual player performance provide critical guidance for a broad spectrum of decisions related to training and game strategy. However, most studies on this topic focus on performance level measurement, neglecting other important factors, such as performance variability. Here we model shooting performance variability by using Markov switching models, assuming the existence

Circular regression trees and forests with an application to probabilistic wind direction forecasting J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200925
Moritz N. Lang; Lisa Schlosser; Torsten Hothorn; Georg J. Mayr; Reto Stauffer; Achim ZeileisAlthough circular data occur in a wide range of scientific fields, the methodology for distributional modelling and probabilistic forecasting of circular response variables is quite limited. Most of the existing methods are built on generalized linear and additive models, which are often challenging to optimize and interpret. Specifically, capturing abrupt changes or interactions is not straightforward

Causal mechanism of extreme river discharges in the upper Danube basin network J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200523
Linda Mhalla; Valérie Chavez‐Demoulin; Debbie J. DupuisExtreme hydrological events in the Danube river basin may severely impact human populations, aquatic organisms and economic activity. One often characterizes the joint structure of extreme events by using the theory of multivariate and spatial extremes and its asymptotically justified models. There is interest, however, in cascading extreme events and whether one event causes another. We argue that

Inference for extreme values under threshold‐based stopping rules J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200612
Anna Maria Barlow; Chris Sherlock; Jonathan TawnThere is a propensity for an extreme value analysis to be conducted as a consequence of a large flooding event. This timing of the analysis introduces bias and poor coverage probabilities into the associated risk assessments and leads subsequently to inefficient flood protection schemes. We explore these problems through studying stochastic stopping criteria and propose new likelihood‐based inferences

A hybrid approach for the stratified mark‐specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200522
Yanqing Sun; Li Qi; Fei Heng; Peter B. GilbertDeployment of the recently licensed tetravalent dengue vaccine based on a chimeric yellow fever virus, CYD‐TDV, requires understanding of how the risk of dengue disease in vaccine recipients depends jointly on a host biomarker measured after vaccination (neutralization titre—neutralizing antibodies) and on a ‘mark’ feature of the dengue disease failure event (the amino acid sequence distance of the

Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200707
Oliver Stoner; Gavin Shaddick; Theo Economou; Sophie Gumy; Jessica Lewis; Itzel Lucio; Giulia Ruggeri; Heather Adair‐RohaniIn 2017 an estimated 3 billion people used polluting fuels and technologies as their primary cooking solution, with 3.8 million deaths annually attributed to household exposure to the resulting fine particulate matter air pollution. Currently, health burdens are calculated by using aggregations of fuel types, e.g. solid fuels, as country level estimates of the use of specific fuel types, e.g. wood

Modelling fuel injector spray characteristics in jet engines by using vine copulas J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200615
Maximilian Coblenz; Simon Holz; Hans‐Jörg Bauer; Oliver Grothe; Rainer KochThe emission requirements for jet engines are becoming more stringent and the combustion process determines pollutant emissions. Therefore, we model the distribution of fuel drops generated by a fuel injector in a jet engine, which can be assumed to be a five‐dimensional problem in terms of drop size, x‐position, y‐position, x‐velocity and y‐velocity. The data are generated by numerical simulations

Global forensic geolocation with deep neural networks J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200623
Neal S. Grantham; Brian J. Reich; Eric B. Laber; Krishna Pacifici; Robert R. Dunn; Noah Fierer; Matthew Gebert; Julia S. Allwood; Seth A. FaithAn important problem in modern forensic analyses is identifying the provenance of materials at a crime scene, such as biological material on a piece of clothing. This procedure, which is known as geolocation, is conventionally guided by expert knowledge of the biological evidence and therefore tends to be application specific, labour intensive and often subjective. Purely data‐driven methods have yet

Fault isolation for a complex decentralized waste water treatment facility J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200719
Molly C. Klanderman; Kathryn B. Newhart; Tzahi Y. Cath; Amanda S. HeringDecentralized waste water treatment facilities monitor many features that are complexly related. The ability to detect the onset of a fault and to identify variables accurately that have shifted because of the fault are vital to maintaining proper system operation and high quality produced water. Various multivariate methods have been proposed to perform fault detection and isolation, but the methods

Estimating the binary endogenous effect of insurance on doctor visits by copula‐based regression additive models J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200607
Giampiero Marra; Rosalba Radice; David M. ZimmerThe paper estimates the causal effect of having health insurance on healthcare utilization, while accounting for potential endogeneity bias. The topic has important policy implications, because health insurance reforms implemented in the USA in recent decades have focused on extending coverage to the previously uninsured. Consequently, understanding the effects of those reforms requires an accurate

The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200522
Francesco Schirripa Spagnolo; Nicola Salvati; Antonella D’Agostino; Ides NicaiseM‐quantile random‐effects regression represents an interesting approach for modelling multilevel data when the researcher is focused on conditional quantiles. When data are obtained from complex survey designs, sampling weights must be incorporated in the analysis. A robust pseudolikelihood approach for accommodating sampling weights in M‐quantile random‐effects regression is presented. In particular

Cluster analysis of microbiome data by using mixtures of Dirichlet–multinomial regression models J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200726
Sanjeena Subedi; Drew Neish; Stephen Bak; Zeny FengThe human gut microbiome is one of the fundamental components of our physiology, and exploring the relationship between biological and environmental covariates and the resulting taxonomic composition of a given microbial community is an active area of research. Previously, a Dirichlet–multinomial regression framework has been suggested to model this relationship, but it did not account for any underlying

On the interplay between exposure misclassification and informative cluster size J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200726
Glen McGee; Marianthi‐Anna Kioumourtzoglou; Marc G. Weisskopf; Sebastien Haneuse; Brent A. CoullA recent multigenerational study of diethylstilbestrol and attention deficit hyperactivity disorder exhibited signs of both informative cluster size—the outcome was more prevalent in small families—and exposure misclassification—self‐report of familial diethylstilbestrol exposure was substantially mismeasured. Motivated by this, we study the effect of exposure misclassification when cluster size is

Longitudinal networks of dyadic relationships using latent trajectories: evidence from the European interbank market J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200514
Federica Bianchi; Francesco Bartolucci; Stefano Peluso; Antonietta MiraFinancial markets are ultimately seen as a collection of dyadic transactions. We study the temporal evolution of dyadic relationships in the European interbank market, as induced by monetary transactions registered in the electronic market for interbank deposits (e‐MID) during a period of 10 years (2006–2015). In particular, we keep track of how reciprocal exchange patterns have varied with macro events

Small sample corrections for Wald tests in latent variable models J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200513
Brice Ozenne; Patrick M. Fisher; Esben Budtz‐J⊘rgensenLatent variable models are commonly used in psychology and increasingly used for analysing brain imaging data. Such studies typically involve a small number of participants (n <100), where standard asymptotic results often fail to control the type 1 error appropriately. The paper presents two corrections improving the control of the type 1 error of Wald tests in latent variable models estimated by

Statistical inference on tree swallow migrations with random forests J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200509
Tim Coleman; Lucas Mentch; Daniel Fink; Frank A. La Sorte; David W. Winkler; Giles Hooker; Wesley M. HochachkaBird species’ migratory patterns have typically been studied through individual observations and historical records. In recent years, the eBird citizen science project, which solicits observations from thousands of bird watchers around the world, has opened the door for a data‐driven approach to understanding the large‐scale geographical movements. Here, we focus on the North American tree swallow

Variable selection in functional linear concurrent regression J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200504
Rahul Ghosal; Arnab Maity; Timothy Clark; Stefano B. LongoWe propose a novel method for variable selection in functional linear concurrent regression. Our research is motivated by a fisheries footprint study where the goal is to identify important time‐varying sociostructural drivers influencing patterns of seafood consumption, and hence the fisheries footprint, over time, as well as estimating their dynamic effects. We develop a variable‐selection method

Generalized partially linear models on Riemannian manifolds J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200503
Amelia Simó; M. Victoria Ibáñez; Irene Epifanio; Vicent GimenoWe introduce generalized partially linear models with covariates on Riemannian manifolds. These models, like ordinary generalized linear models, are a generalization of partially linear models on Riemannian manifolds that allow for scalar response variables with error distribution models other than a normal distribution. Partially linear models are particularly useful when some of the covariates of

The harmonic mean χ2‐test to substantiate scientific findings J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200430
Leonhard HeldStatistical methodology plays a crucial role in drug regulation. Decisions by the US Food and Drug Administration or European Medicines Agency are typically made based on multiple primary studies testing the same medical product, where the two‐trials rule is the standard requirement, despite shortcomings. A new approach is proposed for this task based on the harmonic mean of the squared study‐specific

Simulating gene silencing through intervention analysis J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200426
Vera Djordjilović; Monica Chiogna; Chiara RomualdiWe propose a novel method for simulating the effects of gene silencing. Our approach combines relevant subject matter information provided by biological pathways with gene expression levels measured in regular conditions to predict the behaviour of the system after one of the genes has been silenced. We achieve this by modelling gene silencing as an external intervention in a causal graphical model

Modelling the spatial extent and severity of extreme European windstorms J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20191223
Paul Sharkey; Jonathan A. Tawn; Simon J. BrownWindstorms are a primary natural hazard affecting Europe that are commonly linked to substantial property and infrastructural damage and are responsible for the largest spatially aggregated financial losses. Such extreme winds are typically generated by extratropical cyclone systems originating in the North Atlantic and passing over Europe. Previous statistical studies tend to model extreme winds at

The analysis of transformations for profit‐and‐loss data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20191217
Anthony C. Atkinson; Marco Riani; Aldo CorbelliniWe analyse data on the performance of investment funds, 99 out of 309 of which report a loss, and on the profitability of 1405 firms, 407 of which report losses. The problem in both cases is to use regression to predict performance from sets of explanatory variables. In one case, it is clear from scatter plots of the data that the negative responses have a lower variance than the positive responses

Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200107
Vincent Chin; David Gunawan; Denzil G. Fiebig; Robert Kohn; Scott A. SissonThe paper considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high dimensional correlation matrix and improving the overall efficiency of the data augmentation approach. We reparameterize the correlation matrix in a principled way and then carry out efficient Bayesian inference by using Hamiltonian Monte Carlo sampling. We also propose

Bayesian protein sequence and structure alignment J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200108
Christopher J. Fallaize; Peter J. Green; Kanti V. Mardia; Stuart BarberThe structure of a protein is crucial in determining its functionality and is much more conserved than sequence during evolution. A key task in structural biology is to compare protein structures to determine evolutionary relationships, to estimate the function of newly discovered structures and to predict unknown structures. We propose a Bayesian method for protein structure alignment, with the prior

Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200123
Martin Jullum; Thordis Thorarinsdottir; Fabian E. BachlThe Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of annual seal pup production are critical factors in the estimation of abundance that is needed for management of the species. These estimates are usually based on counts from aerial photographic surveys. However, only a minor part of the whelping region can be photographed, because of its large extent. To estimate

Estimation and inference in mixed effect regression models using shape constraints, with application to tree height estimation J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20191224
Xiyue Liao; Mary C. MeyerEstimation of tree height given diameter is an important part of the forest inventory analysis of the US Forest Service. Existing methods use parametric models to estimate the curve. We propose a semiparametric model in which log (height) is a smooth, increasing and concave function of diameter, with a random‐plot component and fixed effect covariates. Large sample properties and inference methods

Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20191227
Jim E. Griffin; Eleni Matechou; Andrew S. Buxton; Dimitrios Bormpoudakis; Richard A. GriffithsEnvironmental DNA is a survey tool with rapidly expanding applications for assessing the presence of a species at surveyed sites. Environmental DNA methodology is known to be prone to false negative and false positive errors at the data collection and laboratory analysis stages. Existing models for environmental DNA data require augmentation with additional sources of information to overcome identifiability

Using Cox regression to develop linear rank tests with zero‐inflated clustered data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.59) Pub Date : 20200203
Stuart R. Lipsitz; Garrett M. Fitzmaurice; Debajyoti Sinha; Alexander P. Cole; Christian P. Meyer; Quoc‐Dien TrinhZero‐inflated data arise in many fields of study. When comparing zero‐inflated data between two groups with independent subjects, a 2 degree‐of‐freedom test has been developed, which is the sum of a 1 degree‐of‐freedom Pearson χ2‐test for the 2×2 table of group versus dichotomized outcome (0,>0) and a 1 degree‐of‐freedom Wilcoxon rank sum test for the values of the outcome ‘>0’. Here, we extend this