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Estimating a brain network predictive of stress and genotype with supervised autoencoders. J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2023-05-22 Austin Talbot,David Dunson,Kafui Dzirasa,David Carlson
Targeted brain stimulation has the potential to treat mental illnesses. We develop an approach to help design protocols by identifying relevant multi-region electrical dynamics. Our approach models these dynamics as a superposition of latent networks, where the latent variables predict a relevant outcome. We use supervised autoencoders (SAEs) to improve predictive performance in this context, describe
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Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures. J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2023-05-22 Yuan Zhang,David M Vock,Megan E Patrick,Lizbeth H Finestack,Thomas A Murray
In recent sequential multiple assignment randomized trials, outcomes were assessed multiple times to evaluate longer-term impacts of the dynamic treatment regimes (DTRs). Q-learning requires a scalar response to identify the optimal DTR. Inverse probability weighting may be used to estimate the optimal outcome trajectory, but it is inefficient, susceptible to model mis-specification, and unable to
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A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data. J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2023-04-25 Arthur Lui,Juhee Lee,Peter F Thall,May Daher,Katy Rezvani,Rafet Basar
A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A
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Bayesian matrix completion for hypothesis testing. J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2023-03-15 Bora Jin,David B Dunson,Julia E Rager,David M Reif,Stephanie M Engel,Amy H Herring
We aim to infer bioactivity of each chemical by assay endpoint combination, addressing sparsity of toxicology data. We propose a Bayesian hierarchical framework which borrows information across different chemicals and assay endpoints, facilitates out-of-sample prediction of activity for chemicals not yet assayed, quantifies uncertainty of predicted activity, and adjusts for multiplicity in hypothesis
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Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer. J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2023-02-13 Yushu Shi,Liangliang Zhang,Kim-Anh Do,Robert Jenq,Christine B Peterson
There is a keen interest in characterizing variation in the microbiome across cancer patients, given increasing evidence of its important role in determining treatment outcomes. Here our goal is to discover subgroups of patients with similar microbiome profiles. We propose a novel unsupervised clustering approach in the Bayesian framework that innovates over existing model-based clustering approaches
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Utility-based Bayesian personalized treatment selection for advanced breast cancer J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-12-22
The Section 2 heading ‘A BNR MODEL’ should be corrected to read as ‘A BAYESIAN NONPARAMETRIC REGRESSION MODEL’.
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Bayesian modelling strategies for borrowing of information in randomised basket trials J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-10-28 Luke O. Ouma, Michael J. Grayling, James M. S. Wason, Haiyan Zheng
Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing
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Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-10-26 José L. Jiménez, Mourad Tighiouart
Phase I-II cancer clinical trial designs are intended to accelerate drug development. In cases where efficacy cannot be ascertained in a short period of time, it is common to divide the study in two stages: (i) a first stage in which dose is escalated based only on toxicity data and we look for the maximum tolerated dose (MTD) set and (ii) a second stage in which we search for the most efficacious
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Bayesian multi-level mixed-effects model for influenza dynamics J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-10-24 Hanwen Huang
Influenza A viruses (IAV) are the only influenza viruses known to cause flu pandemics. Understanding the evolution of different sub-types of IAV on their natural hosts is important for preventing and controlling the virus. We propose a mechanism-based Bayesian multi-level mixed-effects model for characterising influenza viral dynamics, described by a set of ordinary differential equations (ODE). Both
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Derivation of maternal dietary patterns accounting for regional heterogeneity J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-10-18 Briana J. K. Stephenson, Amy H. Herring, Andrew F. Olshan
Latent class models are often used to characterise dietary patterns. Yet, when subtle variations exist across different sub-populations, overall population patterns can be masked and affect statistical inference on health outcomes. We address this concern with a flexible supervised clustering approach, introduced as Supervised Robust Profile Clustering, that identifies outcome-dependent population-based
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Non-parametric calibration of multiple related radiocarbon determinations and their calendar age summarisation J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-10-17 Timothy J. Heaton
Due to fluctuations in past radiocarbon (14$$ {}^{14} $$C) levels, calibration is required to convert 14$$ {}^{14} $$C determinations Xi$$ {X}_i $$ into calendar ages θi$$ {\theta}_i $$. In many studies, we wish to calibrate a set of related samples taken from the same site or context, which have calendar ages drawn from the same shared, but unknown, density f(θ)$$ f\left(\theta \right) $$. Calibration
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Optimal approximate choice designs for a two-step coffee choice, taste and choice again experiment J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-10-03 Nedka Dechkova Nikiforova, Rossella Berni, Jesús Fernando López-Fidalgo
This work deals with consumers' preferences about coffee. Firstly, a choice experiment is performed on a sample of potential consumers. Following this, a sensory test involving the tasting of two varieties of coffee is carried out with the respondents, after which the same choice experiment is supplied to them again. An innovative approach for building heterogeneous choice designs is specifically developed
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Flexible domain prediction using mixed effects random forests J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-10-02 Patrick Krennmair, Timo Schmid
This paper promotes the use of random forests as versatile tools for estimating spatially disaggregated indicators in the presence of small area-specific sample sizes. Small area estimators are predominantly conceptualised within the regression-setting and rely on linear mixed models to account for the hierarchical structure of the survey data. In contrast, machine learning methods offer non-linear
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A Bayesian model for estimating Sustainable Development Goal indicator 4.1.2: School completion rates J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-25 Ameer Dharamshi, Bilal Barakat, Leontine Alkema, Manos Antoninis
Estimating school completion is crucial for monitoring Sustainable Development Goal (SDG) 4 on education. The recently introduced SDG indicator 4.1.2, defined as the percentage of children aged 3–5 years above the expected completion age of a given level of education that have completed the respective level, differs from enrolment indicators in that it relies primarily on household surveys. This introduces
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Efficient estimation of the marginal mean of recurrent events J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-21 Giuliana Cortese, Thomas H. Scheike
Recurrent events are often encountered in clinical and epidemiological studies where a terminal event is also observed. With recurrent events data it is of great interest to estimate the marginal mean of the cumulative number of recurrent events experienced prior to the terminal event. The standard nonparametric estimator was suggested in Cook and Lawless and further developed in Ghosh and Lin. We
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Contour models for physical boundaries enclosing star-shaped and approximately star-shaped polygons J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-19 Hannah M. Director, Adrian E. Raftery
Boundaries on spatial fields divide regions with particular features from surrounding background areas. Methods to identify boundary lines from interpolated spatial fields are well established. Less attention has been paid to how to model sequences of connected spatial points. Such models are needed for physical boundaries. For example, in the Arctic ocean, large contiguous areas are covered by sea
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Sequential one-step estimator by sub-sampling for customer churn analysis with massive data sets J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-19 Feifei Wang, Danyang Huang, Tianchen Gao, Shuyuan Wu, Hansheng Wang
Customer churn is one of the most important concerns for large companies. Currently, massive data are often encountered in customer churn analysis, which bring new challenges for model computation. To cope with these concerns, sub-sampling methods are often used to accomplish data analysis tasks of large scale. To cover more informative samples in one sampling round, classic sub-sampling methods need
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The saturated pairwise interaction Gibbs point process as a joint species distribution model J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-19 Ian Flint, Nick Golding, Peter Vesk, Yan Wang, Aihua Xia
In an effort to effectively model observed patterns in the spatial configuration of individuals of multiple species in nature, we introduce the saturated pairwise interaction Gibbs point process. Its main strength lies in its ability to model both attraction and repulsion within and between species, over different scales. As such, it is particularly well-suited to the study of associations in complex
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Score test for assessing the conditional dependence in latent class models and its application to record linkage J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-18 Huiping Xu, Xiaochun Li, Zuoyi Zhang, Shaun Grannis
The Fellegi–Sunter model has been widely used in probabilistic record linkage despite its often invalid conditional independence assumption. Prior research has demonstrated that conditional dependence latent class models yield improved match performance when using the correct conditional dependence structure. With a misspecified conditional dependence structure, these models can yield worse performance
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Leveraging network structure to improve pooled testing efficiency J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-16 Daniel K. Sewell
Screening is a powerful tool for infection control, allowing for infectious individuals, whether they be symptomatic or asymptomatic, to be identified and isolated. The resource burden of regular and comprehensive screening can often be prohibitive, however. One such measure to address this is pooled testing, whereby groups of individuals are each given a composite test; should a group receive a positive
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Semi-parametric time-to-event modelling of lengths of hospital stays J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-15 Yang Li, Hao Liu, Xiaoshen Wang, Wanzhu Tu
Length of stay (LOS) is an essential metric for the quality of hospital care. Published works on LOS analysis have primarily focused on skewed LOS distributions and the influences of patient diagnostic characteristics. Few authors have considered the events that terminate a hospital stay: Both successful discharge and death could end a hospital stay but with completely different implications. Modelling
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Utility-based Bayesian personalized treatment selection for advanced breast cancer J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-09 Juhee Lee, Peter F. Thall, Bora Lim, Pavlos Msaouel
A Bayesian method is proposed for personalized treatment selection in settings where data are available from a randomized clinical trial with two or more outcomes. The motivating application is a randomized trial that compared letrozole plus bevacizumab to letrozole alone as first-line therapy for hormone receptor-positive advanced breast cancer. The combination treatment arm had larger median progression-free
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Measuring diachronic sense change: New models and Monte Carlo methods for Bayesian inference J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-09-06 Schyan Zafar, Geoff K. Nicholls
In a bag-of-words model, the senses of a word with multiple meanings, for example ‘bank’ (used either in a river-bank or an institution sense), are represented as probability distributions over context words, and sense prevalence is represented as a probability distribution over senses. Both of these may change with time. Modelling and measuring this kind of sense change are challenging due to the
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Environmental Engel curves: A neural network approach J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-08-31 Tullio Mancini, Hector Calvo-Pardo, Jose Olmo
Environmental Engel curves describe how households' income relates to the pollution associated with the services and goods consumed. This paper estimates these curves with neural networks using the novel dataset constructed in Levinson and O'Brien. We provide further statistical rigor to the empirical analysis by constructing prediction intervals obtained from novel neural network methods such as extra-neural
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Non-parametric Bayesian covariate-dependent multivariate functional clustering: An application to time-series data for multiple air pollutants J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-08-30 Daewon Yang, Taeryon Choi, Eric Lavigne, Yeonseung Chung
Air pollution is a major threat to public health. Understanding the spatial distribution of air pollution concentration is of great interest to government or local authorities, as it informs about target areas for implementing policies for air quality management. Cluster analysis has been popularly used to identify groups of locations with similar profiles of average levels of multiple air pollutants
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A model-based approach to predict employee compensation components J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-08-26 Andreea L. Erciulescu, Jean D. Opsomer
The demand for official statistics at fine levels is motivating researchers to explore estimation methods that extend beyond the traditional survey-based estimation. For this work, the challenge originated with the US Bureau of Labor Statistics, who conducts the National Compensation Survey to collect compensation data from a nationwide sample of establishments. The objective is to obtain predictions
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Statistical integration of heterogeneous omics data: Probabilistic two-way partial least squares (PO2PLS) J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-08-16 Said el Bouhaddani, Hae-Won Uh, Geurt Jongbloed, Jeanine Houwing-Duistermaat
The availability of multi-omics data has revolutionized the life sciences by creating avenues for integrated system-level approaches. Data integration links the information across datasets to better understand the underlying biological processes. However, high dimensionality, correlations and heterogeneity pose statistical and computational challenges. We propose a general framework, probabilistic
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Investigating the association of a sensitive attribute with a random variable using the Christofides generalised randomised response design and Bayesian methods J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-08-16 Shen-Ming Lee, Truong-Nhat Le, Phuoc-Loc Tran, Chin-Shang Li
In empirical studies involving sensitive topics, in addition to the problem of estimating the population proportion with a sensitive characteristic, a question arises as to whether or not there is heterogeneity in the distribution of an auxiliary random variable representing the information of subjects collected from a sensitive group and a non-sensitive group. That is, it is of interest to investigate
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Modelling time-varying rankings with autoregressive and score-driven dynamics J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-08-02 Vladimír Holý, Jan Zouhar
We develop a new statistical model to analyse time-varying ranking data. The model can be used with a large number of ranked items, accommodates exogenous time-varying covariates and partial rankings, and is estimated via the maximum likelihood in a straightforward manner. Rankings are modelled using the Plackett–Luce distribution with time-varying worth parameters that follow a mean-reverting time
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Network Hawkes process models for exploring latent hierarchy in social animal interactions J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-07-28 Owen G. Ward, Jing Wu, Tian Zheng, Anna L. Smith, James P. Curley
Group-based social dominance hierarchies are of essential interest in understanding social structure (DeDeo & Hobson in, Proceedings of the National Academy of Sciences 118(21), 2021). Recent animal behaviour research studies can record aggressive interactions observed over time. Models that can explore the underlying hierarchy from the observed temporal dynamics in behaviours are therefore crucial
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Robust correspondence analysis J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-07-27 Marco Riani, Anthony C. Atkinson, Francesca Torti, Aldo Corbellini
Correspondence analysis is a method for the visual display of information from two-way contingency tables. We introduce a robust form of correspondence analysis based on minimum covariance determinant estimation. This leads to the systematic deletion of outlying rows of the table and to plots of greatly increased informativeness. Our examples are trade flows of clothes and consumer evaluations of the
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Spatiotemporal ETAS model with a renewal main-shock arrival process J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-07-26 Tom Stindl, Feng Chen
We propose a spatiotemporal point process model that enhances the classical Epidemic-Type Aftershock Sequence (ETAS) model. This is achieved with the introduction of a renewal main-shock arrival process and we call this extension the renewal ETAS (RETAS) model. This modification is similar in spirit to the renewal Hawkes (RHawkes) process but the conditional intensity process supports a spatial component
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Specification analysis for technology use and teenager well-being: Statistical validity and a Bayesian proposal J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-07-13 Christoph Semken, David Rossell
A key issue in science is assessing robustness to data analysis choices, while avoiding selective reporting and providing valid inference. Specification Curve Analysis is a tool intended to prevent selective reporting. Alas, when used for inference it can create severe biases and false positives, due to wrongly adjusting for covariates, and mask important treatment effect heterogeneity. As our motivating
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Heterogeneous graphical model for non-negative and non-Gaussian PM2.5 data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-06-22 Jiaqi Zhang, Xinyan Fan, Yang Li, Shuangge Ma
Studies on the conditional relationships between PM2.5 concentrations among different regions are of great interest for the joint prevention and control of air pollution. Because of seasonal changes in atmospheric conditions, spatial patterns of PM2.5 may differ throughout the year. Additionally, concentration data are both non-negative and non-Gaussian. These data features pose significant challenges
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A Bayesian spatio-temporal analysis of markets during the Finnish 1860s famine J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-06-20 Tiia-Maria Pasanen, Miikka Voutilainen, Jouni Helske, Harri Högmander
We develop a Bayesian spatio-temporal model to study pre-industrial grain market integration during the Finnish famine of the 1860s. Our model takes into account several problematic features often present when analysing multiple spatially interdependent time series. For example, compared with the error correction methodology commonly applied in econometrics, our approach allows simultaneous modelling
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Nowcasting COVID-19 deaths in England by age and region J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-06-15 Shaun R. Seaman, Pantelis Samartsidis, Meaghan Kall, Daniela De Angelis
Understanding the trajectory of the daily number of COVID-19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate
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Unconventional policies effects on stock market volatility: The MAP approach J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-06-14 Demetrio Lacava, Giampiero M. Gallo, Edoardo Otranto
Taking the European Central Bank unconventional policies as a reference, we suggest a class of multiplicative error models (MEMs) tailored to analyse the impact such policies have on stock market volatility. The new set of models, called MEM with asymmetry and policy effects, keeps the base volatility dynamics separate from a component reproducing policy effects, with an increase in volatility on announcement
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Multilevel network item response modelling for discovering differences between innovation and regular school systems in Korea J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-06-13 Ick Hoon Jin, Minjeong Jeon, Michael Schweinberger, Jonghyun Yun, Lizhen Lin
The innovation school system in South Korea has been developed in response to the traditional high-pressure school system in South Korea, with a view to cultivate a bottom-up and student-centred educational culture. Despite its ambitious goals, questions have been raised about the success of the innovation school system. Leveraging data from the Gyeonggi Education Panel Study along with advances in
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Stopping time detection of wood panel compression: A functional time-series approach J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-06-07 Han Lin Shang, Jiguo Cao, Peijun Sang
We consider determining the optimal stopping time for the glue curing of wood panels in an automatic process environment. Using the near-infrared spectroscopy technology to monitor the manufacturing process ensures substantial savings in energy and time. We collect a time-series of curves from a near-infrared spectrum probe consisting of 72 spectra and aim to detect an optimal stopping time. We propose
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Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-05-28 Marco Molinari, Andrea Cremaschi, Maria De Iorio, Nishi Chaturvedi, Alun D. Hughes, Therese Tillin
We propose a novel approach to the estimation of multiple Gaussian graphical models (GGMs) to analyse patterns of association among a set of metabolites, under different conditions. Our motivating application is the SABRE (Southall And Brent REvisited) study, a triethnic cohort study conducted in the United Kingdom. Through joint modelling of pattern of association corresponding to different ethnic
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Dynamic disease screening by joint modelling of survival and longitudinal data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-05-27 Peihua Qiu, Lu You
Sequential monitoring of dynamic processes is an active research area because of its broad applications in different industries and scientific research projects, including disease screening in medical research. In the literature, it has been shown that dynamic screening system (DySS) is a powerful tool for sequential monitoring of dynamic processes. To detect a disease (e.g. stroke) for a patient,
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Assessing predictive discrimination performance of biomarkers in the presence of treatment-induced dependent censoring J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-05-25 Cuihong Zhang, Jing Ning, Steven H. Belle, Robert H. Squires, Jianwen Cai, Ruosha Li
In medical studies, some therapeutic decisions could lead to dependent censoring for the survival outcome of interest. This is exemplified by a study of paediatric acute liver failure, where death was subject to dependent censoring due to liver transplantation. Existing methods for assessing the predictive performance of biomarkers often pose the independent censoring assumption and are thus not applicable
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Non-separable spatio-temporal models via transformed multivariate Gaussian Markov random fields J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-05-23 Marcos O. Prates, Douglas R. M. Azevedo, Ying C. MacNab, Michael R. Willig
Models that capture spatial and temporal dynamics are applicable in many scientific fields. Non-separable spatio-temporal models were introduced in the literature to capture these dynamics. However, these models are generally complicated in construction and interpretation. We introduce a class of non-separable transformed multivariate Gaussian Markov random fields (TMGMRF) in which the dependence structure
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A nearest-neighbour Gaussian process spatial factor model for censored, multi-depth geochemical data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-05-19 Tilman M. Davies, Sudipto Banerjee, Adam P. Martin, Rose E. Turnbull
We investigate the relationships between local environmental variables and the geochemical composition of the Earth in a region spanning over 26,000 km2 in the lower South Island of New Zealand. Part of the Southland–South Otago geochemical baseline survey—a pilot study pre-empting roll-out across the country—the data comprise the measurements of 59 chemical trace elements, each at two depth prescriptions
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Classification of myocardial blood flow based on dynamic contrast-enhanced magnetic resonance imaging using hierarchical Bayesian models J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-05-12 Yalei Yang, Hao Gao, Colin Berry, David Carrick, Aleksandra Radjenovic, Dirk Husmeier
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising approach to assess microvascular blood flow (perfusion) within the myocardium, and the Fermi microvascular perfusion model is widely applied to extract estimates of the myocardial blood flow (MBF) from DCE-MRI data sets. The classification of myocardial tissues into normal (healthy) and hypoperfused (lesion) regions provides
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Highly irregular functional generalized linear regression with electronic health records J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-05-09 Justin Petrovich, Matthew Reimherr, Carrie Daymont
This work presents a new approach, called Multiple Imputation of Sparsely-sampled Functions at Irregular Times (MISFIT), for fitting generalized functional linear regression models with sparsely and irregularly sampled data. Current methods do not allow for consistent estimation unless one assumes that the number of observed points per curve grows sufficiently quickly with the sample size. In contrast
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A unifying framework for flexible excess hazard modelling with applications in cancer epidemiology J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-05-03 Alessia Eletti, Giampiero Marra, Manuela Quaresma, Rosalba Radice, Francisco Javier Rubio
Excess hazard modelling is one of the main tools in population-based cancer survival research. Indeed, this setting allows for direct modelling of the survival due to cancer even in the absence of reliable information on the cause of death, which is common in population-based cancer epidemiology studies. We propose a unifying link-based additive modelling framework for the excess hazard that allows
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Reliability analysis of artificial intelligence systems using recurrent events data from autonomous vehicles J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-29 Jie Min, Yili Hong, Caleb B. King, William Q. Meeker
Artificial intelligence (AI) systems have become increasingly common and the trend will continue. Examples of AI systems include autonomous vehicles (AV), computer vision, natural language processing and AI medical experts. To allow for safe and effective deployment of AI systems, the reliability of such systems needs to be assessed. Traditionally, reliability assessment is based on reliability test
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Analysing cycling sensors data through ordinal logistic regression with functional covariates J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-26 Julien Jacques, Sanja Samardžić
With the emergence of digital sensors in sports, all cyclists can now measure many parameters during their effort, such as speed, slope, altitude, heart rate or pedalling cadence. The present work studies the effect of these parameters on the average developed power, which is the best indicator of cyclist performance. For this, a cumulative logistic model for ordinal response with functional covariate
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Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-23 Radka Jersakova, James Lomax, James Hetherington, Brieuc Lehmann, George Nicholson, Mark Briers, Chris Holmes
Obtaining up to date information on the number of UK COVID-19 regional infections is hampered by the reporting lag in positive test results for people with COVID-19 symptoms. In the UK, for ‘Pillar 2’ swab tests for those showing symptoms, it can take up to five days for results to be collated. We make use of the stability of the under reporting process over time to motivate a statistical temporal
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Improving cardio-mechanic inference by combining in vivo strain data with ex vivo volume–pressure data J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-22 Alan Lazarus, Hao Gao, Xiaoyu Luo, Dirk Husmeier
Cardio-mechanic models show substantial promise for improving personalised diagnosis and disease risk prediction. However, estimating the constitutive parameters from strains extracted from in vivo cardiac magnetic resonance scans can be challenging. The reason is that circumferential strains, which are comparatively easy to extract, are not sufficiently informative to uniquely estimate all parameters
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Exploring British accents: Modelling the trap–bath split with functional data analysis J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-21 Aranya Koshy, Shahin Tavakoli
The sound of our speech is influenced by the places we come from. Great Britain contains a wide variety of distinctive accents which are of interest to linguistics. In particular, the ‘a’ vowel in words like ‘class’ is pronounced differently in the North and the South. Speech recordings of this vowel can be represented as formant curves or as mel-frequency cepstral coefficient curves. Functional data
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A Bayesian multi-region radial composite reservoir model for deconvolution in well test analysis J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-20 Themistoklis Botsas, Jonathan A. Cumming, Ian H. Jermyn
In petroleum well test analysis, deconvolution is used to obtain information about reservoir systems, for example the presence of heterogeneities and boundaries. This information is contained in the response function, which can be estimated by solving an inverse problem in the pressure and flow rate measurements. Our Bayesian approach to this problem is based upon a parametric physical model of reservoir
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Assessing seismic origin of geological features by fitting equidistant parallel lines J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-17 P.E. Jupp, I.B.J. Goudie, R.A. Batchelor, R.J.B. Goudie
Some planes in sedimentary rocks contain features that appear to lie near equally spaced parallel lines. Determining whether or not they do so can provide information on possible mechanisms for their formation. The problem is recast here in terms of circular statistics, enabling closeness of candidate sets of lines to the points to be measured by a mean resultant length. This leads to a test of goodness
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The modelling of movement of multiple animals that share behavioural features J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-13 Gianluca Mastrantonio
In this work, we propose a model that can be used to infer the behaviour of multiple animals. Our proposal is defined as a set of hidden Markov models that are based on the sticky hierarchical Dirichlet process, with a shared base-measure, and a step and turn with an attractive point (STAP) emission distribution. The latent classifications are representative of the behaviour assumed by the animals
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Modelling the extremes of seasonal viruses and hospital congestion: The example of flu in a Swiss hospital J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-13 Setareh Ranjbar, Eva Cantoni, Valérie Chavez-Demoulin, Giampiero Marra, Rosalba Radice, Katia Jaton
Viruses causing flu or milder coronavirus colds are often referred to as ‘seasonal viruses’ as they tend to subside in warmer months. In other words, meteorological conditions tend to impact the activity of viruses, and this infor2mation can be exploited for the operational management of hospitals. In this study, we use 3 years of daily data from one of the biggest hospitals in Switzerland and focus
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Posterior summaries of grocery retail topic models: Evaluation, interpretability and credibility J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-09 Mariflor Vega Carrasco, Ioanna Manolopoulou, Jason O'Sullivan, Rosie Prior, Mirco Musolesi
Understanding the shopping motivations behind market baskets has significant commercial value for the grocery retail industry. The analysis of shopping transactions demands techniques that can cope with the volume and dimensionality of grocery transactional data while delivering interpretable outcomes. Latent Dirichlet allocation (LDA) allows processing grocery transactions and the discovering of customer
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Predicting phenotypes from brain connection structure J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-09 Subharup Guha, Rex Jung, David Dunson
This article focuses on the problem of predicting a response variable based on a network-valued predictor. Our motivation is the development of interpretable and accurate predictive models for cognitive traits and neuro-psychiatric disorders based on an individual's brain connection network (connectome). Current methods reduce the complex, high-dimensional brain network into low-dimensional pre-specified
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Multifidelity computer model emulation with high-dimensional output: An application to storm surge J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-09 Pulong Ma, Georgios Karagiannis, Bledar A. Konomi, Taylor G. Asher, Gabriel R. Toro, Andrew T. Cox
Hurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm surges. Such computer models can be implemented with a wide range of fidelity levels, with computational burdens varying by several orders of magnitude
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A principal stratification approach to estimating the effect of continuing treatment after observing early outcomes J. R. Stat. Soc. Ser. C Appl. Stat. (IF 1.6) Pub Date : 2022-04-07 Patrick M. Schnell, Richard Baumgartner, Shahrul Mt-Isa, Vladimir Svetnik
Chronic diseases often require continuing care, and early response to treatment can be an important predictor of long-term efficacy. Often, an apparent lack of early efficacy may lead to discontinuation of treatment, with the decision made either by clinicians or by the patients themselves. Thus, it is important to determine whether or not a desired early outcome corresponds to a beneficial long-term