A random‐effects model for clustered circular data

Rivest, Louis‐Paul; Kato, Shogo

doi:10.1002/cjs.11520

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…As far as

\theta

is concerned, circular regression 19,20 for independent observations revolves around the von Mises distribution, whose unimodal shape may be a limitation as already stressed in Section 4.1. There are also some proposals for longitudinal circular responses, 33,48‐51 although they have limited or no software implementations. It is noteworthy to point out that the conditional specification in () starts from the same model considered by others 33 .…”

Section: Methodsmentioning

confidence: 99%

Joint regression modelling of intensity and timing of accelerometer counts

Geraci

2022

Statistics in Medicine

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Accelerometers are commonly used in human medical and public health research to measure physical movement, which is relevant in a wide range of studies, from physical activity and sleep behaviours studies, to identification of movement patterns in people affected by diseases of the locomotor system and prediction of risk of injury in high performance sports. The accelerometer output provides the intensity (activity count) and timing (timestamp) of the movement, which can be used to define bouts of activity (periods of sustained movement of a given intensity). In some contexts, it may be important to include both dimensions to obtain a broader and deeper understanding of the phenomenon under study. Such is the case of a large‐scale epidemiological investigation on the daily and weekly physical activity behaviours of school‐aged children enrolled in the UK Millennium Cohort Study, which has motivated the present article. I present a statistical approach to joint modelling of intensity and timing of activity bouts that takes advantage of the circular nature of the timing. The model, which accounts for the longitudinal structure of the observations, is remarkably simple to implement using standard statistical software.

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“…As far as

\theta

Section: Methodsmentioning

confidence: 99%

Joint regression modelling of intensity and timing of accelerometer counts

Geraci

2022

Statistics in Medicine

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“…The panel data model can be divided into fixed effect model (FE), random effect model (RE) and mixed effect model (ME) according to different assumptions of random error term µ it [33][34][35]. The formula is as follows:…”

Section: Common Panel Data Modelmentioning

confidence: 99%

Factors Influencing the Coordinated Development of Urbanization and Its Spatial Effects: A Case Study of Beijing-Tianjin-Hebei Region

2023

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The purpose of the coordinated development of urbanization is to achieve the coordination of the internal subsystems of urbanization and the spatial coordination within the region. The coordinated development level of urbanization and its spatial effect are affected by many factors. Based on the influence mechanism of coordinated development of urbanization, in this study, the evaluation index system of coordinated development of urbanization and the analysis of influencing factors are constructed respectively. The coupling coordination model, fixed effect model and spatial lag model are used to analyze the coordinated development level and influencing factors of urbanization in Beijing-Tianjin-Hebei region, and the changes of influencing factors before and after the spatial effects are compared. The results are as follows: although the coordinated development level of urbanization in Beijing-Tianjin-Hebei region shows a trend of increasing year by year, the spatial differentiation phenomenon is becoming more and more obvious. In this process, the internal and external influencing factors of coordinated development of urbanization play a decisive role. In addition, after the spatial effect is included, the significance level of the influencing factors has changed, and the promotion effect on the coordinated development of urbanization is reduced. The performance is as follows: there is insufficient population transfer and employment among regions, low spatial spillover effect of economic development, strong dependence on land finance, expansion of habitat destruction and low degree of governance coordination, imbalance between public resource allocation and public service supply, lack of unified market-oriented environment and strong government intervention. The key points of future policies and reforms are as follows: under the guidance of the national strategic thinking, we should deepen the reform of the administrative system of the government, actively break through the institutional obstacles restricting the coordinated development of urbanization, continuously optimize the state of the urbanization system and form a joint force of coordinated development.

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“…Other models for such data include that of Rivest et al (2016), which features a consensus model for the angular response, based on circular and linear covariates, combined with vM errors. Recently, Rivest and Kato (2019) proposed a random effects circular regression model for clustered circular data where both the cluster effects and the regression errors have vM distributions. Their model is based on the multivariate angular pdf with vM-distributed cluster-level random effects of Holmquist and Gustafsson (2017).…”

Section: Circular-linear Regressionmentioning

confidence: 99%

Recent advances in directional statistics

Pewsey

García‐Portugués

2021

TEST

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Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere, and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper, we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (Wiley 1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, space situational awareness, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. AnWe are most grateful to three anonymous referees and, in alphabetical order, to

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A random‐effects model for clustered circular data

Cited by 8 publications

References 26 publications

Joint regression modelling of intensity and timing of accelerometer counts

Joint regression modelling of intensity and timing of accelerometer counts

Factors Influencing the Coordinated Development of Urbanization and Its Spatial Effects: A Case Study of Beijing-Tianjin-Hebei Region

Recent advances in directional statistics

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