Marginal Logistic Regression for Spatially Clustered Binary Data

Cattelan, Manuela; Varin, Cristiano

doi:10.1111/rssc.12270

Cited by 3 publications

(3 citation statements)

References 49 publications

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“…In this analysis, the probabilities and the odds ratio are used to interpret the coefficients. The probability ratio Ω is the correlation of the probability value calculated for x = 1, between the probability value calculated for x = 0 [59]. According to this, the odds ratio can be written as follows:…”

Section: Data Collection and Analysis Processmentioning

confidence: 99%

Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions

2021

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The goal of this study is to analyze how religion, ethnic group, and race influence the likelihood of becoming either a cybervictim or cyberbully in intercultural educational environments. In the research, 755 students in secondary education were analyzed in the south of Spain through the Cyberbullying Scale for students with Cultural and Religious Diversity (CSCRD). The analyses were carried out using the Statistical Package SPSS and the STATA software. The results obtained from the Kruskal–Wallis H test showed significant differences according to these aspects, for both the cybervictim and cyberbully parameters. The results stemming from binary logistic regressions confirmed such differences and regarded those students who belong to the Muslim religion, the gypsy ethnic group and the Asian race as being more likely to become cybervictims. Furthermore, these analyses showed that Gypsy and Asian students were also more likely to be cyberbullies than other groups. The main conclusions state that minority groups are more likely to suffer cyberbullying in intercultural educational environments, and that students from these groups are also more likely to become cyberbullies.

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Section: Data Collection and Analysis Processmentioning

confidence: 99%

Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions

2021

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“…In principle, it is possible to simultaneously model the mean and covariance structure for binary responses, with the latter parameterized in terms of pairwise log‐odds ratios (Albert & Mcshane, ; Carey, Zeger, & Diggle, ; Cattelan & Varin, ; Heagerty & Zeger, ). This approach has the advantage of providing covariate‐adjusted lorelogram estimators that account for non‐stationarity, while also guarding against type I errors when making inferences about mean parameters.…”

Section: Other Potential Applications and Future Researchmentioning

confidence: 99%

Using lorelograms to measure and model correlation in binary data: Applications to ecological studies

et al. 2019

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Tools for describing correlation structures in binary data are underrepresented in the ecological literature, and methods commonly applied to continuous data are inappropriate because of intrinsic features of binary data (e.g. variance dependent on the mean). Describing how correlation changes in time and space, or in response to different stimuli, can provide insights into the ecological processes generating observed patterns. Moreover, proper modelling of correlation structures is necessary for reliably estimating covariate effects. We introduce ecologists to the lorelogram (Heagerty & Zeger, 1998), a tool used to identify and describe dependency structures in binary data. Lorelograms can help guide data aggregation efforts to facilitate independence, or alternatively, to inform appropriate structures for modelling correlated data. We developed the r‐package, lorelogram, for estimating and plotting lorelograms and show how it can be used to identify various dependency structures using simulated data. We analyse data from the North American Breeding Bird Survey and camera trap data from a carnivore study in Minnesota to demonstrate how the lorelogram can describe spatial and temporal dependencies, respectively. In the latter case, we show how the lorelogram can identify short‐term dependencies (e.g. individuals that linger at the camera site for several minutes), longer‐term dependencies (i.e. diel activity patterns) and effects of site‐specific covariates such as attractants. We then illustrate how this information can be incorporated in a modelling framework that accounts for these correlation structures (e.g. when modelling daily activity patterns). The lorelogram is a promising tool for quantifying correlation in binary data over space or time. In addition to the applications presented in our paper, it could be used to identify independent sampling units for occupancy modelling or to quantify behavioural responses to covariates such as anthropogenic stressors or recent presence of prey, predators or competitors.

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“…In the last several decades, there are a large number of works on GEE for analyzing longitudinal data; see Hardin and Hilbe (2012) for an overview. The GEE has also been used for Statistica Sinica: Newly accepted Paper (accepted author-version subject to English editing) analyzing spatially dependent data; see Oman et al (2007), Lin (2008), Lin (2010), Thurman et al (2015), Adegboye et al (2018) and Cattelan and Varin (2018). We aim to develop a flexible and computationally feasible GEE method to deal with spatial data with complex spatial dependence, which is commonly seen in practice (Guan et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

A Mixture Generalized Estimating Equations Approach for Complex Spatially-Dependent Data

Zhu,

Lin,

Wang

et al. 2026

STAT SINICA

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The generalized estimating equations (GEE) method is a popular approach for analyzing dependent data of various types. While GEE estimators are robust against the misspecification of the correlation matrix, their estimation efficiency can be seriously affected by the choice of the working correlation matrix.For spatially correlated data, it is difficult to specify the true spatial correlation structure due to the complexity of dependence and the high dimension of spatial correlation matrices. To achieve estimation efficiency while allowing flexibility to capture complex spatial dependence, we propose a new GEE-type approach based on a mixture of spatial working correlation matrices, referred to as mix-GEE. We show that the mix-GEE estimator is asymptotically efficient without any parametric assumption on the distribution as long as one of the candidate

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Marginal Logistic Regression for Spatially Clustered Binary Data

Cited by 3 publications

References 49 publications

Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions

Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions

Using lorelograms to measure and model correlation in binary data: Applications to ecological studies

A Mixture Generalized Estimating Equations Approach for Complex Spatially-Dependent Data

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