Additive Quantile Regression for Clustered Data with an Application to Children's Physical Activity

Geraci, Marco

doi:10.1111/rssc.12333

Cited by 17 publications

(33 citation statements)

References 97 publications

(157 reference statements)

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“…Thus, it emerged as an effective analytic technique in numerous study areas of science due to its competence to drive inferences about individuals that rank below or above the conditional population mean and/or focused on features of the response beyond its central tendency 4 – 13 . QR is specifically appropriate for the parameters' heterogeneous effect as it yields inferences that can be legitimate irrespective of the true underlying distribution 4 , 14 . QR techniques look further into the data, get more information, and become more important 15 .…”

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confidence: 99%

“…Additive mixed models (AMMs), an extension of additive models, have been developed precisely to incorporate linear and nonlinear effects, as well as random terms when the data are sampled according to longitudinal designs 4 , 17 . AMMs have been integrated into QR methods to obtain robust results, not only focused on features of the longitudinal outcome at its central tendency that may not be the best location to characterize the data specifically when the errors are non-normally distributed, and the location-shift hypothesis of the normal model is violated but also at conditional quantiles of the longitudinal outcome with no assumption about the response or errors distribution apart from the distribution is restricted to have the quantile to be zero.…”

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confidence: 99%

“…AMMs have been integrated into QR methods to obtain robust results, not only focused on features of the longitudinal outcome at its central tendency that may not be the best location to characterize the data specifically when the errors are non-normally distributed, and the location-shift hypothesis of the normal model is violated but also at conditional quantiles of the longitudinal outcome with no assumption about the response or errors distribution apart from the distribution is restricted to have the quantile to be zero. Thus, additive quantile mixed models, which have gained popularity recently as a general method for longitudinal data, bring a comprehensive and more complete picture of the nonparametric as well as the parametric effects 1 , 4 .…”

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confidence: 99%

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Additive quantile mixed effects modelling with application to longitudinal CD4 count data

Yirga

Melesse

Mwambi

et al. 2021

Sci Rep

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Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean. Quantile regression for mixed-effects models has become practical for longitudinal data analysis due to the recent computational advances and the ready availability of efficient linear programming algorithms. Recently, quantile regression has also been extended to additive mixed-effects models, providing an efficient and flexible framework for nonparametric as well as parametric longitudinal forms of data analysis focused on features of the outcome beyond its central tendency. This study applies the additive quantile mixed model to analyze the longitudinal CD4 count of HIV-infected patients enrolled in a follow-up study at the Centre of the AIDS Programme of Research in South Africa. The objective of the study is to justify how the procedure developed can obtain robust nonlinear and linear effects at different conditional distribution locations. With respect to time and baseline BMI effect, the study shows a significant nonlinear effect on CD4 count across all fitted quantiles. Furthermore, across all fitted quantiles, the effect of the parametric covariates of baseline viral load, place of residence, and the number of sexual partners was found to be major significant factors on the progression of patients’ CD4 count who had been initiated on the Highly Active Antiretroviral Therapy study.

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Additive quantile mixed effects modelling with application to longitudinal CD4 count data

Yirga

Melesse

Mwambi

et al. 2021

Sci Rep

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“…Prior to the construction of flow–ecology relationships, all hydrological indices were standardized to z‐scores and extreme outliers identified from interquartile range (IQR) values (defined as observations that fall below Q1 – 3 × IQR or above Q3 + 3 × IQR) removed. Quantile regression (QR) and quantile mixed‐effect regression (QMR) analyses were performed using the lqmm package (Geraci, ). Within each QMR, ‘river’ was used as a random effect to account for potential temporal autocorrelation (see above).…”

Section: Methodsmentioning

confidence: 99%

Flow regimes control the establishment of invasive crayfish and alter their effects on lotic macroinvertebrate communities

Mathers

White

Fornaroli

et al. 2020

Journal of Applied Ecology

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1. Invasive non-native species (INNS) threaten biodiversity and ecosystem functioning globally. However, there remains a pressing need to understand the environmental factors controlling the dispersal, successful establishment and subsequent ecological impacts of INNS for receiving ecosystems. Here, we examine how region-wide flow regime magnitudes facilitate the successful establishment of an invasive crayfish species (Pacifastacus leniusculus, signal crayfish) in England (UK). We also consider the interactive effects of invasive crayfish with flow regime variations on the structural and functional diversity of macroinvertebrate communities. 2. Low-flow magnitudes increased the likelihood of P. leniusculus establishment, with 80% of recorded invasion dates falling in years with flow magnitudes below average (low-and low-moderate flow classes), whilst only 1.6% occurred in high-flow years. 3. Temporal trajectories of structural and functional macroinvertebrate responses in invaded rivers demonstrated reduced diversity compared to control rivers. Lower taxonomic and functional richness measures typically coincided with periods of low discharge in invaded rivers and were greatest during regionally high-flows. 4. Macroinvertebrate communities displayed significant structural and functional responses to the interaction between invasive crayfish and flow regime variations.Specifically, a number of low-and high-flow indices yielded significant associations, highlighting the role of extreme hydrological events in shaping INNS effects on receiving ecosystems. We also detected greater ecological effects of invasive crayfish under hydrologically stable conditions. Importantly, and for the first time, we observed that invasive crayfish reversed macroinvertebrate community responses to flow regime cues (e.g. discharge fall rate and minimum flows in the preceding 180 days). 5. Synthesis and applications. Results from this study indicate that low-flow events facilitate the spread/establishment of invasive crayfish and correspond with greater ecological effects for receiving ecosystems. Given that low-flow events are predicted to increase in intensity, duration and frequency over the 21st century, our results highlight the potential threat that invasive crayfish may pose under future hydroclimatic changes. Managing river flow regimes effectively (including | Additional supporting information may be found online in the Supporting Information section. How to cite this article: Mathers KL, White JC, Fornaroli R, Chadd R. Flow regimes control the establishment of invasive crayfish and alter their effects on lotic macroinvertebrate communities. J Appl Ecol. 2020;57:886-902. https://doi.

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“…The approach of Witowski et al (2014) assumes homogeneity of the population and does not consider missingness within the observations. However, heterogeneity in physical activity behaviors is often present (see, for instance, Geraci (2018)) and the use of more than one HMM allows it to be taken into account (see, e.g., Van de Pol and Langeheine (1990)). Clustering enables the heterogeneity of the population to be addressed by grouping observations into a few homogeneous classes.…”

Section: Introductionmentioning

confidence: 99%

Mixture of hidden Markov models for accelerometer data

Chaumaray¹,

Marbac²,

Navarro³

2020

Ann. Appl. Stat.

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Additive Quantile Regression for Clustered Data with an Application to Children's Physical Activity

Cited by 17 publications

References 97 publications

Additive quantile mixed effects modelling with application to longitudinal CD4 count data

Additive quantile mixed effects modelling with application to longitudinal CD4 count data

Flow regimes control the establishment of invasive crayfish and alter their effects on lotic macroinvertebrate communities

Mixture of hidden Markov models for accelerometer data

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