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Inference for biomedical data by using diffusion models with covariates and mixed effects

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Inference for biomedical data by using diffusion models with covariates and mixed effects. / Ruse, Mareile Große; Samson, Adeline; Ditlevsen, Susanne.

I: Journal of the Royal Statistical Society. Series C: Applied Statistics, Bind 69, Nr. 1, 2020, s. 167-193.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Ruse, MG, Samson, A & Ditlevsen, S 2020, 'Inference for biomedical data by using diffusion models with covariates and mixed effects', Journal of the Royal Statistical Society. Series C: Applied Statistics, bind 69, nr. 1, s. 167-193. https://doi.org/10.1111/rssc.12386

APA

Ruse, M. G., Samson, A., & Ditlevsen, S. (2020). Inference for biomedical data by using diffusion models with covariates and mixed effects. Journal of the Royal Statistical Society. Series C: Applied Statistics, 69(1), 167-193. https://doi.org/10.1111/rssc.12386

Vancouver

Ruse MG, Samson A, Ditlevsen S. Inference for biomedical data by using diffusion models with covariates and mixed effects. Journal of the Royal Statistical Society. Series C: Applied Statistics. 2020;69(1):167-193. https://doi.org/10.1111/rssc.12386

Author

Ruse, Mareile Große ; Samson, Adeline ; Ditlevsen, Susanne. / Inference for biomedical data by using diffusion models with covariates and mixed effects. I: Journal of the Royal Statistical Society. Series C: Applied Statistics. 2020 ; Bind 69, Nr. 1. s. 167-193.

Bibtex

@article{34d3567bd1c143b2832cfb7ec0bdabdb,
title = "Inference for biomedical data by using diffusion models with covariates and mixed effects",
abstract = "Neurobiological data such as electroencephalography measurements pose a statistical challenge due to low spatial resolution and poor signal-to-noise ratio, as well as large variability from subject to subject. We propose a new modelling framework for this type of data based on stochastic processes. Stochastic differential equations with mixed effects are a popular framework for modelling biomedical data, e.g. in pharmacological studies. Whereas the inherent stochasticity of diffusion models accounts for prevalent model uncertainty or misspecification, random-effects model intersubject variability. The two-layer stochasticity, however, renders parameter inference challenging. Estimates are based on the discretized continuous time likelihood and we investigate finite sample and discretization bias. In applications, the comparison of, for example, treatment effects is often of interest. We discuss hypothesis testing and evaluate by simulations. Finally, we apply the framework to a statistical investigation of electroencephalography recordings from epileptic patients. We close the paper by examining asymptotics (the number of subjects going to ∞) of maximum likelihood estimators in multi-dimensional, non-linear and non-homogeneous stochastic differential equations with random effects and included covariates.",
keywords = "Approximate maximum likelihood, Asymptotic normality, Consistency, Covariates, Electroencephalography data, Local asymptotic normality, Mixed effects, Non-homogeneous observations, Random effects, Stochastic differential equations",
author = "Ruse, {Mareile Gro{\ss}e} and Adeline Samson and Susanne Ditlevsen",
year = "2020",
doi = "10.1111/rssc.12386",
language = "English",
volume = "69",
pages = "167--193",
journal = "Journal of the Royal Statistical Society, Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley",
number = "1",

}

RIS

TY - JOUR

T1 - Inference for biomedical data by using diffusion models with covariates and mixed effects

AU - Ruse, Mareile Große

AU - Samson, Adeline

AU - Ditlevsen, Susanne

PY - 2020

Y1 - 2020

N2 - Neurobiological data such as electroencephalography measurements pose a statistical challenge due to low spatial resolution and poor signal-to-noise ratio, as well as large variability from subject to subject. We propose a new modelling framework for this type of data based on stochastic processes. Stochastic differential equations with mixed effects are a popular framework for modelling biomedical data, e.g. in pharmacological studies. Whereas the inherent stochasticity of diffusion models accounts for prevalent model uncertainty or misspecification, random-effects model intersubject variability. The two-layer stochasticity, however, renders parameter inference challenging. Estimates are based on the discretized continuous time likelihood and we investigate finite sample and discretization bias. In applications, the comparison of, for example, treatment effects is often of interest. We discuss hypothesis testing and evaluate by simulations. Finally, we apply the framework to a statistical investigation of electroencephalography recordings from epileptic patients. We close the paper by examining asymptotics (the number of subjects going to ∞) of maximum likelihood estimators in multi-dimensional, non-linear and non-homogeneous stochastic differential equations with random effects and included covariates.

AB - Neurobiological data such as electroencephalography measurements pose a statistical challenge due to low spatial resolution and poor signal-to-noise ratio, as well as large variability from subject to subject. We propose a new modelling framework for this type of data based on stochastic processes. Stochastic differential equations with mixed effects are a popular framework for modelling biomedical data, e.g. in pharmacological studies. Whereas the inherent stochasticity of diffusion models accounts for prevalent model uncertainty or misspecification, random-effects model intersubject variability. The two-layer stochasticity, however, renders parameter inference challenging. Estimates are based on the discretized continuous time likelihood and we investigate finite sample and discretization bias. In applications, the comparison of, for example, treatment effects is often of interest. We discuss hypothesis testing and evaluate by simulations. Finally, we apply the framework to a statistical investigation of electroencephalography recordings from epileptic patients. We close the paper by examining asymptotics (the number of subjects going to ∞) of maximum likelihood estimators in multi-dimensional, non-linear and non-homogeneous stochastic differential equations with random effects and included covariates.

KW - Approximate maximum likelihood

KW - Asymptotic normality

KW - Consistency

KW - Covariates

KW - Electroencephalography data

KW - Local asymptotic normality

KW - Mixed effects

KW - Non-homogeneous observations

KW - Random effects

KW - Stochastic differential equations

UR - http://www.scopus.com/inward/record.url?scp=85074618875&partnerID=8YFLogxK

U2 - 10.1111/rssc.12386

DO - 10.1111/rssc.12386

M3 - Journal article

AN - SCOPUS:85074618875

VL - 69

SP - 167

EP - 193

JO - Journal of the Royal Statistical Society, Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society, Series C (Applied Statistics)

SN - 0035-9254

IS - 1

ER -

ID: 231900605