Semi-parametric estimation of random effects in a logistic regression model using conditional inference

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Standard

Semi-parametric estimation of random effects in a logistic regression model using conditional inference. / Petersen, Jørgen Holm.

I: Statistics in Medicine, Bind 35, Nr. 1, 15.01.2016, s. 41-52.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Petersen, JH 2016, 'Semi-parametric estimation of random effects in a logistic regression model using conditional inference', Statistics in Medicine, bind 35, nr. 1, s. 41-52. https://doi.org/10.1002/sim.6611

APA

Petersen, J. H. (2016). Semi-parametric estimation of random effects in a logistic regression model using conditional inference. Statistics in Medicine, 35(1), 41-52. https://doi.org/10.1002/sim.6611

Vancouver

Petersen JH. Semi-parametric estimation of random effects in a logistic regression model using conditional inference. Statistics in Medicine. 2016 jan. 15;35(1):41-52. https://doi.org/10.1002/sim.6611

Author

Petersen, Jørgen Holm. / Semi-parametric estimation of random effects in a logistic regression model using conditional inference. I: Statistics in Medicine. 2016 ; Bind 35, Nr. 1. s. 41-52.

Bibtex

@article{b2e888d059a84367895229367aebb328,

title = "Semi-parametric estimation of random effects in a logistic regression model using conditional inference",

abstract = "This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied. For each term in the composite likelihood, a conditional likelihood is used that eliminates the influence of the random effects, which results in a composite conditional likelihood consisting of only one-dimensional integrals that may be solved numerically. Good properties of the resulting estimator are described in a small simulation study.",

author = "Petersen, {J{\o}rgen Holm}",

note = "Copyright {\textcopyright} 2015 John Wiley & Sons, Ltd.",

year = "2016",

month = jan,

day = "15",

doi = "10.1002/sim.6611",

language = "English",

volume = "35",

pages = "41--52",

journal = "Statistics in Medicine",

issn = "0277-6715",

publisher = "JohnWiley & Sons Ltd",

number = "1",

}

RIS

TY - JOUR

T1 - Semi-parametric estimation of random effects in a logistic regression model using conditional inference

AU - Petersen, Jørgen Holm

PY - 2016/1/15

Y1 - 2016/1/15

N2 - This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied. For each term in the composite likelihood, a conditional likelihood is used that eliminates the influence of the random effects, which results in a composite conditional likelihood consisting of only one-dimensional integrals that may be solved numerically. Good properties of the resulting estimator are described in a small simulation study.

AB - This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied. For each term in the composite likelihood, a conditional likelihood is used that eliminates the influence of the random effects, which results in a composite conditional likelihood consisting of only one-dimensional integrals that may be solved numerically. Good properties of the resulting estimator are described in a small simulation study.

U2 - 10.1002/sim.6611

DO - 10.1002/sim.6611

M3 - Journal article

C2 - 26265116

VL - 35

SP - 41

EP - 52

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 1

ER -

ID: 161080361