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Fitting the Multinomial model with continuous covariates in GLIM.

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Fitting the Multinomial model with continuous covariates in GLIM. / Aitkin, Murray; Francis, Brian.

In: Computational Statistics and Data Analysis, Vol. 14, No. 1, 06.1992, p. 89-97.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Aitkin, M & Francis, B 1992, 'Fitting the Multinomial model with continuous covariates in GLIM.', Computational Statistics and Data Analysis, vol. 14, no. 1, pp. 89-97. https://doi.org/10.1016/0167-9473(92)90083-R

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Aitkin, Murray ; Francis, Brian. / Fitting the Multinomial model with continuous covariates in GLIM. In: Computational Statistics and Data Analysis. 1992 ; Vol. 14, No. 1. pp. 89-97.

Bibtex

@article{58e3b54336c5418bb3814e02a7e2b8a5,
title = "Fitting the Multinomial model with continuous covariates in GLIM.",
abstract = "A standard method for fitting the multinomial logit model, used in some statistical packages, is to represent it in terms of the equivalent Poisson log-linear model. The constraint necessary for this equivalence requires the inclusion of a set of nuisance parameters in the Poisson model, of dimension equal to the number of distinct values of the set of covariates. In such packages the model is therefore restricted to the analysis of categorical covariates, i.e. contingency tables. This paper describes a method for fitting the multinomial logit model which requires only a simple scoring algorithm, but does not use the equivalent Poisson model, and can be used with continuous covariates with an unlimited number of distinct values. The method is implemented as a set of GLIM macros. An example is discussed.",
keywords = "Multinomial logit model, Poisson log-linear model , Continuous covariates , GLIM",
author = "Murray Aitkin and Brian Francis",
year = "1992",
month = jun,
doi = "10.1016/0167-9473(92)90083-R",
language = "English",
volume = "14",
pages = "89--97",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Fitting the Multinomial model with continuous covariates in GLIM.

AU - Aitkin, Murray

AU - Francis, Brian

PY - 1992/6

Y1 - 1992/6

N2 - A standard method for fitting the multinomial logit model, used in some statistical packages, is to represent it in terms of the equivalent Poisson log-linear model. The constraint necessary for this equivalence requires the inclusion of a set of nuisance parameters in the Poisson model, of dimension equal to the number of distinct values of the set of covariates. In such packages the model is therefore restricted to the analysis of categorical covariates, i.e. contingency tables. This paper describes a method for fitting the multinomial logit model which requires only a simple scoring algorithm, but does not use the equivalent Poisson model, and can be used with continuous covariates with an unlimited number of distinct values. The method is implemented as a set of GLIM macros. An example is discussed.

AB - A standard method for fitting the multinomial logit model, used in some statistical packages, is to represent it in terms of the equivalent Poisson log-linear model. The constraint necessary for this equivalence requires the inclusion of a set of nuisance parameters in the Poisson model, of dimension equal to the number of distinct values of the set of covariates. In such packages the model is therefore restricted to the analysis of categorical covariates, i.e. contingency tables. This paper describes a method for fitting the multinomial logit model which requires only a simple scoring algorithm, but does not use the equivalent Poisson model, and can be used with continuous covariates with an unlimited number of distinct values. The method is implemented as a set of GLIM macros. An example is discussed.

KW - Multinomial logit model

KW - Poisson log-linear model

KW - Continuous covariates

KW - GLIM

U2 - 10.1016/0167-9473(92)90083-R

DO - 10.1016/0167-9473(92)90083-R

M3 - Journal article

VL - 14

SP - 89

EP - 97

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

IS - 1

ER -