The EM algorithm for graphical association models with missing data

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Standard

The EM algorithm for graphical association models with missing data. / Lauritzen, Steffen L.

I: Computational Statistics & Data Analysis, Bind 19, Nr. 2, 1995, s. 191-201.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Lauritzen, SL 1995, 'The EM algorithm for graphical association models with missing data', Computational Statistics & Data Analysis, bind 19, nr. 2, s. 191-201. https://doi.org/10.1016/0167-9473(93)E0056-A

APA

Lauritzen, S. L. (1995). The EM algorithm for graphical association models with missing data. Computational Statistics & Data Analysis, 19(2), 191-201. https://doi.org/10.1016/0167-9473(93)E0056-A

Vancouver

Lauritzen SL. The EM algorithm for graphical association models with missing data. Computational Statistics & Data Analysis. 1995;19(2):191-201. https://doi.org/10.1016/0167-9473(93)E0056-A

Author

Lauritzen, Steffen L. / The EM algorithm for graphical association models with missing data. I: Computational Statistics & Data Analysis. 1995 ; Bind 19, Nr. 2. s. 191-201.

Bibtex

@article{d659bee7069f485c84c284ea1521fa9a,
title = "The EM algorithm for graphical association models with missing data",
abstract = "It is shown how the computational scheme of Lauritzen and Spiegelhalter (1988) can be exploited to perform the E-step of the EM algorithm when applied to finding maximum likelihood estimates or penalized maximum likelihood estimates in hierarchical log-linear models and recursive models for contingency tables with missing data. The generalization to mixed association models introduced in Lauritzen and Wermuth (1989) and Edwards (1990) is indicated.",
author = "Lauritzen, {Steffen L.}",
year = "1995",
doi = "10.1016/0167-9473(93)E0056-A",
language = "English",
volume = "19",
pages = "191--201",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",
number = "2",

}

RIS

TY - JOUR

T1 - The EM algorithm for graphical association models with missing data

AU - Lauritzen, Steffen L.

PY - 1995

Y1 - 1995

N2 - It is shown how the computational scheme of Lauritzen and Spiegelhalter (1988) can be exploited to perform the E-step of the EM algorithm when applied to finding maximum likelihood estimates or penalized maximum likelihood estimates in hierarchical log-linear models and recursive models for contingency tables with missing data. The generalization to mixed association models introduced in Lauritzen and Wermuth (1989) and Edwards (1990) is indicated.

AB - It is shown how the computational scheme of Lauritzen and Spiegelhalter (1988) can be exploited to perform the E-step of the EM algorithm when applied to finding maximum likelihood estimates or penalized maximum likelihood estimates in hierarchical log-linear models and recursive models for contingency tables with missing data. The generalization to mixed association models introduced in Lauritzen and Wermuth (1989) and Edwards (1990) is indicated.

U2 - 10.1016/0167-9473(93)E0056-A

DO - 10.1016/0167-9473(93)E0056-A

M3 - Journal article

VL - 19

SP - 191

EP - 201

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

IS - 2

ER -

ID: 127873789