Imputation of missing data and Inference by EM algorithm

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DC Field	Value	Language
dc.contributor.author	Abbaci, Safaa	-
dc.contributor.author	Saidi, Manel	-
dc.contributor.author	Rassoul, Abdelaziz (Promoteur)	-
dc.date.accessioned	2022-09-29T12:16:50Z	-
dc.date.available	2022-09-29T12:16:50Z	-
dc.date.issued	2022-07	-
dc.identifier.uri	https://di.univ-blida.dz/jspui/handle/123456789/19532	-
dc.description	ill., Bibliogr. Cote: ma-510-135	fr_FR
dc.description.abstract	Missing data is a major issue in many applied problems. in our work we examine data that are missing and . the aims of multiple imputation in comparison to single imputation, we also studying the statistical inference by likelihood Maximum method for sample with missing data with Maximization-Expectation algorithm. Finally, we present the mains packages for imputation of missing data, and we applied the EM algorithm for Mixture Gaussian model Keywords: expectation maximization algorithm, missing data, imputation, maximum likelihood Method	fr_FR
dc.language.iso	en	fr_FR
dc.publisher	Université Blida 1	fr_FR
dc.subject	expectation maximization algorithm	fr_FR
dc.subject	missing data	fr_FR
dc.subject	imputation	fr_FR
dc.subject	maximum likelihood Method	fr_FR
dc.title	Imputation of missing data and Inference by EM algorithm	fr_FR
dc.type	Thesis	fr_FR
Appears in Collections:	Mémoires de Master

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