Sample Size for Maximum-Likelihood Estimates of Gaussian Model Depending on Dimensionality of Pattern Space

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dc.contributor.authorPsutka, Josef
dc.contributor.authorPsutka, Josef
dc.date.accessioned2019-11-11T11:00:22Z
dc.date.available2019-11-11T11:00:22Z
dc.date.issued2019
dc.description.abstract-translatedGrowing awareness towards the sustainability has compelled supply chain domain experts to explore its relevance in this context. As a result, a number of studies in recent years have focused on investigating sustainable supply chain practices across the globe. Short food supply chains (SFSCs) have emerged as a promising sustainable alternative to the industrialized agro-food supply systems. However, academic literature hasn’t fully explored the linkage between SFSCs and sustainability. This study therefore aims to explore how SFSCs conforms to the dimensions of sustainability using the sustainability framework (social, economic, and environmental). The findings are based on a systematic literature review of 44 articles published between 2000 and 2018 selected from six electronic databases was used for the analysis. All items were properly analyzed by the researchers, seeking to identify the relationship or proximity of the information found in the papers with the SFSC concept. Our studies highlight the societal, environmental and cultural benefits of SFSC in addition to the associated economic and safety benefits. Our study thus, adds to the scant literature on SFSCs and shows a clear linkage between SFSCs and five-dimensional sustainability framework. We also propose a set of research questions that sets direction for future research.en
dc.format11 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationKUMAR, V., WANG, M., KUMARI, A., AKKARANGGOON, S., GARZA-REYES, J.A., NEUTZLING, D., TUPA, J. Exploring short food supply chains from triple bottom line lens: A comprehensive systematic review. In: Proceedings of the International Conference on Industrial Engineering and Operations Management (IEOM 2019). Michigan: IEOM Society International, 2019. s. 728-738. ISBN 978-1-5323-5948-4 , ISSN 2169-8767.en
dc.identifier.document-number466250400003
dc.identifier.doi10.1016/j.patcog.2019.01.046
dc.identifier.isbn978-1-5323-5948-4
dc.identifier.issn0031-3203
dc.identifier.obd43926841
dc.identifier.uri2-s2.0-85067239868
dc.identifier.urihttp://hdl.handle.net/11025/35857
dc.language.isoenen
dc.publisherIEOM Society Internationalen
dc.relation.ispartofseriesProceedings of the International Conference on Industrial Engineering and Operations Management (IEOM 2019)en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEOM Society Internationalen
dc.rights.accessrestrictedAccessen
dc.subjectMaximálně věrohodný odhadcs
dc.subjectvěrohodnostní funkcecs
dc.subjectGaussovský modelcs
dc.subjectGMMcs
dc.subjectvelikost datcs
dc.subjectdimenzionalitacs
dc.subjectobrazový prostrorcs
dc.subjectheterosceadistická datacs
dc.subject.translatedMaximum-likelihood estimateen
dc.subject.translatedLikelihood functionen
dc.subject.translatedGaussian modelen
dc.subject.translatedGaussian mixture modelen
dc.subject.translatedSample sizeen
dc.subject.translatedDimensionalityen
dc.subject.translatedPattern spaceen
dc.subject.translatedHeteroscedastic dataen
dc.titleSample Size for Maximum-Likelihood Estimates of Gaussian Model Depending on Dimensionality of Pattern Spaceen
dc.title.alternativePočet dat pro maximálně věrohodný odhad Gaussovského modelu v závislosti na dimenzi obrazového prostoru.cs
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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