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dc.contributor.authorYılmaz, Ersin
dc.contributor.authorAydın, Dursun
dc.contributor.authorAhmed, Syed Ejaz
dc.date.accessioned2023-10-03T10:22:25Z
dc.date.available2023-10-03T10:22:25Z
dc.date.issued2023en_US
dc.identifier.citationYılmaz, E.; Aydın, D.; Ahmed, S.E. Modified Local Linear Estimators in Partially Linear Additive Models with Right-Censored Data Based on Different Censorship Solution Techniques. Entropy 2023, 25, 1307. https://doi.org/10.3390/e25091307en_US
dc.identifier.urihttps://doi.org/10.3390/e25091307
dc.identifier.urihttps://hdl.handle.net/20.500.12809/10997
dc.description.abstractThis paper introduces a modified local linear estimator (LLR) for partially linear additive models (PLAM) when the response variable is subject to random right-censoring. In the case of modeling right-censored data, PLAM offers a more flexible and realistic approach to the estimation procedure by involving multiple parametric and nonparametric components. This differs from the widely used partially linear models that feature a univariate nonparametric function. The LLR method is employed to estimate unknown smooth functions using a modified backfitting algorithm, delivering a non-iterative solution for the right-censored PLAM. To address the censorship issue, three approaches are employed: synthetic data transformation (ST), Kaplan-Meier weights (KMW), and the kNN imputation technique (kNNI). Asymptotic properties of the modified backfitting estimators are detailed for both ST and KMW solutions. The advantages and disadvantages of these methods are discussed both theoretically and practically. Comprehensive simulation studies and real-world data examples are conducted to assess the performance of the introduced estimators. The results indicate that LLR performs well with both KMW and kNNI in the majority of scenarios, along with a real data exampleen_US
dc.item-language.isoengen_US
dc.publisherMDPIen_US
dc.relation.isversionof10.3390/e25091307en_US
dc.item-rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPartially linear additive modelsen_US
dc.subjectLocal linear regressionen_US
dc.subjectRight-censored dataen_US
dc.titleModified Local Linear Estimators in Partially Linear Additive Models with Right-Censored Data Based on Different Censorship Solution Techniquesen_US
dc.item-typearticleen_US
dc.contributor.departmentMÜ, Fen Fakültesi, İstatistik Bölümüen_US
dc.contributor.authorID0000-0002-9871-4700en_US
dc.contributor.authorID0000-0001-8393-1270en_US
dc.contributor.institutionauthorYılmaz, Ersin
dc.contributor.institutionauthorAydın, Dursun
dc.identifier.volume25en_US
dc.identifier.issue9en_US
dc.identifier.startpage1307en_US
dc.relation.journalEntropy (Basel)en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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