MODEL ADITIF TERGENERALISASI
Modeling relationship between respond variable and predictor is not always following linearity and normality assumption. Hastie and Tibshirani (1986) adapted additive models to generalized linear models that is called by generalized additive models. Generalized additive models is modeling technique...
| Main Authors: | , |
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| Format: | Thesis |
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[Yogyakarta] : Universitas Gadjah Mada
2013
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| _version_ | 1797033860912906240 |
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| author | , NURLIA HIKMANANDA , Prof. Drs. Subanar, Ph.D. |
| author_facet | , NURLIA HIKMANANDA , Prof. Drs. Subanar, Ph.D. |
| author_sort | , NURLIA HIKMANANDA |
| collection | UGM |
| description | Modeling relationship between respond variable and predictor is not always
following linearity and normality assumption. Hastie and Tibshirani (1986) adapted
additive models to generalized linear models that is called by generalized additive
models. Generalized additive models is modeling technique that appropriates for
overcomes nonlinearity in the relationship between respond variable and predictor,
and does not limited respond variable to normal distribution but other distributions
in the exponential family allowing to use in this model.
Generalized additive models replace linear component on the generalized
linear models with sum of functions which is estimated using local scoring algorithm.
Additive component in generalized additive models is sum of univariat function of
every predictors, so we can see contribution of each predictor to respond. Illustration
is given in case study using R software. |
| first_indexed | 2024-03-13T23:01:36Z |
| format | Thesis |
| id | oai:generic.eprints.org:122894 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T23:01:36Z |
| publishDate | 2013 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:1228942016-03-04T08:41:19Z https://repository.ugm.ac.id/122894/ MODEL ADITIF TERGENERALISASI , NURLIA HIKMANANDA , Prof. Drs. Subanar, Ph.D. ETD Modeling relationship between respond variable and predictor is not always following linearity and normality assumption. Hastie and Tibshirani (1986) adapted additive models to generalized linear models that is called by generalized additive models. Generalized additive models is modeling technique that appropriates for overcomes nonlinearity in the relationship between respond variable and predictor, and does not limited respond variable to normal distribution but other distributions in the exponential family allowing to use in this model. Generalized additive models replace linear component on the generalized linear models with sum of functions which is estimated using local scoring algorithm. Additive component in generalized additive models is sum of univariat function of every predictors, so we can see contribution of each predictor to respond. Illustration is given in case study using R software. [Yogyakarta] : Universitas Gadjah Mada 2013 Thesis NonPeerReviewed , NURLIA HIKMANANDA and , Prof. Drs. Subanar, Ph.D. (2013) MODEL ADITIF TERGENERALISASI. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=63003 |
| spellingShingle | ETD , NURLIA HIKMANANDA , Prof. Drs. Subanar, Ph.D. MODEL ADITIF TERGENERALISASI |
| title | MODEL ADITIF TERGENERALISASI |
| title_full | MODEL ADITIF TERGENERALISASI |
| title_fullStr | MODEL ADITIF TERGENERALISASI |
| title_full_unstemmed | MODEL ADITIF TERGENERALISASI |
| title_short | MODEL ADITIF TERGENERALISASI |
| title_sort | model aditif tergeneralisasi |
| topic | ETD |
| work_keys_str_mv | AT nurliahikmananda modeladitiftergeneralisasi AT profdrssubanarphd modeladitiftergeneralisasi |