Inverse Gaussian regression with Broyden-Fletcher-Goldfarb Shanno (BFGS) optimization: an application of river pollution
| Main Authors: | Nisa, Eva Khoirun, Purhadi, Purhadi, Prastyo, Dedy Dwi |
|---|---|
| Format: | Proceeding PeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://eprints.walisongo.ac.id/id/eprint/19698/1/artikel%20dan%20sertifikat%20ICON%20ISHIC.pdf https://eprints.walisongo.ac.id/id/eprint/19698/ |
Daftar Isi:
- Abstract. Some cases in environmental studies show that the response variable does not follow a Gaussian distribution but belongs to an exponential family, for instance an inverse Gaussian distribution. Suppose that response variable value depend on a set of predictors, we can model it using an inverse Gaussian regression. The parameter estimation of inverse Gaussian regression requires an optimization method because the results are nonlinear. Therefore, this article provides numerically study of the estimation parameter in inverse Gaussian regression with Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization. Inverse Gaussian regression was applied to river pollution data. It was found that speed water an effect on Biochemical Oxygen Demand (BOD) levels in river pollution