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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.3
- 수록면
- 465 - 476 (12page)
- DOI
- 10.7465/jkdi.2020.31.2.465
이용수
초록· 키워드
오류제보하기
There are many difficulties in predicting problems in real life with an individual model. In order to solve these problems, we use combined these models using an ensemble learning. Combined models can be applied to the prediction of the number of days in hospital. In this paper, we use the 2011 National Sample Cohort Database to construct a combined model using an ensemble learning and predict the number of days in hospital in 2013. Also, we consider the zero inflated poisson models and hurdle poisson models considering that the days in hospital as the response variable have excess zeros. For the problem of overdispersion, we consider the zero inflated negative binomial models and hurdle negative binomial models. As baseline models, we consider the generalized additive models and the generalized linear models using the poisson and negative binomial distributions. Finally, we consider the combined models using ensemble learning. We use the National Sample Cohort Database by National Health Insurance Service and apply eight models to this data. Finally, we suggest the best prediction model by the accuracy criterion of the root mean squared logarithmic error which penalizes an under-predicted estimate greater than an over-predicted estimate.
목차
Abstract
1. Introdunction
2. National sample cohort database
3. Methodology
4. Results
5. Conclusions
References
참고문헌 (7)
참고문헌 신청
Greene, W. / 1994 / Accounting for excess zeros and sample selection in poisson and negative binomial regression models
Huber, Peter J. / 1964 / Robust estimation of a location parameter / The Annals of Mathematical Statistics 35:73~101
정지현 / 2017 / 앙상블 기법을 이용한 가뭄지수 예측 / 한국데이터정보과학회지 28(5):1125~1132
Lambert, D. / 1992 / Zero-in ated poisson regression, with an application to defects in manufacturing / Technometrics 34:1~14
Mullahy, J. / 1986 / Specification and testing of some modified count data models / Journal of Econometrics 33:341~365
Huber, Peter J. / 1964 / Robust estimation of a location parameter / The Annals of Mathematical Statistics 35:73~101
정지현 / 2017 / 앙상블 기법을 이용한 가뭄지수 예측 / 한국데이터정보과학회지 28(5):1125~1132
Lambert, D. / 1992 / Zero-in ated poisson regression, with an application to defects in manufacturing / Technometrics 34:1~14
Mullahy, J. / 1986 / Specification and testing of some modified count data models / Journal of Econometrics 33:341~365
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UCI(KEPA) : I410-ECN-0101-2020-041-000547618