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목차 1
식중독 발생 건수에 대한 계층 시계열 예측 = Forecasting hierarchical time series for foodborne disease outbreaks / 여인권 1
Abstract 1
1. 서론 1
2. 제안 방법론 2
2.1. 원인균별 식중독 건수 추정 3
2.2. 주요, 기타, 전체 식중독 건수 추정 4
2.3. 계층구조 하에서의 적합값과 예측값 조정 4
3. 실증분석 6
4. 결론 8
References 9
요약 10
| 번호 | 참고문헌 | 국회도서관 소장유무 |
|---|---|---|
| 1 | Agarwal DK, Gelfand AE, and Citron-Pousty S (2002). Zero-inflated models with application to spatial count data, Environmental and Ecological Statistics, 9, 341–355. | 미소장 |
| 2 | Choi K, Kim B, Bae W, Jung W, and Cho Y (2008). Developing the index of foodborne disease occurrence, The Korean Journal of Applied Statistics, 21, 649–658. | 미소장 |
| 3 | Dunn DM, Williams WH, and DeChaine TL (1976). Aggregate versus subaggregate models in local area forecasting, Journal of the American Statistical Association, 71, 68–71. | 미소장 |
| 4 | Fleury M, Charron DF, Holt JD, Allen OB, and Maarouf AR (2006). A time series analysis of the relationship of ambient temperature and common bacterial enteric infections in two Canadian provinces, International Journal of Biometeorology, 50, 385–391. | 미소장 |
| 5 | Hyndman RJ, Ahmed RA, Athanasopoulos G, and Shang HL (2011). Optimal combination forecasts for hierarchical time series, Computational Statistics and Data Analysis, 55, 2579–2589. | 미소장 |
| 6 | Jung HS., Kim BJ, Cho S, and Yeo, IK (2012). Analysis of food poisoning via zero inflation models, The Korean Journal of Applied Statistics, 25, 859–864. | 미소장 |
| 7 | Jeong MS and Oh SS (2009). Study on the Impact Analysis and Control System of Foodborne Disease Outbreak due to Climate Change, Korea Food & Drug Administration, Cheongju-si. | 미소장 |
| 8 | Kim S, Seong B, Choi YG, and Yeo IK (2022). A study on time series linkage in the household income and expenditure survey, The Korean Journal of Applied Statistics, 35, 553–568. | 미소장 |
| 9 | Lambert D (1992). Zero-inflated Poisson regression models with an application to defects in manufacturing, Technometrics, 34, 1–14. | 미소장 |
| 10 | Orcutt GH, Watts HW, and Edwards JB (1968). Data aggregation and information loss, The American Economic Review, 58, 773–787. | 미소장 |
| 11 | Shlifer E and Wolff RW (1979). Aggregation and proration in forecasting, Management Science, 25, 594–603. | 미소장 |
| 12 | Wickramasuriya SL, Athanasopoulos G, and Hyndman RJ (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization, Journal of the American Statistical Association, 114, 804–819 | 미소장 |
| 13 | Yeo IK (2012). Models for forecasting food poisoning occurrences, Journal of the Korean Data & Information Science Society, 23, 1117–1125. | 미소장 |
| 14 | Yeo IK (2013). Prediction of the number of food poisoning occurrences by microbes, The Korean Journal of Applied Statistics, 26, 923–932. | 미소장 |
| 15 | Zhang Y, Bi P, and Hiller JE (2008). Climate variations and salmonellosis transmission in Adelaide, South Australia:A comparison between regression models, International Journal of Biometeorology, 52, 179–187. | 미소장 |
| 16 | Zhang Y, Bi P, Hiller JE, Sun Y, and Ryan P (2007). Climate variations and bacillary dysentery in northern and southern cities of China, The Journal of Infection, 55, 194–200. | 미소장 |
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