Communications for Statistical Applications and Methods

  • 제18권5호
  • /
  • Pages.571-579
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  • 2011
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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서로 다른 산포를 허용하는 이변량 영과잉 음이항 회귀모형

Bivariate Zero-Inflated Negative Binomial Regression Model with Heterogeneous Dispersions

  • 투고 : 20110700
  • 심사 : 20110800
  • 발행 : 2011.09.30
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초록

본 연구에서는 두 반응 변수에 서로 다른 산포를 허용하는 새로운 이변량 영과잉 음이항 회귀모형을 제안하고, Deb과 Trivedi (1997)에 나타난 헬스케어 자료를 이용하여 두 반응변수가 갖는 서로 다른 산포도를 무시한 Wang (2003)이 제안한 이변량 영과잉 음이항 회귀모형과의 효율성을 로그우도와 AIC의 관점에서 비교 하였다. 모형적합결과, 본 연구에서 제안한 모형이 모형선택기준 관점에서 기존모형에 비하여 월등히 우수한 결과를 보여주었다.

We propose a new bivariate zero-inflated negative binomial regression model to allow heterogeneous dispersions. To show the performance of our proposed model, Health Care data in Deb and Trivedi (1997) are used to compare it with the other bivariate zero-inflated negative binomial model proposed by Wang (2003) that has a common dispersion between the two response variables. This empirical study shows better results from the views of log-likelihood and AIC.

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참고문헌

  1. 이동희, 정병철 (2010). 코폴라를 활용한 이변량 제로팽창 일반화 포아송 회귀모형, Journal of the Korean Data Analysis Society, 12, 1473-1484.
  2. Cameron, A. C., Li, T., Trivedi, P. K. and Zimmer, D. M. (2004). Modelling the differences in counted outcomes using bivariate copula models with application to mismeasured counts, Econometrics Journal, 7, 566-584. https://doi.org/10.1111/j.1368-423X.2004.00144.x
  3. Deb, P. and Trivedi, P. K. (1997). Demand for medical care by the elderly: A finite mixture approach, Journal of Applied Econometrics, 12, 313-336. https://doi.org/10.1002/(SICI)1099-1255(199705)12:3<313::AID-JAE440>3.0.CO;2-G
  4. Dempster, A. P., Laird, N. M., Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discusiion), Journal of the Royal Statistical Society B, 39, 1-38.
  5. Gupta, P. L., Gupta, R. C. and Tripathi, R. C. (2004). Score test for zero inflated generalized Poisson regression model, Communications in Statistics - Theory and Methods, 33, 47-64. https://doi.org/10.1081/STA-120026576
  6. Gurmu, S. and Elder, J. (2000). Generalized bivariate Count data regression models, Economics Letters, 68, 31-36. https://doi.org/10.1016/S0165-1765(00)00225-1
  7. Lee, J., Jung, B. C. and Jin, S. H. (2007). Tests for zero inflation in a bivariate zero-inflated Poisson model, Statistica Neerlandica, 63, 400-417. https://doi.org/10.1111/j.1467-9574.2009.00430.x
  8. Li, C. S., Lu, J. C., Park, J., Kim, K., Brinkley, P. A. and Peterson, J. (1999). Multivariate zero-inflated Poisson models and their applications, Technometrics, 41, 29-38. https://doi.org/10.2307/1270992
  9. Marshall, A. W. and Olkin, I. (1990). Multivariate distributions generated from mixtures of convolution and product families, In H.W. Block, A.R. Sampson and T.H. Savits(eds), Topics in Statistical Dependence, 372-393. IMS Lecture Notes - Monograph Series, 16.
  10. Munkin, M. K. and Trivedi, P. K. (1999). Simulated maximum likelihood estimation of multivariate mixed-Poisson regression models, with application, Econometrics Journal, 2, 29-48. https://doi.org/10.1111/1368-423X.00019
  11. So, S., Chun, H. and Jung, B. C. (2011). Bivariate negative binomial regression model with heterogeneous dispersions, Communications in Statistics - Theory and Methods, Submitted.
  12. Van den Broek, J. (1995). A score test for zero inflation in a Poisson distribution, Biometrics, 51, 738-743. https://doi.org/10.2307/2532959
  13. Walhin, J. F. (2001). Bivariate ZIP models, Biometrical Journal, 43, 147-160. https://doi.org/10.1002/1521-4036(200105)43:2<147::AID-BIMJ147>3.0.CO;2-5
  14. Wang, K., Lee, A. H., Yau, K. K. W. and Carivick, P. J. W. (2003). A bivariate zero-inflated Poisson regression model to analyze occupational injuries, Accident Analysis and Prevention, 35, 625-629. https://doi.org/10.1016/S0001-4575(02)00036-2
  15. Wang, P. (2003). A bivariate zero-inflated negative binomial regression model for count data with excess zeros, Economics Letters, 78, 373-378. https://doi.org/10.1016/S0165-1765(02)00262-8