A flexible count data regression model for risk analysis

Risk Analysis

Volumn 28, Issue 1, 2008, Pages 213-223

  Guikema, Seth D ^a,b,c   Goffelt, Jeremy P ^d  

^a The Johns Hopkins University   (United States)

^b Blinn College   (United States)

^c JOHNS HOPKINS UNIVERSITY   (United States)

^d NONE

Author keywords

Conway Maxwell Poisson distribution; Count data; Generalized linear model

Indexed keywords

DISEASES; MATHEMATICAL MODELS; REGRESSION ANALYSIS; RELIABILITY ANALYSIS; RISK ANALYSIS;

CONWAY-MAXWELL POISSON DISTRIBUTION; COUNT DATA; GENERALIZED LINEAR MODELS; REGRESSION MODELS;

DATA RECORDING;

ELECTRICAL POWER; ESTIMATION METHOD; HARDWARE; MODELING; POISSON RATIO; REGRESSION ANALYSIS; RISK ASSESSMENT;

ARTICLE; DATA ANALYSIS; POISSON DISTRIBUTION; POWER SUPPLY; REGRESSION ANALYSIS; RISK ASSESSMENT; STATISTICAL MODEL;

COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; LIKELIHOOD FUNCTIONS; LINEAR MODELS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; POISSON DISTRIBUTION; PROBABILITY; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; STATISTICS AS TOPIC;

EID: 39849095134     PISSN: 02724332     EISSN: 15396924     Source Type: Journal    
DOI: 10.1111/j.1539-6924.2008.01014.x     Document Type: Article

References (25)

1. Agresti, A. (2002). Categorical Data Analysis, 2nd ed. Hoboken, NJ : Wiley-Interscience.
2. Andreou, S. A., Marks, D. H., & Clark, R. M. (1987a). A new methodology for modeling break failure patterns in deteriorating water distribution systems: Theory. Advances in Water Resources, 10, 2 10.
3. Andreou, S. A., Marks, D. H., & Clark, R. M. (1987b). A new methodology for modeling break failure patterns in deteriorating water distribution systems: Applications. Advances in Water Resources, 10, 11 20.
4. Cameron, A. C., & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambride, UK : Cambridge University Press.
5. Conway, R.W., & Maxwell, W. L. (1962). A queuing model with state dependent service rates. Journal of Industrial Engineering, 12, 132 136.
6. Englin, J., & Shonkwiler, J. S. (1995). Estimating social welfare using count data models: An application to long-run recreation demand under conditions of endogenous stratification and truncation. Review of Economics and Statistics, 77 (1 104 112.
7. Guikema, S. D. (2007). Formulating informative, data-based priors for failure probability estimation in reliability analysis. Reliability Engineering & System Safety, 92 (4 490 502.
8. Guikema, S. D., Davidson, R. A., & Liu, H. (2006). Statistical models of the effects of tree trimming on power system outages. IEEE Transactions on Power Delivery, 21 (3 1549 1557.
9. Guikema, S. D., & Paté-Cornell, M. E. (2004). Bayesian analysis for launch vehicle reliability. Journal of Spacecraft and Rockets, 41 (1 93 102.
10. Guikema, S. D., & Paté-Cornell, M. E. (2005). Probability of infancy problems for space launch vehicles. Reliability Engineering and System Safety, 87 (3 303 314.
11. Han S., Guikema, S. D., Quiring, S. M., Lee, K., Rowowsky, D., & Davidson, R. A. Estimating the spatial distribution of power outages during hurricanes in the gulf coast region. Reliability Engineering & System Safety, submitted.
12. Hausman, J., Hall, B. H., & Griliches, Z. (1984). Econometric models for count data with an application to the patents-R&D relationship. Econometrica, 52 (4 909 938.
13. Kadane, J. B., Shmueli, G., Minka, T. P., Borle, S., & Boatwright, P. (2006). Conjugate analysis of the Conway-Maxwell-Poisson distribution. Bayesian Analysis, 1, 363 374.
14. Lindsey, J. K., & Jones, B. (1998). Choosing among generalized linear models applied to medical data. Statistics in Medicine, 17 (1 59 68.
15. Link, W. A., & Sauer, J. R. (1998). Estimating population change from count data: Application to the North American Breeding Bird Survey. Ecological Applications, 8 (2 258 268.
16. Liu, H., Davidson, R. A., Rosowsky, D. V., & Stedinger, J. R. (2005). Negative binomial regression of electric power outages in hurricanes. ASCE Journal of Infrastructure Systems, 11 (4 258 267.
17. Lord, D., Washington, S. P., & Ivan, J. N. (2005). Poisson, Poisson-gamma, and zero-inflated regression models of motor vehicle crashes: Balancing statistical fit and theory. Accident Analysis and Prevention, 37 (1 35 46.
18. Lunn, D. (2003). WinBUGS development interface (WBDev). ISBA Bulletin, 10 (3). Available at http://www.mrc-bsu.cam.ac.uk/bugs/latest/isbalunn.html, accessed on January 30, 2007.
19. Minka, T. P., Shmueli, G., Kadane, J. B., Borle, S., & Boatwright, P. (2003). Computing with the COM-Poisson Distribution. Technical Report, 775, Department of Statistics, Carnegie Mellon University, Pittsburgh. Available at http://www.stat.cmu.edu/tr/.
20. Nedler, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society, Part A, 135, Part 3, 370 384.
21. Shmueli, G., Minka, T. P., Kadane, J. B., Borle, S., & Boatwright, P. (2005). A useful distribution for fitting discrete data: Revival of the Conway-Maxwell-Poisson distribution. Journal of the Royal Statistical Society, Part C, 54, 127 142.
22. Thall, P. F. (1988). Mixed Poisson likelihood regression models for longitudinal interval count data. Biometrics, 44 (1 197 209.
23. White, G. C., & Bennetts, R. E. (1996). Analysis of frequency count data using the negative binomial distribution. Ecology, 77 (8 2549 2557.
24. Winkelmann, R. (2003). Econometric Analysis of Count Data. New York : Springer-Verlag.
25. Xu, N., Guikema, S. D., Davidson, R. A., Nozick, L. K., Çaǧnan, Z., & Vaziri, K. (2007). Optimizing scheduling of post-earthquake electric power restoration tasks. Earthquake Engineering and Structural Dynamics, 36 (12 265 284.