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IJSTR >> Volume 5 - Issue 1, January 2016 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Count Data On Cancer Death In Ohio: A Bayesian Analysis

[Full Text]

 

AUTHOR(S)

Walaa Hamdi

 

KEYWORDS

Modeling, Cancer death, predictive.

 

ABSTRACT

This paper considers statistical modeling of count data on cancer death in Ohio State. We obtained count data on male and female from a website of the Centers for Disease Control and Prevention and used Bayesian analyses to find suitable models which help us to do inferences and predictions for next year. To assist us in selecting appropriate models, we use criteria such as the DIC. In this paper, we analyze the data to spatial/ longitudinal so we can capture possible correlations. Using our analyses, we make predictions of the numbers of people who will die with cancer in a future year in Ohio State.

 

REFERENCES

[1] American Cancer society. Retrieved from http://www.cancer.org/

[2] Berkhof J., Mechelen, I. E., and Gelmanm A. (2003).A Bayesian Approach to the Selection and Testing of Mixture Models. Statistica sinica 13, 4232-442.

[3] Carlin, B., and Louis, T. (2009). Bayesian Methods for Data Analysis. New York: Chapman and Hall. Book.

[4] Centers for Disease Control and Prevention. Retrieved from http://www.cdc.gov

[5] Millar, R. B. (2009).Comparison of Hierarchical Bayesian Model for Overdispersed Count data Using DIC and Bayes’ Factors. Biometrics 65, 962-969.

[6] National Cancer Institute. Retrieved from http://www.nih.gov/about-nih/what-we-do/nih-almanac/national-cancer-institute-nci