On modeling of lifetime data using one parameter continuous distributions

Journal of Biometrics & Biostatistics

ISSN: 2155-6180

Open Access

On modeling of lifetime data using one parameter continuous distributions

5th International Conference on Biometrics & Biostatistics

October 20-21, 2016 Houston, USA

Rama Shanker

Eritrea Institute of Technology, Eritrea

Posters & Accepted Abstracts: J Biom Biostat

Abstract :

The time to the occurrence of event of interest is known as lifetime or survival time or failure time in reliability analysis. The event may be failure of a piece of equipment, death of a person, development (or remission) of symptoms of disease, health code violation (or compliance). The modeling and statistical analysis of lifetime data are crucial for statisticians and research workers in almost all applied sciences including biomedical sciences, engineering, insurance and finance, amongst others. Two important one parameter lifetime distributions that have been popular in Statistics literature for modeling lifetime data are exponential and Lindley distributions. Shanker et al. (2015) has done extensive study on these two distributions for modeling lifetime data from medical sciences and engineering and observed that there are many cases where these two distributions are not suitable from theoretical and applied point of view. Recently the author (2015, 2016) has introduced four one parameter lifetime distributions namely Akash, Aradhana, Shanker, and Sujatha to model lifetime data. In this paper, the comparative study of Akash, Aradhana, Shanker, Sujatha, Lindley and exponential distributions have been made to model lifetime data. The relationships of these distributions, their distributional properties and estimation of parameter have been discussed. The theoretical justifications and applications of these distributions for modeling lifetime data have been discussed and explained through several examples from biomedical science and engineering.

Biography :


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