Browsing by Author "Rashmi Bundel"

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Colder environments are associated with a greater cancer incidence in the female population of the United States
(SAGE Publications Ltd, 2017) Ankit Sharma; Tanu Sharma; Mahaveer S Panwar; Devesh Sharma; Rashmi Bundel; Ryan T Hamilton; James A Radosevich; Chandi C Mandal
Cancer incidence and/or mortality among individuals varies with diet, socio-culture, ethnicity, race, gender, and age. Similarly, environmental temperature modulates many biological functions. To study the effect of environment temperature on cancer incidence, the US population was selected. Because, county-wise cancer incidence rate data of various anatomical site–specific cancers and different races/ethnicities for both males and females are available. Moreover, the differences amongst the aforementioned factors among individuals are much less, as compared to the world population. Statistical analysis showed a negative correlation between the average annual temperature and cancer incidence rate at all anatomical sites and individually for 13 types (out of 16 types) of anatomical site–specific cancer incidence rates (e.g. uterine, bladder, thyroid, breast, esophagus, ovary, melanoma, non-Hodgkin lymphoma, leukemia, brain, pancreas, etc.) for females. Further analysis found a similar inverse trend in all races/ethnicities of the female population but not in all male races/ethnicities or anatomical site–specific cancers. Moreover, the majority of the counties having the top-most cancer incidence rate in females are located above the latitude 36.5°N. These findings indicate that living in a cold county in the United States might have a higher risk of cancer irrespective of cancer type (except cervical and liver) and races/ethnicities for females but not in all such cases for the male population. © 2017, © The Author(s) 2017.
Component reliability estimation through competing risk analysis of fuzzy lifetime data
(wiley, 2023) Rashmi Bundel; M.S. Panwar; Sanjeev K. Tomer
The competing risk analysis of lifetime data from multi-component series systems is often carried out to assess the reliability measures of systems' components. In many cases, systems' lifetimes are not observed precisely, or they are reported in a "vague" form. This imprecision or vagueness in data can be dealt with higher accuracy by incorporating fuzzy concepts. In the present chapter, we perform a competing risk analysis of lifetime data by considering lifetimes as fuzzy numbers. Using different membership functions, we provide procedures for maximum likelihood and Bayesian estimation of component reliability. We also evaluate bootstrap confidence intervals and the highest posterior density intervals. To observe the impact of various membership functions on the considered estimators, a comprehensive simulation study has been carried out. Finally, a real data set of small electric appliance has been analyzed. © 2023 Scrivener Publishing LLC. All rights reserved.