AN INTRODUCTION TO INNOVATIVE SURVIVAL DISTRIBUTIONS BY ANALYZING THEIR UTILITY WITH TWO REAL-WORLD DATASET APPLICATIONS
SIRAJUDDEEN. P. K
STATISTICS
April 2024
Within the constraints of this study, we offer a Modi family of continuous probability distributions that is exhaustive in its scope. The persons who are afflicted with sickness and the lengths of time they are able to survive are the targets of this family's application. The application focuses on the folks who are able to survive. A density function that has three parameters and a hazard rate function that has an inverted J-shape are two of the characteristics of the distribution that have been described in depth with regard to the distribution. Each of these density functions will be discussed in more detail below. Through the utilization of its mathematical and statistical capabilities, which we applied in order to carry out the inquiry, we were able to analyze the attributes of the distribution that was recommended. Additionally, the probability density function of order statistics may be derived for this distribution. This is something that can be done. Certainly, this is something that is attainable. The maximum likelihood estimate is the approach that we employ in order to carry out the conventional way of estimating parameters. This method is known as the maximum likelihood estimate. It is possible for us to do things in this way. For the purpose of proving that our distribution provides a better match than other well-known distributions, we applied it to two real datasets and demonstrated that it delivered. It is via the utilization of this distribution that this shall be proved.
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