Non-Parametric Hypothesis Testing for Unknown Aged Class of Life Distribution Using Real Medical Data

Abstract

Over the last few decades, the statisticians and reliability analysts have looked at putting exponentiality to the test using the Laplace transform technique. The non-parametric statistical test used in this study, which is based on this technique, evaluates various treatment modalities by looking at failure behavior in the survival data that were gathered. Following use of the suggested strategy, patient survival times are recorded. In this investigation, it was presupposed that the Laplace transform order of (UBAC2) attribute or the constant failure rate would determine how the observed data behave (exponential scenario). If the survival data are exponential, the recommended treatment approach is ineffective. If the survival data are UBAC2L, the technique in use produces a better or a higher expected total present value than an older one governed by an exponential survival function (discussed in the Applications section). The efficiency and critical values of the test are calculated and compared to those of other tests in order to ensure that the suggested statistical test is applied correctly.

Statistical Modeling for Some Real Applications in Reliability Analysis Using Non-Parametric Hypothesis Testing

“…The most well-known classes proposed over the past few decades include increasing failure rate and average (IFR and IFRA), new better than used and expectation (NBU and NBUE), used is better than aged (UBA), and used better than aged in moment generating function (UBAmgf). One might consult Deshpand et al [1], Klefsjo [2], Ahmed [3,4], Bakr et al [5], Barlow and Proschan [6], Rolski [7], Abu Youssef et al [8], Etman et al [9], EL-Sagheer et al [10], Abu Youssef et al [11], Bakr et al [12], EL-Sagheer et al [13], Mahmoud et al [14], Gadallah et al [15], and Bakr and Al Babtain [16].…”

The NBRULC Reliability Class: Mathematical Theory and Goodness-of-Fit Testing with Applications to Asymmetric Censored and Uncensored Data

“…Our objective is to provide an overview of these advancements. Researchers in statistics and reliability analysis have looked at testing exponentiality problems using different age classes of life distributions from a variety of angles; for more information, see Bryson and Siddiqui [1], Bhattacharyya et al [2], Barlow and Proschan [3], Klefsjo [4], Khan et al [5], Kumazawa [6], Mahmoud et al [7,8], Majumder and Mitra [9], Bhattacharyya et al [10], Navarro [11], Abu-Youssef et al [12], Ghosh and Mitra [13], Navarro and Pellerey [14], El-Morshedy et al [15], EL-Sagheer et al [16] Gadallah et al [17], Mansour [18], Ghosh and Mitra [19], Majumder and Mitra [20], Lai and Xie [21], Etman et al [22], Ghosh and Majumder [23], Bakr and Al-Babtain [24], and Ghosh and Mitra [25] and Alqifar et al [26]. The goodness-of-fit test indicates whether the data in one's sample correspond to the data one would anticipate seeing in the actual population.…”