Statistical inference and data analysis of the record-based transmuted Burr X model

Probability distribution has proven its usefulness in almost every discipline of human endeavors. A novel extension of Bur X distribution is developed in this study employing the record-based transmuted mapping technique, which can be used to fit skewed and complex data. We referred to this novel distribution as a record-based transmuted Burr X model. We established the shape of the probability density function and hazard function. Numerous statistical and mathematical properties are provided, including quantile function, moment, and ordered statistics of the proposed model. Further, we obtain the estimation of the model parameters using the maximum likelihood estimation method, and four sets of Monte Carlo simulation studies are carried out to evaluate the efficiency of these estimates. Finally, the practical applicability of the developed model is demonstrated by analyzing three data sets, comparing its performance with several well-known distributions. The results highlight the flexibility and accuracy of the model, establishing it as a powerful and reliable tool for advanced statistical modeling in environmental and survival research.