Browsing by Author "Singh, Sanjay Kumar"

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    Bayesian inference: Weibull poisson model for censored data using the expectation–maximization algorithm and its application to bladder cancer data
    (Journal of Applied Statistics, 2022) Anurag, Pathak; Kumar, Manoj; Singh, Sanjay Kumar; Singh, Umesh
    This article focuses on the parameter estimation of experimental items/units from Weibull Poisson Model under progressive type- II censoring with binomial removals (PT-II CBRs). The expectation– maximization algorithm has been used for maximum likelihood estimators (MLEs). The MLEs and Bayes estimators have been obtained under symmetric and asymmetric loss functions. Performance of competitive estimators have been studied through their simulated risks. One sample Bayes prediction and expected experiment time have also been studied. Furthermore, through real bladder cancer data set, suitability of considered model and proposed methodology have been illustrated.
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    Statistical inferences based on exponentiated exponential model to assess novel corona virus (COVID-19) kerala patient data
    (Annals of Data Science, 2022) Pathak, Anurag; Kumar, Manoj; Singh, Sanjay Kumar; Singh, Umesh
    In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the model has been followed by different statistical tools like the value of logarithm of likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian information criterion. Moreover, likelihood ratio test and empirical posterior probability analysis are performed to show its suitability. The maximumlikelihood and asymptotic confidence intervals for the parameters are derived from Fisher information matrix. We use the Markov Chain Monte Carlo technique to generate samples from the posterior density function. Based on generated samples, we can compute the Bayes estimates of the unknown parameters and can also construct highest posterior density credible intervals. Further we discuss the Bayesian prediction for future observation based on the observed sample. The Gibbs sampling technique has been used for estimating the posterior predictive density and also for constructing predictive intervals of the order statistics from the future sample.