Search

Model misspecification can be a serious issue in any lifetime data analysis. Assumption of the correct model is very important particularly in prediction for future observations and for estimating the tail probabilities of any lifetime distribution. In this paper we have considered the model misspecification of the log-normal and Birnbaum-Saunders distributions. These two distributions have striking similarities both in terms of the probability density functions and hazard functions, in certain range of parameter values, which makes it extremely difficult to detect the correct model. Hence, to identify the correct model, first the conventional method of ratio of maximized likelihood approach has been tried. The necessary theoretical results have been derived. Based on extensive simulations it has been observed that for certain range of parameter values the model misspecification can be quite high even for very large sample sizes. Some of the other methods like the ratio of maximized product of spacings and minimized Kolmogorov-Smirnov distance, have also been explored. But none of the method performs uniformly better than the other over the entire range of the parameter space. Some counter intuitive results have also been obtained. Hence, we finally conclude that it is extremely difficult to choose the correct model in case of log-normal and Birnbaum-Saunders distribution for certain range of parameter values. Finally, we have suggested a modified ratio of maximized likelihood approach, and the performances are quite satisfactory.AMS Subject Classifications: 62F10, 62F03, 62H12.

This paper aims to propose a suitable statistical model for the age distribution of prostate cancer detection. Descriptive studies suggest the onset of prostate cancer after 37 years of age with maximum diagnosis age at around 70 years. The major deficiency of descriptive studies is that the results cannot be generalized for all types of populations usually having non-identical environmental conditions. The proposition follows by checking the suitability of the model through different statistical tools like Akaike Information Criterion, Kolmogorov- Smirnov distance, Bayesian Information Criterion and _χ2 statistic. The Maximum likelihood estimate of the parameters of the proposed model along with their asymptotic confidence intervals have been obtained for the considered real data set.

Nanofibrous microspheres (NFM) are emerging as prominent next-generation biomimetic injectable scaffold system for stem cell delivery and different tissue regeneration where nanofibrous topography facilitates ECM-like stem cells niches. Addition of osteogenic bioactive nanosilicate platelets within NFM can provide osteoconductive cues to facilitate matrix mediated osteogenic differentiation of stem cells and enhance the efficiency of bone tissue regeneration. In this study, gelatin nanofibrous microspheres are prepared containing fluoride-doped laponite XL21 (LP) using the emulsion mediated thermal induce phase separation (TIPS) technique. Systematic studies are performed to understand the effect of physicochemical properties of biomimicking NFM alone and with different concentrations of LP on human dental follicle stem cells (hDFSCs), their cellular attachment, proliferation, and osteogenic differentiation. The study highlights the effect of LP nanosilicate with biomimicking nanofibrous injectable scaffold system aiding in enhancing stem cell differentiation under normal physiological conditions compared to NFM without LP. The laponite-NFM shows suitability as excellent injectable biomaterials system for stem cell attachment, proliferation and osteogenic differentiation for stem cell transplantation and bone tissue regeneration.

This article presents different estimation procedure for inverse Lindley distribution for Type-I hybrid censored data. We have obtained the parameter estimate under both the classical and Bayesian paradigm. In the classical set up, method of Maximum Likelihood(ML) and Maximum Product of spacings (MPS) estimates are obtained along with 95% asymptotic confidence interval. Bayesian estimation is implemented under the assumption of squared error loss function. An alternative Bayesian procedure is also proposed by incorporating the sample information through the spacings function instead of likelihood function. The Bayes estimates are computed using Markov Chain Monte Carlo (MCMC) technique due to their implicit nature. Highest posterior density (HPD) intervals based on these MCMC samples are evaluated and compared in terms of simulated risks. Further, a real data of 72 guinea pigs, infected with tuberculosis is analysed to justify the suitability of the afore-said estimation techniques under the specified censoring scheme.

This article is devoted to the development of product of spacings estimator for a Progressive hybrid Type-I censoring scheme with binomial removals. The experimental units are assumed to follow inv...

Studies have examined the differences in sociodemographic/clinical characteristics between patients on long-acting injectable (LAI) versus oral medications. However, most studies did not focus specifically on patients for whom LAIs would clearly be indicated. We performed a chart review of patients with schizophrenia or schizoaffective disorder. Patients were categorized as having an ‘indication for an LAI’ or not on the basis of their adherence history. Patients for whom an LAI was indicated and prescribed on discharge were then compared with similar patients for whom an LAI was not prescribed. Of 305 charts reviewed, consisting of 279 unique patients, 27.2% were judged to have an indication for an LAI (n = 76), but only 32.9% of these (n = 25) were discharged on an LAI. In the multiregression model, being African American, residing in a psychiatric residence, having a previous history of an LAI trial, and being treated with a higher antipsychotic dose were predictive of LAI prescription. It is important to focus on the population who are not likely to receive an LAI, but who have such indications for treatment.

In life testing experiments, Type-I censoring scheme has been widely used due to its simplicity and poise with considerable gain in the completion time of an experiment. This article deals with the parameter estimation of inverse Lindley distribution when the data is Type-I censored. Estimates have been obtained under both the classical and Bayesian paradigm. In the classical scenario, estimates based on maximum likelihood and maximum product of spacings coupled with their 95% asymptotic confidence interval have been obtained. Under the Bayesian set up, the point estimate is obtained by considering squared error loss function using Markov Chain Monte Carlo technique and highest posterior density intervals based on these samples are reckoned. The performance of above mentioned techniques are evaluated on the basis of their simulated risks. Further, a real data set is analysed for appraisal of aforementioned estimation techniques under the specified censoring scheme.

Vi polysaccharide typhoid vaccines cannot be used in children