Bander Al-Zahrani

Additive and subtractive scrambling in optional randomized response modeling.

This article considers unbiased estimation of mean, variance and sensitivity level of a sensitive variable via scrambled response modeling. In particular, we focus on estimation of the mean. The idea of using additive and subtractive scrambling has been suggested under a recent scrambled response model. Whether it is estimation of mean, variance or sensitivity level, the proposed scheme of estimation is shown relatively more efficient than that recent model. As far as the estimation of mean is concerned, the proposed estimators perform relatively better than the estimators based on recent additive scrambling models. Relative efficiency comparisons are also made in order to highlight the performance of proposed estimators under suggested scrambling technique.

On Parameters Estimation of Lomax Distribution under General Progressive Censoring

We consider the estimation problem of the probability for Lomax distribution based on general progressive censored data. The maximum likelihood estimator and Bayes estimators are obtained using the symmetric and asymmetric balanced loss functions. The Markov chain Monte Carlo (MCMC) methods are used to accomplish some complex calculations. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation study.

Statistical analysis of the Lomax–Logarithmic distribution

In this paper we introduce a three-parameter lifetime distribution following the Marshall and Olkin [New method for adding a parameter to a family of distributions with application to the exponential and Weibull families. Biometrika. 1997;84(3):641–652] approach. The proposed distribution is a compound of the Lomax and Logarithmic distributions (LLD). We provide a comprehensive study of the mathematical properties of the LLD. In particular, the density function, the shape of the hazard rate function, a general expansion for moments, the density of the rth order statistics, and the mean and median deviations of the LLD are derived and studied in detail. The maximum likelihood estimators of the three unknown parameters of LLD are obtained. The asymptotic confidence intervals for the parameters are also obtained based on asymptotic variance–covariance matrix. Finally, a real data set is analysed to show the potential of the new proposed distribution.

Mean and sensitivity estimation of a sensitive variable through additive scrambling

Abstract This article focuses on reducing the additional variance due to randomization of the responses. The idea of additive scrambling and its inverse has been used along with (i) split sample approach and (ii) double response approach. Specifically, our proposal is based on Gupta et al. (2006) randomized response model. We selected this model for improvement because it provides estimator of mean and sensitivity level of a sensitive variable and is better than all of its competitors proposed earlier to it and even Gupta et al. (2006) sensitivity estimator is better than that of Gupta et al. (2010). Our suggested estimators are unbiased estimators and perform better than Gupta et al. (2006) estimator. The issue of privacy protection is also discussed.

A 3-Component Mixture of Rayleigh Distributions: Properties and Estimation in Bayesian Framework

To study lifetimes of certain engineering processes, a lifetime model which can accommodate the nature of such processes is desired. The mixture models of underlying lifetime distributions are intuitively more appropriate and appealing to model the heterogeneous nature of process as compared to simple models. This paper is about studying a 3-component mixture of the Rayleigh distributionsin Bayesian perspective. The censored sampling environment is considered due to its popularity in reliability theory and survival analysis. The expressions for the Bayes estimators and their posterior risks are derived under different scenarios. In case the case that no or little prior information is available, elicitation of hyperparameters is given. To examine, numerically, the performance of the Bayes estimators using non-informative and informative priors under different loss functions, we have simulated their statistical properties for different sample sizes and test termination times. In addition, to highlight the practical significance, an illustrative example based on a real-life engineering data is also given.

Recursive computation of the single and product moments of order statistics from the complementary exponential–geometric distribution

The complementary exponential–geometric distribution has been proposed recently as a simple and useful reliability model for analysing lifetime data. For this distribution, some recurrence relations are established for the single and product moments of order statistics. These recurrence relations enable the computation of the means, variances and covariances of all order statistics for all sample sizes in a simple and efficient recursive manner. By using these relations, we have tabulated the means, variances and covariances of order statistics from samples of sizes up to 10 for various values of the shape parameter θ. These values are in turn used to determine the best linear unbiased estimator of the scale parameter β based on complete and Type-II right-censored samples.

The (P-A-L) Extended Weibull Distribution: A New Generalization of the Weibull Distribution

Recently, some attempts have been made to construct new families of models to extend well-known distributions and at the same time provide great flexibility in modeling data in practice. So, several classes by adding shape parameters to generate new models have been explored in the statistical literature. We propose a new generalization of the threeparameter extended Weibull distribution pioneered by Pappas et al. (2012) by using the generator by Marshall and Olkin (1997). The new model is called the (P-A-L) extended Weibull, where (P-A-L) denote the first letters of the scientists Pappas, Adamidis and Loukas.

Parameter estimation of a two-parameter Lindley distribution under hybrid censoring

The paper deals with the classical and Bayesian estimation of a two-parameter weighted Lindley distribution based on hybrid censoring. The maximum likelihood estimators with its standard errors are obtained. Bayes estimates of the parameters along with standard errors and credible intervals are also obtained. The Markov chain Monte Carlo methods are used to compute the Bayes estimates. Monte Carlo simulation study is used to compare the performance of the maximum likelihood and Bayesian estimators. A real data set is given for illustration purpose.

On Using Negative Binomial Distribution as a Randomization Device in Sensitive Surveys

In this article, an interesting improvement of some recent randomized response techniques has been proposed. The proposed randomized response technique applies Negative Binomial distribution to obtain data from respondents. An unbiased estimator of proportion of a sensitive attribute has been suggested and it is shown, numerically, that the new estimator performs better than the recent estimators while doing a sensitive survey. It is also established that the proposed estimator is unconditionally better than that of the estimator based on using the geometric distribution.

Cross-layer QoE prediction for mobile video based on random neural networks

Based on random neural networks, a cross-layer prediction model is proposed for estimating the perceptual quality of mobile video in no reference mode. The model exploits key parameters affecting video quality. Simulation results show considerable predictability performance with R-squared correlation of 0.90 and 0.39 root mean squared error.