Kernel-based estimation of P(X >Y) in ranked set sampling

This article is directed at the problem of reliability estimation using ranked set sampling. A nonparametric estimator based on kernel density estimation is developed. The estimator is shown to be superior to its analog in simple random sampling. Monte Carlo simulations are employed to assess performance of the proposed estimator. Two real data sets are analysed for illustration

Variance Estimation in Ranked Set Sampling Using a Concomitant Variable

We propose a nonparametric variance estimator when ranked set sampling (RSS) and judgment post stratification (JPS) are applied by measuring a concomitant variable. Our proposed estimator is obtained by conditioning on observed concomitant values and using nonparametric kernel regression

Some nonparametric tests of perfect judgment ranking for judgment post stratification

We develop some nonparametric tests of perfect judgment ranking for judgment post stratification sampling scheme. We show that the best proposed test beats the best existing nonparametric test of perfect judgment ranking in ranked set sampling applied to the judgment post stratification case by conditioning on the observed stratum sizes

Goodness of fit tests for Rayleigh distribution based on Phi-divergence

In this paper, we develop some goodness of fit tests for Rayleigh distribution based on Phi-divergence. Using Monte Carlo simulation, we compare the power of the proposed tests with some traditional goodness of fit tests including Kolmogorov-Smirnov, Anderson-Darling and Cramer von-Mises tests. The results indicate that the proposed tests perform well as compared with their competing tests in the literature. Finally, the proposed procedures are illustrated via a real data set.En este artículo desarrollamos pruebas de bondad de ajuste para distribución Rayleigh basados en divergencia Phi. Usando simulaciones de Monte Carlo, comparamos el poder de las pruebas propuestas con algunas pruebas tradicionales incluyendo Kolmogorov-Smirnov, Anderson-Darling y Cramer von-Mises. Los resultados indican que la prueba propuesta funciona mejor que las otras pruebas reportadas en literatura. Finalmente, los procedimientos nuevos son ilustrados sobre dos conjuntos de datos reales

Goodness of t tests for logistic distribution based on Phi-divergence

Some goodness of fit tests for logistic distribution based on Phi-divergenceare developed. The powers of the introduced tests are compared with sometraditional goodness of t tests including Kolmogorov-Smirnov, Anderson-Darling and Cramer-von Mises tests for logistic distribution using MonteCarlo simulation. It is shown the proposed tests have good performance ascompared with their competitors in the literature. A real data set is used forillustration