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We suggest a new class of difference-type estimators under two-phase stratified sampling when using information from two auxiliary variables including their distribution functions. The bias and MSE...

Sousa et al. and Gupta et al. presented ratio and regression estimators of population mean of a sensitive variable using auxiliary information in simple random sampling without replacement. In this...

This article studies interval estimation of the population mean in ranked set sampling. Eight types of confidence intervals are considered which are based on asymptotic theory, resampling methods, ...

In this paper, an efficient generalized estimation strategy has been proposed to present the problem of estimation of current population mean Y¯ of study variable y in two-occasion rotation samplin...

In this study we propose an improved mean estimator for a sensitive variable under simple random sampling using Optional RRT models when measurement errors are present. We discuss two scrambling op...

Motivated by some recent improvements for mean estimation in finite sampling theory, we propose, in a design-based approach, a new class of ratio-type estimators. The class is initially discussed o...

Conflicting decision signals resulting from Shewhart Mean and Variability Control Charts produce undesirable consequences. Therefore, we construct “six-sigma” control charts by alternative methods to avoid problems associated with Control Charts yielding different signals. By adjusting methods for control chart construction for dispersion charts, we produce new reference tables. The new tables provide consistent signals and appear similar and familiar to users of historical tables.

It is well known that the Shewhart control chart is relatively insensitive to detect small process average shifts. In this article, we introduce and investigate the basic features of the modified r out of m control chart. The new control chart outperforms the Shewhart control chart for small to moderate process average shifts and the corresponding r out of m control chart which has been recently appeared in the literature.

The 2 × 2 crossover is commonly used to establish average bioequivalence of two treatments. In practice, the sample size for this design is often calculated under a supposition that the true average bioavailabilities of the two treatments are almost identical. However, the average bioequivalence analysis that is subsequently carried out does not reflect this prior belief and this leads to a loss in efficiency. We propose an alternate average bioequivalence analysis that avoids this inefficiency. The validity and substantial power advantages of our proposed method are illustrated by simulations, and two numerical examples with real data are provided.

This article studies the asymptotic confidence limits for the steady-state availability, failure frequency, and mean time to failure of a repairable K-out-of-(M + S) system with M operating devices, S spares, and an imperfect service station that may be interrupted by a breakdown when it is repairing for the failed devices.

This article presents applicable bounds for distributions with monotone log-odds rate. The bounds are based on results obtained by Wang et al. (2003) and Zimmer et al. (1998). Due to the complexity of some of the equations, numerical analysis and computer algorithms were necessary to calculate some of the results of these bounds. Five different types of bounds are tabled in this research. Numerical examples of their application and comparisons to the results obtained by Barlow and Marshall (1965) of bounds on distributions with monotone hazard rate are also provided. The first type of tables is for lower bounds of increasing log-odds rate distributions with knowledge of two values of the reliability of the distribution. The second and third types of tables are for upper bounds under the same conditions. The fourth type of tables is for lower bounds of increasing log-odds rate distributions with knowledge of the mean and the median of the distribution. Finally, the fifth type of tables is for lowe...

We propose a new charting procedure, combining two or more Exponentially Weighted Moving Averages (EWMAs), which we call a Composite EWMA (CEWMA) Control Chart. In the case of two EWMAs, the CEWMA chart corresponds to the usual combined Shewhart-EWMA chart, although our plotting procedure remain, we feel, an improvement in application and interpretation. When three EWMAs are combined, the composite chart improves on the average run length performance of the combined Shewhart-EWMA chart. We also briefly discuss the combination of more than three EWMAs.

In many experimental situations, the average treatment performance within its own group is used as a benchmark to be compared with each individual treatment. Multiple comparison procedures with the average (MCA) are thus proposed. A simulation comparison study of the traditional MCA, the single-stage MCA and the two-stage MCA for normal distribution under heteroscedasticity is investigated by the Monte-Carlo techniques in this paper. It was found that the two-stage MCA has shorter confidence length than the single-stage MCA for most cases and it is also more robust for non-normal distributions. Therefore, the two-stage MCA is recommended. But when the additional samples at the second stage could be costly, the data-analysis oriented single-stage MCA can be used. A biometrical example to illustrate the single-stage MCA and the two-stage MCA with equal confidence length is also given in this article.

The MaxEWMA chart has recently been introduced as an alternative to control charting procedures that are designed to jointly detect changes in the mean and standard deviation of a normally distributed process. Here, a method for computing both in-control and out-of-control average run lengths for purposes of effectively designing this chart is offered. Design strategies are considered, numerical results to aid the design effort are given, and examples are discussed.

The average run length performances of Shewhart charts with supplementary runs rules and zone control charts are studied when the process mean changes linearly over time. Shewhart charts with all possible combinations of the typical runs rules are compared to zone control charts with identical false alarm rates. The zone control charts generally outperform the Shewhart charts in detecting a process mean that is changing linearly over time.

The purpose of this paper is to compare four methods for constructing approximate confidence intervals on the among-group variance component for the unbalanced random one-way model. Monte-Carlo simulation is used to compare the estimated coverage probabilities of these intervals. The comparison is based on a method for generating designs having specified degrees of imbalance. Two of these methods, namely, the one by Thomas and Hultquist [5] and the modified harmonic mean method, appear to be the best in most cases considered.

Consider the problem of simultaneously estimating p Gamma means when the means are believed a priori to be exchangeable. A Bayesian hierarchical model is employed to model this exchangeability assumption. Several approximation methods are discussed to compute the hierarchical Bayes estimate of the Gamma means. The accuracy of the different approaches is investigated.