One-sided tolerance limits for unbalanced one-way random effects models: a generalized Mee and Owen procedure

This article proposes a new procedure for obtaining one-sided tolerance limits in unbalanced random effects models. The procedure is a generalization of that proposed by Mee and Owen for the balanced situation, and can be easily implemented, because it only needs a non-central-t table. Two simulation studies are carried out to assess the performance of the new procedure and to compare it with one of the other procedures laid out in previous statistical literature. The article findings show that the new procedure is much simpler to compute and performs better than the previous ones, having inferior values of the gamma bias in a wide range of situations, representative of many actual industrial applications, and behaving also reasonably well in more extreme sampling situations. The use of the new limits is illustrated by an application to an actual example from the steel industry.     

Robustness of residual-based bootstrap to the composition of serially correlated errors

In a simple autoregressive model with serially correlated errors, we evaluate size distortions resulting from the residual bootstrap when the Wold innovation is serially dependent and hence is expected to contaminate the inference. The small distortions caused by the presence of strong conditional heteroskedasticity or other nonlinearities can be partly removed further by using the wild bootstrap.     

Shrinkage estimation in replicated median ranked set sampling

In this article, we consider the median ranked set sampling estimation and test of hypothesis for the mean for symmetric distributions. We suggest some alternative estimation strategies for parameters based on shrinkage and pretest principles. It is advantageous to use the non-sample information in the estimation process to construct alternative estimations for the parameter of interest. In this article, large sample properties of the suggested estimators will be assessed numerically using computer simulation. The relative performance of the suggested estimators for moderate and large samples will also be simulated. For illustration purposes, the proposed methodology is applied using data collocated from the Pepsi Cola production company in Al-Khobar, Saudi Arabia.     

A rule of thumb for testing symmetry about an unknown median against a long right tail

A rule of thumb for testing symmetry of an unknown univariate continuous distribution against the alternative of a long right tail is proposed. Our proposed test is based on the concept of exceedance statistic and is ad hoc in nature. Exact performances of the proposed rule are investigated in detail. Some results from an asymptotic point of view are also provided. We compare our proposed test with several classical tests which are practically applicable and are known to be exact or nearly distribution free. We see that the proposed rule is better than most of the existing tests for symmetry and can be applied with ease. An illustration with real data is provided.     

Bootstrapping probability-proportional-to-size samples via calibrated empirical population

A collection of six novel bootstrap algorithms, applied to probability-proportional-to-size samples, is explored for variance estimation, confidence interval and p-value production. Developed according to bootstrap fundamentals such as the mimicking principle and the plug-in rule, these algorithms make use of an empirical bootstrap population informed by sampled units each with assigned weight. Starting from the natural choice of Horvitz–Thompson (HT)-type weights, improvements based on calibration to known population features are fostered. Focusing on the population total as the parameter to be estimated and on the distribution of the HT estimator as the target of bootstrap estimation, simulation results are presented with the twofold objective of checking practical implementation and of investigating the statistical properties of the bootstrap estimates supplied by the algorithms explored.     

Bayesian inference and prediction analysis of the power law process based on a natural conjugate prior

Under a natural conjugate prior with four hyperparameters, the importance sampling (IS) technique is applied to the Bayesian analysis of the power law process (PLP). Samples of the parameters of the PLP are obtained from IS. Based on these samples, not only the posterior analysis of parameters and some parameter functions in the PLP are performed conveniently, but also single-sample and two-sample prediction procedures are constructed easily. Furthermore, the sensitivity of the posterior mean of the parameter functions in the PLP is studied with respect to the hyperparameters of the natural conjugate prior and it can guide the selections of the hyperparameters directly. Coupled this sensitivity with the relations between the prior moments and the hyperparameters in the natural conjugate prior, it is possible to give directions about the selections of the prior moments to a certain degree. After some numerical experiments illustrate the rationality and feasibility of the proposed methods, an engineering example demonstrates its application.     

Ranked set sampling with random subsamples

A ranked sampling procedure with random subsamples is proposed to estimate the population mean. Four methods of obtaining random subsamples are described. Several estimators of the mean of the population based on random subsamples in ranked set sampling are proposed. These estimators are compared with the mean of a simple random sample for estimating the mean of symmetric and skew distributions. Extensive simulation under several subsampling distributions, sample sizes, and symmetric and skew distributions shows that the estimators of the mean based on random subsamples are more accurate than existing methods.     

On estimating parameters of a progressively censored lognormal distribution

We consider the problem of making statistical inference on unknown parameters of a lognormal distribution under the assumption that samples are progressively censored. The maximum likelihood estimates (MLEs) are obtained by using the expectation-maximization algorithm. The observed and expected Fisher information matrices are provided as well. Approximate MLEs of unknown parameters are also obtained. Bayes and generalized estimates are derived under squared error loss function. We compute these estimates using Lindley's method as well as importance sampling method. Highest posterior density interval and asymptotic interval estimates are constructed for unknown parameters. A simulation study is conducted to compare proposed estimates. Further, a data set is analysed for illustrative purposes. Finally, optimal progressive censoring plans are discussed under different optimality criteria and results are presented.     

A window-limited generalized likelihood ratio test for monitoring Poisson processes with linear drifts

There is gradually increasing attention devoted to the monitoring of Poisson process due to its wide applications in industry quality control and health-care surveillance. However, most of the study focuses on the case with step shifts in Poisson means. Relatively little attention has been paid to the case with linear drifts in Poisson means. This paper extends the window-limited generalized likelihood ratio (WGLR) test from the monitoring of normal means to Poisson processes, with focus on linear drifts. The comparison results with the adaptive cumulative sum (ACUSUM) charts and the weighted CUSUM (WCUSUM) charts show that the WGLR chart generally provides better detection performance than the other alternative methods in both the zero-state and steady-state cases.     

A control chart using an auxiliary variable and repetitive sampling for monitoring process mean

In this paper, a new control chart is proposed by using an auxiliary variable and repetitive sampling in order to enhance the performance of detecting a shift in process mean. The product-difference type estimator of the mean is plotted on the proposed control chart, which utilizes the information of an auxiliary variable correlated with the main quality variable. The proposed control chart is based on the outer and inner control limits so that repetitive sampling is allowed when the plotted statistic falls between the two limits. The average run length (ARL) of the proposed control chart is evaluated using the Monte Carlo simulation. The proposed control chart is compared with the Riaz M control chart and the results show the outperformance of the proposed control chart in terms of the ARL.