A Self-management Approach for Dietary Sodium Restriction in Patients With CKD:A Randomized Controlled Trial

Health and self-regulatio

A comparison between Rosenblatt's estimator and parametric density estimators for determining test limits

Because of measurement errors, test limits instead of specification limits are used for inspection to realize a prescribed bound on the consumer loss. Test limits based on the assumption of normality lead to severe violation of the prescribed bound when normality fails. While relaxing the assumption of normality, it is important to estimate the density of the inspected characteristic at the specification limit correctly. It is investigated whether larger parametric models provide a useful improvement. Simulations are carried out for several such models. It turns out that for estimating a density at a fixed point, the parametric estimators give improvements compared to application of the normal density. However, for small or moderate sample sizes Rosenblatt’s estimator is, in general, more accurate than the parametric density estimators

Accurate test limits under nonnormal measurement error

When screening a production process for nonconforming items the objective is to improve the average outgoing quality level. Due to measurement errors specification limits cannot be checked directly and hence test limits are required, which meet some given requirement, here given by a prescribed bound on the consumer loss. Classical test limits are based on normality, both for the product characteristic and for the measurement error. In practice, often nonnormality occurs for the product characteristic as well as for the measurement error. Recently, nonnormality of the product characteristic has been investigated. In this paper attention is focussed on the measurement error. Firstly, it is shown that nonnormality can lead to serious failure of the test limit. New test limits are therefore derived, which have the desired robustness property: a small loss under normality and a large gain in case of nonnormality when compared to the normal test limit. Monte Carlo results illustrate that the asymptotic theory is in agreement with moderate sample behaviour