Chart

About: Chart is a research topic. Over the lifetime, 11562 publications have been published within this topic receiving 131087 citations. The topic is also known as: graph & charts.

The design of a new letter chart for measuring contrast sensitivity

Abstract: I. A consideration of methods for assessing contrast sensitivity leads to the conclusion that, for a clinical test, letters are more suitable than gratings. 2. A letter chart is described in which letters decrease in contrast but not in size. The letters are arranged in groups of three; successive groups decrease in contrast by a factor of IfJ2 from a very high contrast down to a contrast below the threshold of normal observers. A subject's threshold is taken to be the lowest contrast for which at least two letters in a group are correctly reported. 3. A mathematical model of the observer and the chart-testing procedure has been used to predict how the accuracy and repeatability of the test score depend on the parameters of the chart and observer. This reveals that even a low probability of misreporting supra threshold letters will seriously bias the test score if the passing criterion is strict, requiring correct report of all letters in each group, but will have little effect if the passing criterion is less strict. This effect of the passing criterion may explain Rubin's [Clin. Vision Sci. 2, No. I (1987)] finding that the new test, which uses a lenient criterion, has excellent test-retest reliability, much higher than the Ginsburg [Am. J. Optom. Physiol. Opt. 61,403-407 (1984)] chart with its strict criterion.

A multivariate exponentially weighted moving average control chart

Abstract: A multivariate extension of the exponentially weighted moving average (EWMA) control chart is presented, and guidelines given for designing this easy-to-implement multivariate procedure. A comparison shows that the average run length (ARL) performance of this chart is similar to that of multivariate cumulative sum (CUSUM) control charts in detecting a shift in the mean vector of a multivariate normal distribution. As with the Hotelling's χ2 and multivariate CUSUM charts, the ARL performance of the multivariate EWMA chart depends on the underlying mean vector and covariance matrix only through the value of the noncentrality parameter. Worst-case scenarios show that Hotelling's χ2 charts should always be used in conjunction with multivariate CUSUM and EWMA charts to avoid potential inertia problems. Examples are given to illustrate the use of the proposed procedure.