Student's t-statistic is a data-based linear transformation of an average. This paper proposes an extension i) to more general estimates including m.l.e.'s and M-estimates, and ii) to nonlinear transformations, so that the variance of the estimate is approximately constant. The expansion of the statistic and its properties are derived using basic procedures for the symbolic computation of asymp...

Statistical Rate Monotonic Scheduling (SRMS) is a generalization of the classical RMS results of Liu and Layland [LL73] for periodic tasks with highly variable execution times and statistical QoS requirements. The main tenet of SRMS is that the variability in task resource requirements could be smoothed through aggregation to yield guaranteed QoS. This aggregation is done over time for a given ...

OBJECTIVE To evaluate the validity of using the standardized mortality ratio (SMR) and the W statistic as risk-adjusted measures of hospital mortality to judge ICU performance. DESIGN APACHE (acute physiology and chronic health evaluation) II data were collected prospectively from the surgical ICU (SICU) at a single institution using all adult admissions (n = 6806) over an 8-year period (excl...

In our discussion of linear models relating rCBF to design factors and gCBF, we have not considered the computational problems of simultaneously fitting the model for thousands of voxels.32 Viewing the problem from a multivariate perspective provides efficient computation, and offers some insight into the problem. In this section we demonstrate the relationship between the simultaneous general ...

Several adaptive order statistic filters (OSF) are developed and compared for channel characterization and noise suppression in images and 3-D CT data. Emphasis has been put on the situation when a noise-free reference image is not available but instead we can have a sequence of two noisy versions of the same image (or a 3-D data slice). One of the noisy images is used as the reference in the O...

A. Distribution of the test statistic In the sequential test, we first compute the test statistic from a mini-batch of size m. If a decision cannot be made with this statistic, we keep increasing the mini-batch size by m datapoints until we reach a decision. This procedure is guaranteed to terminate as explained in Section 4. The parameter ✏ controls the probability of making an error in a sing...