Spiridon Penev

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Usually, methods for thresholding wavelet estimators are implemented term by term, with empirical coecients included or excluded depending on whether their absolute values exceed a level that re¯ects plausible moderate deviations of the noise. We argue that performance may be improved by pooling coecients into groups and thresholding them together. This(More)
We derive representations of higher order dual measures of risk in L p spaces as suprema of integrals of Average Values at Risk with respect to probability measures on (0, 1] (Kusuoka representations). The suprema are taken over convex sets of probability measures. The sets are described by constraints on the dual norms of certain transformations of(More)
In covariance structure modelling, the non-centrality parameter of the asymptotic chi-squared distribution is typically used as an indicator of asymptotic power for hypothesis tests. When a latent linear regression is of interest, the contribution to power by the maximal reliability coefficient, which is associated with used latent variable indicators, is(More)
A procedure for point and interval estimation of maximal reliability of multiple-component measuring instruments in multi-level settings is outlined. The approach is applicable to hierarchical designs in which individuals are nested within higher-order units and exhibit possibly related performance on components of a given homogeneous scale. The method is(More)
A covariance structure modelling method for the estimation of reliability for composites of congeneric measures in test-retest designs is outlined. The approach also allows an approximate standard error and confidence interval for scale reliability in such settings to be obtained. The procedure further permits measurement error components due to possible(More)
This paper is concerned with the reliability of weighted combinations of a given set of dichotomous measures. Maximal reliability for such measures has been discussed in the past, but the pertinent estimator exhibits a considerable bias and mean squared error for moderate sample sizes. We examine this bias, propose a procedure for bias correction, and(More)