Image denoising is an important pre-processing step for accurately quantifying functional morphology and measuring activities of the tissues using PET images. Unlike structural imaging modalities, PET images have two difficulties: (1) the Gaussian noise model does not necessarily fit into PET imaging because the exact nature of noise propagation in PET imaging is not well known, and (2) PET ima...

We use household survey data to construct a direct measure of absolute risk aversion based on the maximum price a consumer is willing to pay to enter a lottery. We relate this measure to consumers’ endowment and attributes and to measures of background risk. We ...nd that risk aversion is a decreasing function of endowment thus rejecting CARA preferences but that the elasticity to consumption i...

JNCI Vol. 103, Issue 13 | July 6, 2011 In this issue of the Journal, Petracci et al. (1) present a model to estimate the degree of benefit in terms of breast cancer risk reduction that may be achieved with risk factor modification. By how much would breast cancer risk change over a 10-year period for a 45-yearold Italian woman making changes in modifiable risk factors such as exercise, weight, ...

The estimation of risk measured in terms of a risk measure is typically done in two steps: in the first step, the distribution is estimated by statistical methods, either parametric or nonparametric. In the second step, the estimated distribution is considered as true distribution and the targeted risk-measure is computed. In the parametric case this is achieved by using the formula for the ris...

We adapt the interactive spline model of Wahba to growth curves with covariates. The smoothing spline formulation permits a nonparametric representation of the growth curves. In the limit when the discretization error is small relative to the estimation error, the resulting growth curve estimates often depend only weakly on the number and locations of the knots. The smoothness parameter is dete...

X1, . . . , Xn iid ∼ N (θ, σ), with σ known. Our goal is to estimate θ under squared-error loss. For our first guess, pick the natural estimator X. Note that it has constant risk σ 2 n , which suggests minimaxity because we know that Bayes estimators with constant risk are also minimax estimators. However, X is not Bayes for any prior, because under squared-error loss unbiased estimators are Ba...

We handle two major issues in applying extreme value analysis to financial time series, bias and serial dependence, jointly. This is achieved by studying bias correction method when observations exhibit weakly serial dependence, namely the β−mixing series. For estimating the extreme value index, we propose an asymptotically unbiased estimator and prove its asymptotic normality under the β−mixin...

In several European countries, drivers under influence (DUI), suspected of an alcohol use disorder (AUD, 'alcoholism') are referred for diagnostic examination. The accuracy of diagnostic procedures used in diagnosing AUD in the DUI population is unknown. The aim of this study was to compare three prevalence estimates of AUD based on a structured clinical interview (SCID), a restrictive diagnost...

We consider a sequence space model of statistical linear inverse problems where we need to estimate a function f from indirect noisy observations. Let a nite set of linear estimators be given. Our aim is to mimic the estimator in that has the smallest risk on the true f. Under general conditions, we show that this can be achieved by simple minimization of unbiased risk estimator, provided the s...