BACKGROUND The nested case-control design can be a very efficient approach to an epidemiological investigation. In order to obtain unbiased estimates of relative risk, controls should be selected by incidence density sampling, which involves matching each case to a sample of those who are at risk at the time of case occurrence. METHODS This paper presents a simple computer program for inciden...

In certain scenarios, the odds ratio (OR) provides an unbiased estimate of the rate ratio in case–control studies (1). However, the OR is also frequently used to estimate the risk ratio (relative risk) (RR) of an outcome in the presence of a risk factor. The degree of error in this estimate is frequently small but can sometimes be substantial. The OR as an estimate of the RR always overestimate...

The problem of selecting the appropriate number of basis functions is a critical issue for radial basis function neural networks. An RBF network with an overly restricted basis gives poor predictions on new data, since the model has too little flexibility (yielding high bias and low variance). By contrast, an RBF network with too many basis functions also gives poor generalization performance s...

Patch-based methods have been widely used for noise reduction in recent years. In this paper, we propose a general statistical aggregation method which combines image patches denoised with several commonly-used algorithms. We show that weakly denoised versions of the input image obtained with standard methods, can serve to compute an efficient patch-based aggregated estimator. In our approach, ...

BACKGROUND frail older people often require tailored rehabilitation in order to remain at home, especially following a period of hospitalisation. Restorative care services aim to enhance an older person's ability to remain improve physical functioning, either at home or in residential care but evidence of their effectiveness is limited. OBJECTIVE to evaluate the effectiveness of a restorative...

This paper deals with the parameter choice for the nonlocal means (NLM) algorithm. After basic computations on toy models highlighting the bias of the NLM, we study the bias-variance trade-off of this filter so as to highlight the need of a local choice of the parameters. Relying on Stein’s unbiased risk estimate, we then propose an efficient algorithm to locally set these parameters, and we co...

In this paper, we propose a data-driven block thresholding procedure for waveletbased non-blind deconvolution. The approach consists in appropriately writing the problem in the wavelet domain and then selecting both the block size and threshold parameter at each resolution level by minimizing Stein’s unbiased risk estimate. The resulting algorithm is simple to implement and fast. Numerical illu...

We propose and analyze the convergence of a novel stochastic algorithm for solving monotone inclusions that are sum maximal operator monotone, Lipschitzian operator. The requires only unbiased estimations obtain rate $${\mathcal {O}}(log(n)/n)$$ in expectation strongly case, as well almost sure general case. Furthermore, context application to convex–concave saddle point problems, we derive pri...