There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing parameters can be estimated minimizing criteria that approximate the average mean squared error of the regression function estimator. Second, the maximum likelihood paradigm can be employed, under the assumption that the regression function is a realization of some stochastic process. In this arti...

Periodogram smoothing of the received noisy signal is a challenging problem in speech enhancement. We present a Bayesian approach, where the instantaneous periodogram is smoothed through an adaptive smoothing parameter. By updating sufficient statistics using new samples of the noisy signal, the smoothing parameter is adjusted on-line. The performance of the novel smoothing algorithm is studied...

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. T...

Smoothing splines are a popular method for performing nonparametric regression. Most important in the implementation of this method is the choice of the smoothing parameter. This article provides a simulation study of several smoothing parameter selection methods, including two so{called risk estimation methods. To the best of the author's knowledge, the empirical performances of these two risk...

We consider functional linear regression where a real variable Y depends on a functional variable X. The functional coefficient of the model is estimated by means of smoothing splines. We derive the rates of convergence with respect to the semi-norm induced by the covariance operator of X, which comes to evaluate the error of prediction. These rates, which essentially depend on the smoothness o...

Smoothing splines are one of the most popular approaches to nonparametric regression. Wahba (1978,1983) showed that smoothing splines are also Bayes estimates and used the corresponding prior model to derive interval estimates for the regression function. Although the interval estimates work well on a global basis, they can have poor local properties. The source of this problem is the use of a ...