We study regression estimation when the explanatory variable is functional. Nonparametric estimates of the regression operator have been recently introduced. They depend on a smoothing factor which controls its behavior, and the aim of our work is to construct some data-driven criterion for choosing this smoothing parameter. The criterion can be formulated in terms of a functional version of cr...

Kernel based learning has found wide applications in several data mining problems. In this paper, we propose a modified classical linear kernel using an automatic smoothing parameter (Sp) selection compared with the existing approach. We designed the Sp values using the Eigen values computed from the dataset. Experiment results using some classification related benchmark datasets reveal that th...

Generalized linear mixed-effect models are widely used for the analysis of correlated nonGaussian data such as those found in longitudinal studies. In this article, we consider extensions with nonparametric fixed effects and parametric random effects. The estimation is through the penalized likelihood method, and our focus is on the efficient computation and the effective smoothing parameter se...

Statistical approaches to natural language processing generally obtain the parameters by using the maximum likelihood estimation (MLE ) method. The MLE approaches, however, may fail to achieve good performance in difficult tasks, because the discrimination and robustness issues are not taken into consideration in the estimation processes. Motivated by that concern, a discriminationand robustnes...

The Cp selection criterion is a popular method to choose the smoothing parameter in spline regression. Another widely used method is the generalized maximum likelihood (GML) derived from a normal-theory empirical Bayes framework. These two seemingly unrelated methods, have been shown in Efron (Ann. Statist. 29 (2001) 470) and Kou and Efron (J. Amer. Statist. Assoc. 97 (2002) 766) to be actually...

A crucial ingredient of the Neyman (1937) “smooth type” tests for goodness-of-fit is the selection of the smoothing parameter, which is influenced dramatically by one’s notion of optimality. This is illustrated with the test based on the L1 difference between the histogram estimator and its expectation under the null hypothesis. We determine asymptotically the minimum power β(n,m, δ) as a funct...

Let fX n g be a sequence of i.i.d. random variables whose common cdf F belongs to the domain of attraction of an extreme value distribution. A frequently used estimator of the extreme value parameter is the moment estimator (Dekkers, Einmahl and de Haan, 1989). Because the moment estimator is a function of k = k(n), the number of upper order statistics used in estimation and which is only subje...

Biophysically detailed models of single cells are difficult to fit to real data. Recent advances in imaging techniques allow simultaneous access to various intracellular variables, and these data can be used to significantly facilitate the modelling task. These data, however, are noisy, and current approaches to building biophysically detailed models are not designed to deal with this. We exten...

Data-driven bandwidth selection based on the gradient of an unknown regression function is considered. Uncovering gradients nonparametrically is of crucial importance across a broad range of economic environments such as determining risk premium or recovering distributions of individual preferences. The procedure developed here is shown to deliver bandwidths which have the optimal rate of conve...

IBM models are important word alignment models in Machine Translation. Based on the Maximum Likelihood Estimation principle to estimate their parameters, the models could easily overfit training data when data are sparse. Even though smoothing is a very popular solution in Language Model, there is still a lack of studies on smoothing for word alignment. In this paper, we propose a framework whi...