We consider a nonparametric regression model where the response Y and the covariate X are both functional (i.e. valued in some infinite-dimensional space). We define a kernel type estimator of the regression operator and we first establish its pointwise asymptotic normality. The double...

Problems of specification of discrete bivariate statistical models by a modified power series conditional distribution and a regression function are studied. An identifiability result for a wide class of such mixtures with infinite support is obtained. Also the finite support case within a more...

Consider independent and identically distributed random variables {X, Xn, n[set membership, variant]Zd+} with either EX=0 or E X=[infinity]. We establish strong laws so that [summation operator]n[less-than-or-equals, slant]N anXn/bN--1 almost surely. Our procedure selects the...

Our goal is to predict a scalar value or a group membership from the discretized observation of curves with sharp local features that might vary both vertically and horizontally. To this aim, we propose to combine the use of the nonparametric functional regression estimator developed by Ferraty...

Functional principal components (FPC’s) provide the most important and most extensively used tool for dimension reduction and inference for functional data. The selection of the number, d, of the FPC’s to be used in a specific procedure has attracted a fair amount of attention, and a number...

In this paper we consider the classical problem of testing whether two samples of observations are from the same distribution. Since in many situations the data are multivariate or even of functional type, classical methodology is not applicable. In our approach we conceive a difference in...

Change point detection in sequences of functional data is examined where the functional observations are dependent. Of particular interest is the case where the change point is an epidemic change (a change occurs and then the observations return to baseline at a later time). The theoretical...

Maximum entropy models, motivated by applications in neuron science, are natural generalizations of the β-model to weighted graphs. Similar to the β-model, each vertex in maximum entropy models is assigned a potential parameter, and the degree sequence is the natural sufficient statistic....

In this paper we define a kernel estimator of the conditional density for a left-truncated and right-censored model based on the generalized product-limit estimator of the conditional distributed function. Under the observations with multivariate covariates form a stationary α-mixing sequence,...

Consider the semiparametric regression model yi=xiTβ+g(ti)+εi for i=1,…,n, where xi∈Rp are the random design vectors, ti are the constant sequences on [0,1], β∈Rp is an unknown vector of the slop parameter, g is an unknown real-valued function defined on the closed interval [0,1], and...