This article considers estimation of regression function <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$f$$</EquationSource> </InlineEquation> in the fixed design model <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$Y(x_i)=f(x_i)+ \epsilon (x_i), i=1,\ldots ,n$$</EquationSource> </InlineEquation>, by use of the Gasser and Müller kernel estimator. The point set <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$\{ x_i\}_{i=1}^{n}\subset [0,1]$$</EquationSource> </InlineEquation> constitutes the sampling design points, and <InlineEquation ID="IEq4"> <EquationSource...</equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation>

Consider a compound Poisson process which is discretely observed with sampling interval <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\Delta $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi mathvariant="normal">Δ</mi> </math> </EquationSource> </InlineEquation> until exactly <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$n$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi>n</mi> </math> </EquationSource> </InlineEquation> nonzero increments are obtained. The jump density and the intensity of the Poisson process are unknown. In this paper, we build and study parametric estimators...</equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation>

We introduce and discuss a multivariate version of the classical median that is based on an equipartition property with respect to quarter spaces. These arise as pairwise intersections of the half-spaces associated with the coordinate hyperplanes of an orthogonal basis. We obtain results on...