In this article, we focus on data arriving sequentially by blocks in a stream. A semiparametric regression model involving a common effective dimension reduction (EDR) direction <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\beta $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi mathvariant="italic">β</mi> </math> </EquationSource> </InlineEquation> is assumed in each block. Our goal is to estimate this direction at each arrival of a new block. A...</equationsource></equationsource></inlineequation>

This paper proposes finite mixtures of different Archimedean copula families as a flexible tool for modelling the dependence structure in multivariate data. A novel approach to estimating the parameters in this mixture model is presented by maximizing the penalized marginal likelihood via...

We review variable selection and variable screening in high-dimensional linear models. Thereby, a major focus is an empirical comparison of various estimation methods with respect to true and false positive selection rates based on 128 different sparse scenarios from semi-real data (real data...

In the context of the continuously updated generalized-methods-of-moments (GMM), this study evaluates the finite sample properties of Wald- and criterion-based bootstrap inference for a class of models defined by non-linear conditional moment functions. This work provides simulation evidence...

The Deepwater Horizon oil discharge in the Gulf of Mexico is considered to be one of the worst environmental disasters to date. The spread of the oil spill and its consequences thereof had various environmental impacts. The National Oceanic and Atmospheric Administration (NOAA) in conjunction...

Maximum likelihood estimation of the concentration parameter of von Mises–Fisher distributions involves inverting the ratio <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$R_\nu=I_{\nu +1} / I_\nu $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <msub> <mi>R</mi> <mi mathvariant="italic">ν</mi> </msub> <mo>=</mo> <msub> <mi>I</mi> <mrow> <mi mathvariant="italic">ν</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo stretchy="false">/</mo> <msub> <mi>I</mi> <mi mathvariant="italic">ν</mi> </msub> </mrow> </math> </EquationSource> </InlineEquation> of modified Bessel functions and computational methods are required to invert these functions using...</equationsource></equationsource></inlineequation>

We point out an error in the proof of the main result of the paper of Tanabe et al. (Comput Stat 22:145–157, <CitationRef CitationID="CR7">2007</CitationRef>) concerning a parameter estimation for von Mises–Fisher distributions, we correct the proof of the main result and we present a short alternative proof. Copyright Springer-Verlag...</citationref>

Suppose that random samples are taken from <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$k$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi>k</mi> </math> </EquationSource> </InlineEquation> treatment groups and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$l$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mi>l</mi> </math> </EquationSource> </InlineEquation> control groups, where the observations in each group have a two-parameter exponential distribution. We consider the problem of constructing simultaneous confidence intervals for the differences between...</equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation>

We study the problem of estimating multiple predictive functions from a dictionary of basis functions in the nonparametric regression setting. Our estimation scheme assumes that each predictive function can be estimated in the form of a linear combination of the basis functions. By assuming that...

Nearest neighbour classification requires a good distance metric. Previous approaches try to learn a quadratic distance metric learning so that observations of different classes are well separated. For high-dimensional problems, where many uninformative variables are present, it is attractive to...