We propose a procedure for detecting the modes of a density estimate and test their significance. We use a data-splitting approach: potential modes are identified using the first half of the data and their significance is tested with the second half of the data. The mode test is based on nonparametric confidence intervals for the eigenvalues of the Hessian. In order to get valid bootstrap confidence sets even in presence of multiplicity of the eigenvalues, we use a bootstrap based on an elementary-symmetric-polynomial transformation
We suggest two new methods for conditional density estimation. The first is based on locally fitting...
We suggest two improved methods for conditional density estimation. The rst is based on locally ttin...
We propose a nonparametric method for density estimation over (possibly complicated) spatial domains...
We propose a procedure for detecting the modes of a density estimate and test their significance. We...
We derive nonparametric confidence intervals for the eigenvalues of the Hessian at modes of a densit...
We derive non-parametric confidence intervals for the eigenvalues of the Hessian at modes of a densi...
In this study a family of estimators is developed for local maxima, or modes, of a multivariate prob...
Modes, or local maxima, are often among the most interesting features of a probability density funct...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/15...
AbstractMultivariate mode hunting is of increasing practical importance. Only a few such methods exi...
Abstract—In this contribution we introduce a clustering scheme based on mode boundary detection proc...
A new clustering approach based on mode identification is developed by applying new optimization tec...
Although Hartigan (1975) had already put forward the idea of connecting identification of subpopula...
Four nonparametric estimates of the mode of a density function are investigated. Two mode estimates ...
We suggest two new methods for conditional density estimation. The first is based on locally fitting...
We suggest two improved methods for conditional density estimation. The rst is based on locally ttin...
We propose a nonparametric method for density estimation over (possibly complicated) spatial domains...
We propose a procedure for detecting the modes of a density estimate and test their significance. We...
We derive nonparametric confidence intervals for the eigenvalues of the Hessian at modes of a densit...
We derive non-parametric confidence intervals for the eigenvalues of the Hessian at modes of a densi...
In this study a family of estimators is developed for local maxima, or modes, of a multivariate prob...
Modes, or local maxima, are often among the most interesting features of a probability density funct...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/15...
AbstractMultivariate mode hunting is of increasing practical importance. Only a few such methods exi...
Abstract—In this contribution we introduce a clustering scheme based on mode boundary detection proc...
A new clustering approach based on mode identification is developed by applying new optimization tec...
Although Hartigan (1975) had already put forward the idea of connecting identification of subpopula...
Four nonparametric estimates of the mode of a density function are investigated. Two mode estimates ...
We suggest two new methods for conditional density estimation. The first is based on locally fitting...
We suggest two improved methods for conditional density estimation. The rst is based on locally ttin...
We propose a nonparametric method for density estimation over (possibly complicated) spatial domains...
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