Probal Chaudhuri

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A nonparametric function estimation method called SUPPORT (“Smoothed and Unsmoothed Piecewise-Polynomial Regression Trees”) is described. The estimate is typically made up of several pieces, each piece being obtained by fitting a polynomial regression to the observations in a subregion of the data space. Partitioning is carried out recursively as in a(More)
For xed 2 (0; 1), the quantile regression function gives the th quantile (x) in the conditional distribution of a response variable Y given the value X = x of a vector of covariates. It can be used to measure the e ect of covariates not only in the center of a population, but also in the upper and lower tails. A functional that summarizes key features of(More)
A method that blends tree-structured nonparametric regression with classical maximum likelihood is used in a generalized regression setting. The function estimates constructed are piecewise polynomials and are produced together with decision trees containing useful information on the regressors. Fitting is carried out by applying maximum likelihood(More)
An affine equivariant version of multivariate median is introduced. The proposed median is easy to compute and has some appealing geometric features that are related to the configuration of a multivariate data cloud. The transformation and re-transformation approach used in the construction of the median has some fundamental connection with the data driven(More)
Many edge detectors are available in image processing literature where the choices of input parameters are to be made by the user. Most of the time, such choices are made on an ad-hoc basis. In this article, an edge detector is proposed where thresholding is performed using statistical principles. Local standardization of thresholds for each individual(More)
This paper develops a methodology for finding which features in a noisy image are strong enough to be distinguished from background noise. It is based on scale space, i.e. a family of smooths of the image. Pixel locations having statistically significant gradient and/or curvature are highlighted by colored symbols. The gradient version is enhanced by(More)
Many of the available methods for detecting Genomic Islands (GIs) in prokaryotic genomes use markers such as transposons, proximal tRNAs, flanking repeats etc., or they use other supervised techniques requiring training datasets. Most of these methods are primarily based on the biases in GC content or codon and amino acid usage of the islands. However,(More)