Zobrazeno 1 - 10
of 474
pro vyhledávání: '"Ghosh, Anil"'
In this paper we introduce a new measure of conditional dependence between two random vectors ${\boldsymbol X}$ and ${\boldsymbol Y}$ given another random vector $\boldsymbol Z$ using the ball divergence. Our measure characterizes conditional indepen
Externí odkaz:
http://arxiv.org/abs/2407.21456
Nearest neighbor classifier is arguably the most simple and popular nonparametric classifier available in the literature. However, due to the concentration of pairwise distances and the violation of the neighborhood structure, this classifier often s
Externí odkaz:
http://arxiv.org/abs/2407.05145
Autor:
Banerjee, Bilol, Ghosh, Anil K.
We develop a test for spherical symmetry of a multivariate distribution $P$ that works even when the dimension of the data $d$ is larger than the sample size $n$. We propose a non-negative measure $\zeta(P)$ such that $\zeta(P)=0$ if and only if $P$
Externí odkaz:
http://arxiv.org/abs/2403.12491
We propose a novel semiparametric classifier based on Mahalanobis distances of an observation from the competing classes. Our tool is a generalized additive model with the logistic link function that uses these distances as features to estimate the p
Externí odkaz:
http://arxiv.org/abs/2402.08283
Autor:
Banerjee, Bilol, Ghosh, Anil K.
In this article, we propose some two-sample tests based on ball divergence and investigate their high dimensional behavior. First, we study their behavior for High Dimension, Low Sample Size (HDLSS) data, and under appropriate regularity conditions,
Externí odkaz:
http://arxiv.org/abs/2212.08566
When the competing classes in a classification problem are not of comparable size, many popular classifiers exhibit a bias towards larger classes, and the nearest neighbor classifier is no exception. To take care of this problem, we develop a statist
Externí odkaz:
http://arxiv.org/abs/2206.10866
We propose a new model-free feature screening method based on energy distances for ultrahigh-dimensional binary classification problems. With a high probability, the proposed method retains only relevant features after discarding all the noise variab
Externí odkaz:
http://arxiv.org/abs/2205.03831
Publikováno v:
In Journal of Statistical Planning and Inference January 2025 234
Detection of change-points in a sequence of high-dimensional observations is a very challenging problem, and this becomes even more challenging when the sample size (i.e., the sequence length) is small. In this article, we propose some change-point d
Externí odkaz:
http://arxiv.org/abs/2111.14012
Over the last couple of decades, several copula based methods have been proposed in the literature to test for the independence among several random variables. But these existing tests are not invariant under monotone transformations of the variables
Externí odkaz:
http://arxiv.org/abs/1903.08987