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20092026
most citedCharacterizing Foreground for redshifted 21-cm radiation: 150 MHz GMRT observations

116 citations · 1.2k across the 79 of their papers we have counts for

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Showing 2018Show all

6 papers · 1 filter

stat.ME2018★ 1 cited

Robust and Efficient Estimation in the Parametric Cox Regression Model under Random Censoring

Abhik Ghosh, Ayanendranath Basu

Cox proportional hazard regression model is a popular tool to analyze the relationship between a censored lifetime variable with other relevant factors. The semi-parametric Cox mod…

math.ST2018

Power and Level Robustness of A Composite Hypothesis Testing under Independent Non-Homogeneous Data

Abhik Ghosh, Ayanendranath Basu

Robust tests of general composite hypothesis under non-identically distributed observations is always a challenge. Ghosh and Basu (2018, Statistica Sinica, 28, 1133--1155) have pro…

physics.soc-ph2018★ 18 cited

Universal City-size distributions through rank ordering

Abhik Ghosh, Banasri Basu

We consider a two-parameter discrete generalized beta (DGB) distribution and propose its universal applications to study the size-distribution of the urban agglomerations across va…

astro-ph.IM2018★ 47 cited

HI 21cm Cosmology and the Bi-spectrum: Closure Diagnostics in Massively Redundant Interferometric Arrays

C. L. Carilli, Bojan Nikolic, Nithyanandan Thyagarajan +62

New massively redundant low frequency arrays allow for a novel investigation of closure relations in interferometry. We employ commissioning data from the Hydrogen Epoch of Reioniz…

stat.ME2018★ 1 cited

Robust Wald-type test in GLM with random design based on minimum density power divergence estimators

Ayanendranath Basu, Abhik Ghosh, Abhijit Mandal +2

We consider the problem of robust inference under the generalized linear model (GLM) with stochastic covariates. We derive the properties of the minimum density power divergence es…

stat.ME2018★ 17 cited

Ultrahigh-dimensional Robust and Efficient Sparse Regression using Non-Concave Penalized Density Power Divergence

Abhik Ghosh, Subhabrata Majumdar

We propose a sparse regression method based on the non-concave penalized density power divergence loss function which is robust against infinitesimal contamination in very high dim…