116 citations · 1.2k across the 79 of their papers we have counts for
35 papers · 1 filter
A Componentwise Estimation Procedure for Multivariate Location and Scatter: Robustness, Efficiency and Scalability
Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh
Covariance matrix estimation is an important problem in multivariate data analysis, both from theoretical as well as applied points of view. Many simple and popular covariance matr…
Robust inference for linear regression models with possibly skewed error distribution
Amarnath Nandy, Ayanendranath Basu, Abhik Ghosh
Traditional methods for linear regression generally assume that the underlying error distribution, equivalently the distribution of the responses, is normal. Yet, sometimes real li…
Robust Principal Component Analysis using Density Power Divergence
Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh
Principal component analysis (PCA) is a widely employed statistical tool used primarily for dimensionality reduction. However, it is known to be adversely affected by the presence…
Robust and Efficient Estimation in Ordinal Response Models using the Density Power Divergence
Arijit Pyne, Subhrajyoty Roy, Abhik Ghosh +1
In real life, we frequently come across data sets that involve some independent explanatory variable(s) generating a set of ordinal responses. These ordinal responses may correspon…
Robust Estimation of Average Treatment Effects from Panel Data
Sayoni Roychowdhury, Indrila Ganguly, Abhik Ghosh
In order to evaluate the impact of a policy intervention on a group of units over time, it is important to correctly estimate the average treatment effect (ATE) measure. Due to lac…
Robust adaptive Lasso in high-dimensional logistic regression
Ayanendranath Basu, Abhik Ghosh, María Jaenada +1
Penalized logistic regression is extremely useful for binary classification with large number of covariates (higher than the sample size), having several real life applications, in…
RaJIVE: Robust Angle Based JIVE for Integrating Noisy Multi-Source Data
Erica Ponzi, Magne Thoresen, Abhik Ghosh
With increasing availability of high dimensional, multi-source data, the identification of joint and data specific patterns of variability has become a subject of interest in many…
Robust Estimation under Linear Mixed Models: The Minimum Density Power Divergence Approach
Giovanni Saraceno, Abhik Ghosh, Ayanendranath Basu +1
Many real-life data sets can be analyzed using Linear Mixed Models (LMMs). Since these are ordinarily based on normality assumptions, under small deviations from the model the infe…
Robust Hypothesis Testing and Model Selection for Parametric Proportional Hazard Regression Models
Amarnath Nandy, Abhik Ghosh, Ayanendranath Basu +1
The semi-parametric Cox proportional hazards regression model has been widely used for many years in several applied sciences. However, a fully parametric proportional hazards mode…
Robust Clustering with Normal Mixture Models: A Pseudo -Likelihood Approach
Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh
As in other estimation scenarios, likelihood based estimation in the normal mixture set-up is highly non-robust against model misspecification and presence of outliers (apart from…
A robust variable screening procedure for ultra-high dimensional data
Abhik Ghosh, Magne Thoresen
Variable selection in ultra-high dimensional regression problems has become an important issue. In such situations, penalized regression models may face computational problems and…
Robust Generalised Quadratic Discriminant Analysis
Abhik Ghosh, Rita SahaRay, Sayan Chakrabarty +1
Quadratic discriminant analysis (QDA) is a widely used statistical tool to classify observations from different multivariate Normal populations. The generalized quadratic discrimin…
Robust adaptive variable selection in ultra-high dimensional linear regression models
Abhik Ghosh, Maria Jaenada, Leandro Pardo
We consider the problem of simultaneous variable selection and estimation of the corresponding regression coefficients in an ultra-high dimensional linear regression models, an ext…
Robust Parametric Inference for Finite Markov Chains
Abhik Ghosh
We consider the problem of statistical inference in a parametric finite Markov chain model and develop a robust estimator of the parameters defining the transition probabilities vi…
A Robust Generalization of the Rao Test
Ayanendranath Basu, Abhik Ghosh, Nirian Martin +1
This paper presents new families of Rao-type test statistics based on the minimum density power divergence estimators which provide robust generalizations for testing simple and co…
Consistent Fixed-Effects Selection in Ultra-high dimensional Linear Mixed Models with Error-Covariate Endogeneity
Abhik Ghosh, Magne Thoresen
Recently, applied sciences, including longitudinal and clustered studies in biomedicine require the analysis of ultra-high dimensional linear mixed effects models where we need to…
Robust semiparametric inference for polytomous logistic regression with complex survey design
Elena Castilla, Abhik Ghosh, Nirian Martin +1
Analyzing polytomous response from a complex survey scheme, like stratified or cluster sampling is very crucial in several socio-economics applications. We present a class of minim…
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…
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…
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…
Robust Wald-Type Tests under Random Censoring with Applications to Clinical Trial Analyses
Abhik Ghosh, Ayanendranath Basu, Leandro Pardo
Randomly censored survival data are frequently encountered in applied sciences including biomedical or reliability applications and clinical trial analyses. Testing the significanc…
Robust Wald-type tests for non-homogeneous observations based on minimum density power divergence estimator
Ayanendranath Basu, Abhik Ghosh, Nirian Martin +1
This paper considers the problem of robust hypothesis testing under non-identically distributed data. We propose Wald-type tests for both simple and composite hypothesis for indepe…
Robust Inference under the Beta Regression Model with Application to Health Care Studies
Abhik Ghosh
Data on rates, percentages or proportions arise frequently in many different applied disciplines like medical biology, health care, psychology and several others. In this paper, we…
New robust statistical procedures for polytomous logistic regression models
E. Castilla, A. Ghosh, N. Martín +1
This paper derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polyto…