Suchergebnisse - "Basu, Ayanendranath"

Robust estimation in generalized linear models: the density power divergence approach von Ghosh, Abhik, Basu, Ayanendranath

“… The generalized linear model is a very important tool for analyzing real data in several application domains where the relationship between the response and …”
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A Characterization of All Single-Integral, Non-Kernel Divergence Estimators von Jana, Soham, Basu, Ayanendranath

“… Divergence measures have been used for a long time for different purposes in information theory and statistics. In particular, density-based minimum divergence …”
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A New Family of Divergences Originating From Model Adequacy Tests and Application to Robust Statistical Inference von Ghosh, Abhik, Basu, Ayanendranath

“… Minimum divergence methods are popular tools in a variety of statistical applications. We consider tubular model adequacy tests, and demonstrate that the new …”
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Characterizing the Functional Density Power Divergence Class von Ray, Souvik, Pal, Subrata, Kar, Sumit Kumar, Basu, Ayanendranath

“… Divergence measures have a long association with statistical inference, machine learning and information theory. The density power divergence and related …”
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Robust Wald‐type tests under random censoring von Ghosh, Abhik, Basu, Ayanendranath, Pardo, Leandro

“… Randomly censored survival data are frequently encountered in biomedical or reliability applications and clinical trial analyses. Testing the significance of …”
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Statistical Inference: The Minimum Distance Approach von Basu, Ayanendranath, Shioya, Hiroyuki, Park, Chanseok

“… This book gives a comprehensive account of density-based minimum distance methods and their use in statistical inference. It covers statistical distances, …”
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Robust singular value decomposition with application to video surveillance background modelling von Roy, Subhrajyoty, Ghosh, Abhik, Basu, Ayanendranath

“… The traditional method of computing singular value decomposition (SVD) of a data matrix is based on the least squares principle and is, therefore, very …”
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Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach von Ghosh, Abhik, Basu, Ayanendranath

“… The density power divergence (DPD) measure, defined in terms of a single parameter α, has proved to be a popular tool in the area of robust estimation [ 1 ] …”
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Robust inference for skewed data in health sciences von Nandy, Amarnath, Basu, Ayanendranath, Ghosh, Abhik

“… Health data are often not symmetric to be adequately modeled through the usual normal distributions; most of them exhibit skewed patterns. They can indeed be …”
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Robust estimation of fixed effect parameters and variances of linear mixed models: the minimum density power divergence approach von Saraceno, Giovanni, Ghosh, Abhik, Basu, Ayanendranath, Agostinelli, Claudio

“… Many real-life data sets can be analyzed using linear mixed models (LMMs). Since these are ordinarily based on normality assumptions, under small deviations …”
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A Generalized Relative (α, β)-Entropy: Geometric Properties and Applications to Robust Statistical Inference von Ghosh, Abhik, Basu, Ayanendranath

“… Entropy and relative entropy measures play a crucial role in mathematical information theory. The relative entropies are also widely used in statistics under …”
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Robust Wald-type tests for non-homogeneous observations based on the minimum density power divergence estimator von Basu, Ayanendranath, Ghosh, Abhik, Martin, Nirian, Pardo, Leandro

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Statistical inference based on bridge divergences von Kuchibhotla, Arun Kumar, Mukherjee, Somabha, Basu, Ayanendranath

“… M -estimators offer simple robust alternatives to the maximum likelihood estimator. The density power divergence (DPD) and the logarithmic density power …”
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Robust statistical inference based on the C-divergence family von Maji, Avijit, Ghosh, Abhik, Basu, Ayanendranath, Pardo, Leandro

“… This paper describes a family of divergences, named herein as the C -divergence family, which is a generalized version of the power divergence family and also …”
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Robust Inference Using the Exponential-Polynomial Divergence von Singh, Pushpinder, Mandal, Abhijit, Basu, Ayanendranath

“… Density-based minimum divergence procedures represent popular techniques in parametric statistical inference. They combine strong robustness properties with …”
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A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator von Basu, Ayanendranath, Ghosh, Abhik, Mandal, Abhijit, Martín, Nirian, Pardo, Leandro

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Robust Wald-type tests in GLM with random design based on minimum density power divergence estimators von Basu, Ayanendranath, Ghosh, Abhik, Mandal, Abhijit, Martin, Nirian, Pardo, Leandro

“… We consider the problem of robust inference under the generalized linear model (GLM) with stochastic covariates. We derive the properties of the minimum …”
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Statistical inference based on a new weighted likelihood approach von Majumder, Suman, Biswas, Adhidev, Roy, Tania, Bhandari, Subir Kumar, Basu, Ayanendranath

“… We discuss a new weighted likelihood method for robust parametric estimation. The method is motivated by the need for generating a simple estimation strategy …”
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On the robustness of a divergence based test of simple statistical hypotheses von Ghosh, Abhik, Basu, Ayanendranath, Pardo, Leandro

“… The most popular hypothesis testing procedure, the likelihood ratio test, is known to be highly non-robust in many real situations. Basu et al. (2013a) …”
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Does the generalized mean have the potential to control outliers? von Mukhopadhyay, Soumalya, Das, Amlan Jyoti, Basu, Ayanendranath, Chatterjee, Aditya, Bhattacharya, Sabyasachi

“… The efficacy of the generalized mean in controlling outliers is explored in this paper. We found that in the presence of outliers in the data, the generalized …”
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