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Cited by 29
The Minimum Density Power Divergence Approach in Building Robust Regression Models

A discrete probabilistic model for analyzing pairwise comparison matrices
Sumito Kurata, Etsuo Hamada
https://doi.org/10.1080/03610926.2018.1481975
Journal  Communications in Statistics - Theory and Methods Volume 48, Issue 15 (2019), p. 3801
Inferring gene regulatory relationships with a high-dimensional robust approach
Yangguang Zang, Qing Zhao, Qingzhao Zhang, Yang Li, Sanguo Zhang, Shuangge Ma
https://doi.org/10.1002/gepi.22047
Journal  Genetic Epidemiology Volume 41, Issue 5 (2017), p. 437
Minimum density power divergence estimator for Poisson autoregressive models
Jiwon Kang, Sangyeol Lee
https://doi.org/10.1016/j.csda.2014.06.009
Journal  Computational Statistics & Data Analysis Volume 80 (2014), p. 44
Modeling and inferences for bivariate signed integer-valued autoregressive models
Sangyeol Lee, Minyoung Jo
https://doi.org/10.1007/s42952-024-00300-4
Journal  Journal of the Korean Statistical Society (2024)
Modeling and inferences for bounded multivariate time series of counts
Sangyeol Lee, Minyoung Jo
https://doi.org/10.1007/s42952-024-00273-4
Journal  Journal of the Korean Statistical Society Volume 53, Issue 4 (2024), p. 925
Multiple values-inflated bivariate INAR time series of counts: featuring zero–one inflated Poisson-Lindly case
Sangyeol Lee, Minyoung Jo
https://doi.org/10.1007/s42952-024-00269-0
Journal  Journal of the Korean Statistical Society Volume 53, Issue 3 (2024), p. 815
Proceedings of the 2024 3rd Asia Conference on Algorithms, Computing and Machine Learning
Tingjun Ma, Changyin Zhou, Xuezhou Yan
https://doi.org/10.1145/3654823.3654892
Conference  (2024), p. 376
Recent progress in parameter change test for integer-valued time series models
Sangyeol Lee, Byungsoo Kim
https://doi.org/10.1007/s42952-020-00102-4
Journal  Journal of the Korean Statistical Society Volume 50, Issue 3 (2021), p. 730
Robust Estimation for Bivariate Poisson INGARCH Models
Byungsoo Kim, Sangyeol Lee, Dongwon Kim
https://doi.org/10.3390/e23030367
Journal  Entropy Volume 23, Issue 3 (2021), p. 367
Robust Regression with Density Power Divergence: Theory, Comparisons, and Data Analysis
Marco Riani, Anthony C. Atkinson, Aldo Corbellini, Domenico Perrotta
https://doi.org/10.3390/e22040399
Journal  Entropy Volume 22, Issue 4 (2020), p. 399
Robust Wald-type tests based on minimum Rényi pseudodistance estimators for the multiple linear regression model
E. Castilla, N. Martín, S. Muñoz, L. Pardo
https://doi.org/10.1080/00949655.2020.1787410
Journal  Journal of Statistical Computation and Simulation Volume 90, Issue 14 (2020), p. 2655
Robust adaptive variable selection in ultra-high dimensional linear regression models
Abhik Ghosh, María Jaenada, Leandro Pardo
https://doi.org/10.1080/00949655.2023.2262669
Journal  Journal of Statistical Computation and Simulation Volume 94, Issue 3 (2024), p. 571
Robust estimation for bivariate integer-valued autoregressive models based on minimum density power divergence
Sangyeol Lee, Minyoung Jo
https://doi.org/10.1080/00949655.2023.2218956
Journal  Journal of Statistical Computation and Simulation Volume 93, Issue 17 (2023), p. 3156
Robust estimation for general integer-valued autoregressive models based on the exponential-polynomial divergence
Byungsoo Kim, Sangyeol Lee
https://doi.org/10.1080/00949655.2023.2283764
Journal  Journal of Statistical Computation and Simulation Volume 94, Issue 6 (2024), p. 1300
Robust estimation for general integer-valued time series models
Byungsoo Kim, Sangyeol Lee
https://doi.org/10.1007/s10463-019-00728-0
Journal  Annals of the Institute of Statistical Mathematics Volume 72, Issue 6 (2020), p. 1371
Robust estimation for independent non-homogeneous observations using density power divergence with applications to linear regression
Abhik Ghosh, Ayanendranath Basu
https://doi.org/10.1214/13-EJS847
Journal  Electronic Journal of Statistics Volume 7, Issue none (2013)
Robust estimation for non-homogeneous data and the selection of the optimal tuning parameter: the density power divergence approach
Abhik Ghosh, Ayanendranath Basu
https://doi.org/10.1080/02664763.2015.1016901
Journal  Journal of Applied Statistics Volume 42, Issue 9 (2015), p. 2056
Robust estimation for zero-inflated poisson autoregressive models based on density power divergence
Byungsoo Kim, Sangyeol Lee
https://doi.org/10.1080/00949655.2017.1351563
Journal  Journal of Statistical Computation and Simulation Volume 87, Issue 15 (2017), p. 2981
Robust estimation in stochastic frontier models
Junmo Song, Dong-hyun Oh, Jiwon Kang
https://doi.org/10.1016/j.csda.2016.08.005
Journal  Computational Statistics & Data Analysis Volume 105 (2017), p. 243
Robust estimation of average treatment effects from panel data
Sayoni Roychowdhury, Indrila Ganguly, Abhik Ghosh
https://doi.org/10.1007/s00362-022-01389-7
Journal  Statistical Papers Volume 65, Issue 1 (2024), p. 139
Robust estimation of conditional variance of time series using density power divergences
Jin‐Hong Park, T. N. Sriram
https://doi.org/10.1002/for.2465
Journal  Journal of Forecasting Volume 36, Issue 6 (2017), p. 703
Robust test for dispersion parameter change in discretely observed diffusion processes
Junmo Song
https://doi.org/10.1016/j.csda.2019.106832
Journal  Computational Statistics & Data Analysis Volume 142 (2020), p. 106832
Robust test for structural instability in dynamic factor models
Byungsoo Kim, Junmo Song, Changryong Baek
https://doi.org/10.1007/s10463-020-00773-0
Journal  Annals of the Institute of Statistical Mathematics Volume 73, Issue 4 (2021), p. 821
Robustness concerns in high-dimensional data analyses and potential solutions
Abhik Ghosh
https://doi.org/10.1016/B978-0-323-85713-0.00032-3
Book  (2023), p. 37
Robustness in Stochastic Frontier Analysis
Alexander D. Stead, Phill Wheat, William H. Greene
https://doi.org/10.1007/978-3-031-29583-6_12
Book  Lecture Notes in Economics and Mathematical Systems (Advanced Mathematical Methods for Economic Efficiency Analysis) Volume 692 (2023), p. 197
Sequential change point test in the presence of outliers: the density power divergence based approach
Junmo Song
https://doi.org/10.1214/21-EJS1868
Journal  Electronic Journal of Statistics Volume 15, Issue 1 (2021)
Test for parameter change in the presence of outliers: the density power divergence-based approach
Junmo Song, Jiwon Kang
https://doi.org/10.1080/00949655.2020.1842407
Journal  Journal of Statistical Computation and Simulation Volume 91, Issue 5 (2021), p. 1016
The Bandwidth from the Density Power Divergence
Ro Jin Pak
https://doi.org/10.5351/CSAM.2014.21.5.435
Journal  Communications for Statistical Applications and Methods Volume 21, Issue 5 (2014), p. 435
Ultrahigh-Dimensional Robust and Efficient Sparse Regression Using Non-Concave Penalized Density Power Divergence
Abhik Ghosh, Subhabrata Majumdar
https://doi.org/10.1109/TIT.2020.3013015
Journal  IEEE Transactions on Information Theory Volume 66, Issue 12 (2020), p. 7812


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