Pardo, Leandro2023-06-172023-06-1720191. Basu, A.; Shioya, H.; Park, C. Statistical Inference: The Minimum Distance Approach; Chapman and Hall/CRC: Boca Raton, FL, USA, 2011. [Google Scholar] 2. Pardo, L. Statistical Inference Based on Divergence Measures; Chapman and Hall/CRC: Boca Raton, FL, USA, 2006. [Google Scholar] 3. Ghosh, A.; Basu, A.; Pardo, L. Robust Wald-type tests under random censoring. arXiv, 2017; arXiv:1708.09695. [Google Scholar] 4. Basu, A.; Mandal, A.; Martín, N.; Pardo, L. A Robust Wald-Type Test for Testing the Equality of Two Means from Log-Normal Samples. Methodol. Comput. Appl. Probab. 2019, 21, 85–107. [Google Scholar] [CrossRef] 5. Basu, A.; Mandal, A.; Martin, N.; Pardo, L. Robust tests for the equality of two normal means based on the density power divergence. Metrika 2015, 78, 611–634. [Google Scholar] [CrossRef] 6. Basu, A.; Ghosh, A.; Mandal, A.; Martín, N.; Pardo, L. A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator. Electron. J. Stat. 2017, 11, 2741–2772. [Google Scholar] [CrossRef] 7. Castilla, E.; Ghosh, A.; Martín, N.; Pardo, L. New robust statistical procedures for polytomous logistic regression models. Biometrics 2019, in press. [Google Scholar] [CrossRef] 8. Martín, N.; Pardo, L.; Zografos, K. On divergence tests for composite hypotheses under composite likelihood. Stat. Pap. 2019, in press. [Google Scholar] [CrossRef] 9. Ghosh, A.; Basu, A. A Generalized Relative (α,β)-Entropy: Geometric Properties and Applications to Robust Statistical Inference. Entropy 2018, 20, 347. [Google Scholar] [CrossRef] 10. Maji, A.; Ghosh, A.; Basu, A. The Logarithmic Super Divergence and Asymptotic Inference Properties. AStA Adv. Stat. Anal. 2016, 100, 99–131. [Google Scholar] [CrossRef] 11. Wu, Y.; Hooker, G. Asymptotic Properties for Methods Combining the Minimum Hellinger Distance Estimate and the Bayesian Nonparametric Density Estimate. Entropy 2018, 20, 955. [Google Scholar] [CrossRef] 12. Beran, R. Minimum Hellinger Distance Estimates for Parametric Models. Ann. Stat. 1977, 5, 445–463. [Google Scholar] [CrossRef] 13. Castilla, E.; Martín, N.; Pardo, L.; Zografos, K. Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator. Entropy 2018, 20, 18. [Google Scholar] [CrossRef] 14. Varin, C.; Reid, N.; Firth, D. An overview of composite likelihood methods. Stat. Sin. 2011, 21, 4–42. [Google Scholar] 15. Broniatowski, M.; Jurečková, J.; Moses, A.K.; Miranda, E. Composite Tests under Corrupted Data. Entropy 2019, 21, 63. [Google Scholar] [CrossRef] 16. Abdullah, O. Convex Optimization via Symmetrical Hölder Divergence for a WLAN Indoor Positioning System. Entropy 2018, 20, 639. [Google Scholar] [CrossRef] 17. Nielsen, F.; Sun, K.; Marchand-Maillet, S. k-Means Clustering with Hölder Divergences. In Proceedings of the International Conference on Geometric Science of Information, Paris, France, 7–9 November 2017. [Google Scholar] 18. Broniatowski, M.; Jurečková, J.; Kalina, J. Likelihood Ratio Testing under Measurement Errors. Entropy 2018, 20, 966. [Google Scholar] [CrossRef] 19. Alba-Fernández, M.V.; Jiménez-Gamero, M.D.; Ariza-López, F.J. Minimum Penalized ϕ-Divergence Estimation under Model Misspecification. Entropy 2018, 20, 329. [Google Scholar] [CrossRef] 20. Markatou, M.; Chen, Y. Non-Quadratic Distances in Model Assessment. Entropy 2018, 20, 464. [Google Scholar] [CrossRef] 21. Kateri, M. ϕ-Divergence in Contingency Table Analysis. Entropy 2018, 20, 324. [Google Scholar] [CrossRef] 22. Goodman, L.A. Association models and canonical correlation in the analysis of cross-classifications having ordered categories. J. Am. Stat. Assoc. 1981, 76, 320–334. [Google Scholar] 23. Kawashima, T.; Fujisawa, H. Robust and Sparse Regression via γ-Divergence. Entropy 2017, 19, 608. [Google Scholar] [CrossRef] 24. Kanamori, T.; Fujisawa, H. Robust estimation under heavy contamination using unnormalized models. Biometrika 2015, 102, 559–572. [Google Scholar] [CrossRef] 25. Fan, J.; Li, R. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties. J. Am. Stat. Assoc. 2001, 96, 1348–1360. [Google Scholar] [CrossRef][Green Version] 26. Zhang, C.H. Nearly unbiased variable selection under minimax concave penalty. Ann. Stat. 2010, 38, 894–942. [Google Scholar] [CrossRef][Green Version] 27. Zhang, C.; Zhang, Z. Robust-BD Estimation and Inference for General Partially Linear Models. Entropy 2017, 19, 625. [Google Scholar] [CrossRef] 28. Fan, J.; Huang, T. Profile likelihood inferences on semiparametric varying-coefficient partially linear models. Bernoulli 2005, 11, 1031–1057. [Google Scholar] [CrossRef] 29. Toma, A.; Fulga, C. Robust Estimation for the Single Index Model Using Pseudodistances. Entropy 2018, 20, 374. [Google Scholar] [CrossRef] 30. Sharpe, W.F. A simplified model to portfolio analysis. Manag. Sci. 1963, 9, 277–293. [Google Scholar] [CrossRef] 31. Li, L.; Vidyashankar, A.N.; Diao, G.; Ahmed, E. Robust Inference after Random Projections via Hellinger Distance for Location-scale Family. Entropy 2019, 21, 348. [Google Scholar] [CrossRef] 32. Guo, X.; Zhang, C. Robustness Property of Robust-BD Wald-Type Test for Varying-Dimensional General Linear Models. Entropy 2018, 20, 168. [Google Scholar] [CrossRef] 33. Zhang, C.M.; Guo, X.; Cheng, C.; Zhang, Z.J. Robust-BD estimation and inference for varying-dimensional general linear models. Stat. Sin. 2012, 24, 653–673. [Google Scholar] [CrossRef] 34. Heritier, S.; Ronchetti, E. Robust bounded-influence tests in general parametric models. J. Am. Stat. Assoc. 1994, 89, 897–904. [Google Scholar] [CrossRef] 35. Ronchetti, E.; Trojani, F. Robust inference with GMM estimators. J. Econom. 2001, 101, 37–69. [Google Scholar] [CrossRef][Green Version] 36. Basu, A.; Ghosh, A.; Martin, N.; Pardo, L. Robust Wald-type tests for non-homogeneous observations based on minimum density power divergence estimator. Metrika 2018, 81, 493–522. [Google Scholar] [CrossRef] 37. Hirose, K.; Masuda, H. Robust Relative Error Estimation. Entropy 2018, 20, 632. [Google Scholar] [CrossRef] 38. Fujisawa, H.; Eguchi, S. Robust parameter estimation with a small bias against heavy contamination. J. Multivar. Anal. 2008, 99, 2053–2081. [Google Scholar] [CrossRef][Green Version]1099-430010.3390/e21040391https://hdl.handle.net/20.500.14352/12433engAtribución 3.0 EspañaNew Developments in Statistical Information Theory Based on Entropy and Divergence Measuresjournal articlehttps://doi.org/10.3390/e21040391https://www.mdpi.com/1099-4300/21/4/391open access162.2Wald type testVariable selectionLikelihoodRobustInferenceModelsTestsTest de WaldProbabilidadModelosInferenciaMatemáticas (Matemáticas)Probabilidades (Matemáticas)12 Matemáticas