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Hiabu M, Mammen E, Martínez-Miranda MD, Nielsen JP. Smooth Backfitting of Proportional Hazards With Multiplicative Components. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2020.1753520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Munir Hiabu
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
| | - Enno Mammen
- Institute for Applied Mathematics, Heidelberg University, Heidelberg, Germany
| | | | - Jens P. Nielsen
- Cass Business School, City, University of London, London, UK
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Hiabu M, Mammen E, Martìnez-Miranda MD, Nielsen JP. In-sample forecasting with local linear survival densities. Biometrika 2016. [DOI: 10.1093/biomet/asw038] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Gámiz ML, Mammen E, Miranda MDM, Nielsen JP. Double one-sided cross-validation of local linear hazards. J R Stat Soc Series B Stat Methodol 2015. [DOI: 10.1111/rssb.12133] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Enno Mammen
- Heidelberg University; Germany
- Higher School of Economics; Moscow Russia
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Hiabu M, Martínez-Miranda MD, Nielsen JP, Spreeuw J, Tanggaard C, Villegas AM. Global Polynomial Kernel Hazard Estimation. REVISTA COLOMBIANA DE ESTADÍSTICA 2015. [DOI: 10.15446/rce.v38n2.51668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
<p>This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically reduces bias with unchanged variance. A simulation study investigates the finite-sample properties of GPA. The method is tested on local constant and local linear estimators. From the simulation experiment we conclude that the global estimator improves the goodness-of-fit. An especially encouraging result is that the bias-correction works well for small samples, where traditional bias reduction methods have a tendency to fail.</p>
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Zougab N, Adjabi S, Kokonendji CC. Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation. Comput Stat Data Anal 2014. [DOI: 10.1016/j.csda.2014.02.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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