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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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Dumitrescu M, Gámiz M, Limnios N. Minimum divergence estimators for the Radon–Nikodym derivatives of the semi-Markov kernel. STATISTICS-ABINGDON 2015. [DOI: 10.1080/02331888.2015.1060239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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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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Gámiz Pérez ML, Janys L, Martínez Miranda MD, Nielsen JP. Bandwidth selection in marker dependent kernel hazard estimation. Comput Stat Data Anal 2013. [DOI: 10.1016/j.csda.2013.06.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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