Bias reduction in nonparametric hazard rate estimation
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Bias reduction in nonparametric hazard rate estimation by Dimitrios Ioannis Bagkavos

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Published by University of Birmingham in Birmingham .
Written in English

Book details:

Edition Notes

Thesis (Ph.D.) - University of Birmingham, Faculty of Science, School of Mathematics and Statistics, 2003.

Statementby Dimitrios Ioannis Bagkavos.
The Physical Object
Paginationviii, 139 p. ;
Number of Pages139
ID Numbers
Open LibraryOL21684755M

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A new estimate of the hazard rate function is proposed, based on nonparametric transformations of the data and motivated by the bias expression of conventional kernel hazard : Dimitrios Bagkavos.   ABSTRACTIn this paper, we propose two kernel density estimators based on a bias reduction technique. We study the properties of these estimators and compare them with Parzen–Rosenblatt's density estimator and Mokkadem, A., Pelletier, M., and Slaoui, Y. (, ‘The stochastic approximation method for the estimation of a multivariate probability density’, J. by: 1. On the other hand, bias correction of the maximum likelihood estimator is tied to a specific parameterization. Lack of equivariance also affects the so-called implicit bias reduction methods (Kosmidis, ) that achieve first-oder bias correction through a modification of the score equation, following Firth ().File Size: KB. Nonparametric methods play a central role in modern empirical work. While they provide inference pro-cedures that are more robust to parametric misspeci!cation bias, they may be quite sensitive to tuning parameter choices. We study the e"ects of bias correction on con!dence interval coverage in the context.

On the E ect of Bias Estimation on Coverage Accuracy in Nonparametric Inference Sebastian Calonico Department of Economics University of Miami Coral Gables, FL Matias D. Cattaneo Department of Economics Department of Statistics University of Michigan Ann Arbor, MI Max H. Farrell Booth School of Business University of Chicago Chicago Cited by: 2. Bayesian Nonparametric Estimation of Hazard Rate in Survival Analysis Using Gibbs Sampler J. Timkov´a Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic. Abstract. This text describes the way of estimating the hazard rate of survival data based on techniques which were introduced by Arjas, E., Gasbarra, D. (). Ingrid Van Keilegom & Noël Veraverbeke, "Hazard Rate Estimation in Nonparametric Regression with Censored Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(4), pages , Perch Nielsen & Carsten Tanggaard, "Boundary and Bias Correction in Kernel Hazard Estimation," Scandinavian Journal of. In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called statistics, "bias" is an objective property of an estimator. Bias can also be measured with respect to the median, rather than the mean (expected value), in.

"Bias Reduction In Nonparametric Diffusion Coefficient Estimation," Econometric Theory, Cambridge University Press, vol. 19(5), pages , October. Taoufik Bouezmarni & Jeroen Rombouts, " Density and hazard rate estimation for censored and α-mixing data using gamma kernels," Journal of Nonparametric Statistics, Taylor & Francis. Subjects Primary: 62G Estimation Secondary: 62D Sampling theory, sample surveys 60F Central limit and other weak theorems. Keywords Empirical distribution function biased sampling maximum likelihood weighted distribution. CitationCited by: The estimation of the hazard function from randomly censored data by the kernel method. Ann. Statist. 11, Nonparametric estimation of density and hazard rate functions [51] Tanner, M. A. and Wong, W. H. (). Data-based nonparametric estimation of the hazard function with applications to model diagnostics and exploratory analysis Cited by: In this article, we propose a new method of bias reduction in nonparametric regression estimation. The proposed new estimator has asymptotic bias order h 4, where h is a smoothing parameter, in contrast to the usual bias order h 2 for the local linear regression. In addition, the proposed estimator has the same order of the asymptotic variance as the local linear by: 2.