An L 1 analysis of a kernel-based hazard rate estimator

2018 ◽  
Vol 60 (1) ◽  
pp. 43-64
Author(s):  
Hairui Hua ◽  
Dimitrios Bagkavos ◽  
Prakash N. Patil
Keyword(s):  
Hazard Rate ◽  
Hazard Rate Estimator

scholarly journals Adjusted Hazard Rate Estimator Based on a Known Censoring Probability

2011 ◽  
Vol 40 (24) ◽  
pp. 4409-4416
Author(s):  
Ülkü Gürler ◽  
Paul Kvam
Keyword(s):  
Hazard Rate ◽  
Hazard Rate Estimator

Prediction of remaining useful life, Reliability and hazard rate of a tool

2019 ◽  
Vol 12 (3) ◽  
Author(s):  
Abhishek Baghel
Keyword(s):  
Hazard Rate ◽  
Remaining Useful Life ◽  
Useful Life

Double-smoothing in Kernel Hazard Rate Estimation

2008 ◽  
Vol 47 (02) ◽  
pp. 167-173 ◽  
Author(s):  
A. Pfahlberg ◽  
O. Gefeller ◽  
R. Weißbach
Keyword(s):  
Hazard Rate ◽  
Nearest Neighbor ◽  
Bandwidth Selection ◽  
Study Data ◽  
Computational Effort ◽  
Finite Sample ◽  
Adaptive Smoothing ◽  
Rate Estimation ◽  
Data Adaptive ◽  
Double Smoothing

Summary Objectives: In oncological studies, the hazard rate can be used to differentiate subgroups of the study population according to their patterns of survival risk over time. Nonparametric curve estimation has been suggested as an exploratory means of revealing such patterns. The decision about the type of smoothing parameter is critical for performance in practice. In this paper, we study data-adaptive smoothing. Methods: A decade ago, the nearest-neighbor bandwidth was introduced for censored data in survival analysis. It is specified by one parameter, namely the number of nearest neighbors. Bandwidth selection in this setting has rarely been investigated, although the heuristical advantages over the frequently-studied fixed bandwidth are quite obvious. The asymptotical relationship between the fixed and the nearest-neighbor bandwidth can be used to generate novel approaches. Results: We develop a new selection algorithm termed double-smoothing for the nearest-neighbor bandwidth in hazard rate estimation. Our approach uses a finite sample approximation of the asymptotical relationship between the fixed and nearest-neighbor bandwidth. By so doing, we identify the nearest-neighbor bandwidth as an additional smoothing step and achieve further data-adaption after fixed bandwidth smoothing. We illustrate the application of the new algorithm in a clinical study and compare the outcome to the traditional fixed bandwidth result, thus demonstrating the practical performance of the technique. Conclusion: The double-smoothing approach enlarges the methodological repertoire for selecting smoothing parameters in nonparametric hazard rate estimation. The slight increase in computational effort is rewarded with a substantial amount of estimation stability, thus demonstrating the benefit of the technique for biostatistical applications.

Nonparametric Methods for Hazard Rate Estimation from Right-Censored Samples.

1985 ◽  
Author(s):  
D. T. McNichols ◽  
W. J. Padgett
Keyword(s):  
Hazard Rate ◽  
Nonparametric Methods ◽  
Rate Estimation ◽  
Censored Samples

From Default Correlation to Implied Hazard Rate Densities

2008 ◽  
Author(s):  
Bill Bobey
Keyword(s):  
Hazard Rate ◽  
Default Correlation
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