Nonparametric estimation of hazard rate under the constraint of monotonicity
This article shows how to smoothly "monotonize" standard kernel estimators of hazard rate, using bootstrap weights. Our method takes a variety of forms, depending on choice of kernel estimator and on the distance function used to define a certain constrained optimization problem. We confine attention to a particularly simple kernel approach and explore a range of distance functions. It is straightforward to reduce "quadratic" inequality constraints to "linear" equality constraints, and so our...[Show more]
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|Source:||Journal of Computational and Graphical Statistics|
|01_Hall_Nonparametric_estimation_of_2001.pdf||440.04 kB||Adobe PDF||Request a copy|
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