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Theory & Methods: Data Sharpening for Hazard Rate Estimation

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  • Gerda Claeskens
  • Peter Hall

Abstract

Data sharpening is a general tool for enhancing the performance of statistical estimators, by altering the data before substituting them into conventional methods. In one of the simplest forms of data sharpening, available for curve estimation, an explicit empirical transformation is used to alter the data. The attraction of this approach is diminished, however, if the formula has to be altered for each different application. For example, one could expect the formula for use in hazard rate estimation to differ from that for straight density estimation, since a hazard rate is a ratio–type functional of a density. This paper shows that, in fact, identical data transformations can be used in each case, regardless of whether the data involve censoring. This dramatically simplifies the application of data sharpening to problems involving hazard rate estimation, and makes data sharpening attractive.

Suggested Citation

  • Gerda Claeskens & Peter Hall, 2002. "Theory & Methods: Data Sharpening for Hazard Rate Estimation," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 44(3), pages 277-283, September.
    • Handle: RePEc:bla:anzsta:v:44:y:2002:i:3:p:277-283
    DOI: 10.1111/1467-842X.00230
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