ENBIS: European Network for Business and Industrial Statistics

Abstract

Submitted by

Dimitris Ioannides

Authors

Bagkavos Dimitris, Ioannides Dimitris Kalamatianou Aglaia.

Affiliation

Accenture Marketing in Athens, University of Macedonia and Pantion University .

Abstract

The survival function is one of the most important functions in large variety of industrial problems such as reliability analysis, industrial strength testing etc, as it addresses concepts such as scheduling, maintenance, improved system design, cost analysis, etc. Kernel based estimation of the survival function imposes minimal assumptions on the data and thus offers large flexibility. Based on an existing kernel survival function estimate that admits censored data, we develop confidence intervals to help assess the validity of the estimate. Practical issues of estimation are discussed and then the developments are applied to a real data set. The results are analyzed and discussed further.

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