Abstract
Accelerated life testing has been used for years in engineering. Test units are run at high stress and fail sooner than at design stress. The lifetime at design stress is estimated by extrapolation using a regression model. This paper considers the optimum design of accelerated life tests in which two levels of stresses, high and low are constantly applied. For the exponential model the expected value of an exponential loss function of the arameter is to be used. The initial sample proportion allocated to the high stress which minimizes the expected loss function is determined. In the agriculture context, plants or animal may be the items placed on test and dosage of a chemicals, amount of fertilizer, may be the stress variable. In this paper I suggest several potential applications of constant testing in agriculture and present inferential procedure in the case in which observations have the exponential distribution.
Keywords
Accelerated life test, constant-stress, maximum likelihood
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Khamis, Imad H.
(2000).
"OPTIMUM DESIGN FOR EXPONENTIAL MODEL USING AN EXPONENTIAL LOSS FUNCTION AND ITS APPLICATIONS IN AGRICULTURE,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1251
OPTIMUM DESIGN FOR EXPONENTIAL MODEL USING AN EXPONENTIAL LOSS FUNCTION AND ITS APPLICATIONS IN AGRICULTURE
Accelerated life testing has been used for years in engineering. Test units are run at high stress and fail sooner than at design stress. The lifetime at design stress is estimated by extrapolation using a regression model. This paper considers the optimum design of accelerated life tests in which two levels of stresses, high and low are constantly applied. For the exponential model the expected value of an exponential loss function of the arameter is to be used. The initial sample proportion allocated to the high stress which minimizes the expected loss function is determined. In the agriculture context, plants or animal may be the items placed on test and dosage of a chemicals, amount of fertilizer, may be the stress variable. In this paper I suggest several potential applications of constant testing in agriculture and present inferential procedure in the case in which observations have the exponential distribution.
