• S. Bhanu Prakash
• A. Vasudeva Rao
• SD. Jilani

Since the presence of intractable terms in Maximum likelihood (ML) equation, it does not give an explicit estimator for the scale parameter in scaled generalized exponential (GE) distribution. Therefore, in this paper, we have constructed three linear estimators of the scale parameter. Two of the three linear estimators are obtained by making linear approximations to the intractable terms in the ML equation by using Least Squares (LS) method and Taylor Series (TS) method. We call the above two linear estimators as linear approximate ML estimators (LAMLEs). The other linear estimator of the scale parameter is obtained by estimation based on percentiles method called Percentile Estimator (PCE). On comparing biases of the estimators, LAMLE based on LS method is less biased than the other two linear estimators. From here, LAMLE means LAMLE based on LS method. In the investigation of performance of LAMLE through a Monte Carlo simulation, we have observed that it is almost as efficient as ML estimator, but biased than ML estimator. Also we have constructed unbiased LAMLE and unbiased PCE and compare them with BLUE based on the exact variances of the estimators. Further, we have demonstrated a numerical example to illustrate the construction of the new estimators.

Scale parameter of generalized exponential distribution; Percentile estimator; Least squares method; Linear approximate maximum likelihood estimator; Unbiased linear approximate maximum likelihood estimator; Unbiased Percentile estimator.

"Linear Approximate Maximum Likelihood Estimation in Scaled Generalized Exponential Distribution Based On Type-II Censoring", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.151-172, January-2019, Available :http://www.jetir.org/papers/JETIR1901522.pdf

"Linear Approximate Maximum Likelihood Estimation in Scaled Generalized Exponential Distribution Based On Type-II Censoring", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp151-172, January-2019, Available at : http://www.jetir.org/papers/JETIR1901522.pdf