On Shrinkage Estimation of the Exponential Scale Parameter

Pandey, .; Srivastava,

doi:10.1109/tr.1985.5222124

Cited by 17 publications

(10 citation statements)

References 2 publications

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“…A value of K near to one implies strong belief in sample values. If K is not known, Pandey (1983) suggested two shrunken estimators for θ as…”

Section: Introductionmentioning

confidence: 99%

“…(1.5) Pandey (1983) has shown that Tj, (j = 3, 4) have smaller MSE than MMSE estimator T1 if |r| ≤ 0.3, where r = {(θ0/θ) − 1}. Pandey and Srivastava (1985) proposed an estimator for θ as…”

Section: Introductionmentioning

confidence: 99%

“…For p = 1, (1.13) reduces to Pandey and Srivastava (1985) estimator T5 while p = −1, Tp 1 reduces to the estimator…”

Section: Introductionmentioning

confidence: 99%

“…In particular, some new shrinkage estimators are identified and compared with Pandey (1983), Pandey and Srivastava (1985) and Jani (1991) estimators. Numerical illustrations are also provided.…”

mentioning

confidence: 99%

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Some Alternative Classes of Shrinkage Estimators for a Scale Parameter of the Exponential Distribution

Singh¹,

Singh²,

Kim

2012

Korean Journal of Applied Statistics

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“…A value of K near to one implies strong belief in sample values. If K is not known, Pandey (1983) suggested two shrunken estimators for θ as…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For p = 1, (1.13) reduces to Pandey and Srivastava (1985) estimator T5 while p = −1, Tp 1 reduces to the estimator…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Some Alternative Classes of Shrinkage Estimators for a Scale Parameter of the Exponential Distribution

Singh¹,

Singh²,

Kim

2012

Korean Journal of Applied Statistics

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“…Perhaps the most popular technique that utilizes the knowledge of point guess is the shrinkage technique, originally suggested by Thompson (1968). He suggested shrinking the usual estimator suggested by Pandey and Srivastava (1985) and Pandey and Upadhyay (1988). Their technique chooses a subfamily from a family of priors such that the mean of the prior distribution is equal to 0 θ .…”

Section: Introductionmentioning

confidence: 99%

Bayes Estimators of Exponential Parameters from a Censored Sample Using a Guessed Estimate

Singh

et al. 2008

Data Sci. J.

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INTRODUCTIONIn life testing experiments, experimenters often possess certain information about a parameter of interest, through past experience or familiarity with the experiment. The most common type of information is a probable value of the parameterθ , say 0 θ . This 0 θ has been referred in statistical literature as the point guess aboutθ .The use of the point guess for inferences regarding a parameter has been considered by many authors. Perhaps the most popular technique that utilizes the knowledge of point guess is the shrinkage technique, originally suggested by Thompson (1968). He suggested shrinking the usual estimator suggested by Pandey and Srivastava (1985) and Pandey and Upadhyay (1988). Their technique chooses a subfamily from a family of priors such that the mean of the prior distribution is equal to 0 θ . Equalization of the mean to 0 θ , however, does not seem appropriate unless the prior guess 0 θ is specified as the average value of θ . As mentioned above, prior to the sample selection, the experimenter believes that the point guess 0 θ is a possible value of the parameter. Thus, it seems more appropriate to interpret 0 θ as the most probable value rather than interpreting it as the average value of θ . Therefore, we propose that the choice of subfamily of the prior be made by equalizing the mode of the prior distribution to 0 θ . As an illustration of our proposition, we have considered the problem of estimation of the failure rate and reliability for a one-parameter exponential distribution.The exponential distribution is a widely used model in a variety of statistical procedures. Among its most prominent applications are those in the field of life testing and reliability problems.

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