PRELIMINARY TEST ESTIMATORS FOR THE SCALE PARAMETER OF POWER FUNCTION DISTRIBUTION
Keywords:
Power Function Distribution, Preliminary Test Estimators, Mean Square Error, Relative Efficiency.Abstract
In this paper some preliminary test estimators have been considered for estimating the scale parameter of Power function distribution when a point guess value about scale parameter is available. The shape parameter is assumed to be known. Comparisons with usual estimators in terms of relative efficiency have been made.
Downloads
References
Davis, R. L. and Arnold, J. C. (1970). An efficient preliminary test estimator for the variance
of normal population when mean is unknown, Biometrika, 56, p. 674- 676
Mehta, J.S. and Srinivasan R. (1971). Estimation of mean by shrinkage to a point, Jour. Amer.
Statist. Assoc., 66, p. 86-90.
Pandey, B. N. (1979 a). On shrinkage estimation of Normal population variance.
Communications in Statistics -Theory and Methods, 8, p. 359-365.
Pandey, B.N., Malik, H.J. and Srivastava, R. (1988). Shrinkage estimator for the variance of a
normal distributions at single and double stages, Microelectron Reliability, 28 (6), p.929-944.
Prakash,G., Singh,D.C., and Singh R.D. (2006). Some test estimator for the scale parameter of
classical Pareto distribution. Journals of Statistical Research, 40(2), p. 41- 54.
Singh, D.C., Prakash, G., and Singh P. (2007). Shrinkage testimators for the shape parameter
of Pareto distribution using LINEX loss function. Communication in statistics – Theory and
methods, 36(4), p. 741- 753.
Singh, D.C., Singh, P. and Singh, P.R. (1996), Shrunken estimators for the scale parameter of
Classical Pareto Distribution, Microelectron Reliability, 36 (3), p. 435-439.
Singh, H.P., and Shukla, S.K. (2000). Estimation in the two parameter Weibull Distribution
with prior Information, IAPQR Transactions, 25(2), p. 107- 118.
Saleh, A. K. E. (2006). Theory of Preliminary Test and Stein – type Estimators with
Application, Wiley and Sons, New York.
Thompson, J. R. (1968 a). Some shrinkage techniques for estimating the mean. Journal of the
American Statistical Association, 63, p. 113- 122