Shrinkage Testimation in Rayleigh Distribution and it's Application in Type-II Censored Data

Suppose that we have a random sample from one-parameter Rayleigh distributioný. ýIn classical methodsý, ýwe estimate the interesting parameter based on the sample information andý ýwith usual estimatorsý. ýSometimes in practiceý, ýthe researcher has some information about the unknowný ýparameter in the form of a guess valueý. ýThis guess is known as nonsample informationý. ýIn this caseý, ýlinear shrinkage estimators are introducedý ýby combining nonsample and sample information which have smaller risk than usual estimators in the vicinity ofý ýguess and true valueý. ýIn this paperý, ýsome shrinkage testimators are introduced using different methods based oný ývicinity of guess value and true parameter and their risks are computed under the entropy loss functioný. ýThený, ýthe performance ofý ýshrinkage testimators and the best linear estimator is calculated via the relative efficiency of themý. ýThereforeý, ýthe results are applied for the type-II censored data.