Suppose that we have a random sample from one-parameter Rayleigh distribution‎. ‎In classical methods‎, ‎we estimate the interesting parameter based on the sample information and‎ ‎with usual estimators‎. ‎Sometimes in practice‎, ‎the researcher has some information about the unknown‎ ‎parameter in the form of a guess value‎. ‎This guess is known as nonsample information‎. ‎In this case‎, ‎linear shrinkage estimators are introduced‎ ‎by combining nonsample and sample information which have smaller risk than usual estimators in the vicinity of‎ ‎guess and true value‎. ‎In this paper‎, ‎some shrinkage testimators are introduced using different methods based on‎ ‎vicinity of guess value and true parameter and their risks are computed under the entropy loss function‎. ‎Then‎, ‎the performance of‎ ‎shrinkage testimators and the best linear estimator is calculated via the relative efficiency of them‎. ‎Therefore‎, ‎the results are applied for the type-II censored data.