What is point estimation - Definition and Meaning

Point Estimation :

Point estimation is the procedure of defining a single estimated value of the parameter of given population.


Formula :

MLE = S / T Laplace = (S+1) / (T+2) Jeffrey = (S+0.5) / (T+1) Wilson = (S+(z2/2)) / (T+z2) Where, MLE = Maximum Likelihood Estimation S = Number of Success T = Number of Trials z = Z-Critical Value Best Point Estimation Rules : 1. MLE<=0.5 --> Wilson Estimation 2. Between MLE>0.5 and MLE<0.9 --> MLE 3. MLE>0.9 --> either Laplace or Jeffrey based on which is small

Example :

Success (S) = 4 Trials (T) = 9 Confidence Interval (P) = 99% MLE = 4/9 = 0.4444 Laplace = 5/11 = 0.4545 Jeffery = 4.5/10 = 0.45 z = from z score table of 99% level = 2.5758 Wilson = (4+(2.57582/2)) / (9+2.57582) = 0.468 Best Point: MLE<0.5 so, best point = Wilson Estimation = 0.468

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