Parameter Estimations in a Bernoulli Process

다국어 초록 (Multilingual Abstract)

In this paper, we study the parameter estimation in Bernoulli distribution and Geometric distribution with parameter . We consider the estimator of and we find two confidence intervals for the parameters in these distributions. One is the result of variance stabilization(V. S.) and the other is that of using central limit theorem(C. L. T.). Simulation results shows that the point estimates are not so different and the length of the confidence intervals obtained by V. S. in case of geometric distribution is smaller than that by C. L. T.

참고문헌 (Reference)

1 Kang, "The probability range of the confidence intervals for sigma_A ^2 /sigma_B ^2 in two factor nested variance components model" 9 (9): 159-164, 1998

2 KangK. J., "The confidence Intervals for the Variances Ratio" 6( (6(): 983-993, 2004

3 Hogg, "Introduction to mathematical statistics" John Wiley and Sons Inc 1995

4 Cho, "Confidence Intervals using C. L. T. and Delta method, Journal of the Korean Data Analysis Society" 5 (5): 453-458, 2003

5 Kang K. J, "Confidence Intervals for the Linear Combination of Variances" 5 (5): 459-466, 2003

6 Serfling R, "Approximation theorems of mathematical statistics" John Wiley and Sons Inc 1980

7 Ross, "A first course in probability" Prentice-Hall Inc 1994

8 Ferguson T. S, "A Course in large sample theory" Chapman & Hall. 1996