Date of Award:

5-1975

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Department name when degree awarded

Applied Statistics and Computer Science

Committee Chair(s)

David White

Committee

David White

Committee

Rex L. Hurst

Committee

Donald H. Cooley

Abstract

The development of sequential analysis has led to the proposal of tests that are more economical in that the Average Sample Number (A. S. N.) of the sequential test is smaller than the sample size of the fixed sample test. Although these tests usually have a smaller A. S. N. than the equivelent fixed sample procedure, there still remains the possibility that an extremely large sample size will be necessary to make a decision. To remedy this, truncated sequential tests have been developed.

A method of truncation for testing a composite hypotheses is studied. This method is formed by mixing a fixed sample test and a sequential test and is applied to the exponential distribution and normal distribution to establish its usefulness.

It is proved that our truncation method can give a similar Operating Characteristic (O. C.) curve to that of corresponding fixed sample test if the test parameters are properly chosen. The average sample size required by our truncation method as compared with other existing truncation methods gives us a satisfactory result. Though the truncation method we suggested in this study is not an optimum truncation, it is still worthwhile, especially, when we are interested in the testing of a composite hypotheses.

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3b21e387d2623594f22ba0daee7c3827

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