A sequential likelihood ratio test for general hypotheses

In statistical inference it is often desired to test a specified funcrion of unkown parameters form an underlying distribution. Sequential procedures utilize information from the already collected observations and allow for a possible eariy termination of experimentation with a concurrent savings in time and cost. In the present work a suitable maximum-likelihood based sequential testing procedure for functions of unknown parameters is developed for independent and identically distributed observations of an underlying distribution of known form. The theoretical Operating Characteristic (OC) and Average Sample Number (ASN) functions are derived for local alternatives by approximating the distribution of the test statistic with linear combinations of the standard Wiener process, Simlriation studies were utilized to investigate the goodness of the asymptotic results in finite samples.