Abstract. The purpose of this research is to propose a new method for detecting change points with an epidemic alternative (in the form of a step function). There are several parametric approaches and nonparametric approaches in the literature that can be used for detecting change-points in epidemic models. Yan [16] summarized some existing parametric approaches. The approaches summarized in Yao's paper are based on the assumption of known population variances. The proposed test statistic in this research does not depend on the assumption of known population variances. This better fits the real world situation. Monte-Carlo simulation was used to find the critical values of the test. The power study was also based on Monte-Carlo simulation. The simulation result shows that the test statistic proposed in this research provides quite decent power compared with other existing statistical procedures, especially for the case that the step is large and duration of the epidemic is long. In 1997, a likelihood ratio test was proposed by Csorgo and Horvath Keywords: Change-point, epidemic alternative, power comparison, Monte Carlo simulation, unknown variances. Introduction The problem of detecting changes in the characteristics of a random process is referred to as the changepoint problem. This problem has become a fast developing research area in statistics mainly due to its important applications and newly developed theoretical and computational methods. Change-point problems occur in a wide variety of fields including detecting shifts in production processes, comparing and matching DNA sequences, examining the impact of social programs, and studying structural shifts in one or more parameters of the models in economics, engineering, political science and other empirical sciences. Change-points with epidemic alternatives were formulated by Levin and Kline [11] to model the changes over time in the proportion of abortions. In the epidemic change model, the random process is assumed to be stable initially, and then at an unknown time point it will exhibit an abrupt change in the characteristics, which will continue for an unknown duration before stabilizing again to the initial state. The standard normal of a neuron exhibiting a modulated activity during a time period and then reverting to its spontaneous activity is an example of this model, which was described by Commenges, et al [12]. Bromeling and Tsurumi [13] described a number of applications of this model in econometrics. Later, Siegmun
