Likelihood ratio test for a piecewise continuous Weibull model with an unknown change point

In this paper, we consider the likelihood ratio test for the scale and shape parameters in a piecewise continuous Weibull model with an unknown change point. Under the null hypothesis of no change in scale and shape parameters, we derive that the likelihood ratio process converges weakly to the squared Euclidian norm of a weighted mean zero Gaussian vector process.     

A test for self-exciting clustering mechanism

In this paper, we propose a test to distinguish between data with cluster pattern where the variables are dependent in a self-exciting fashion versus independently identically distributed random variables. We also developed asymptotic distribution of the test statistic with closed-form covariance structure. Comparisons with scan statistics are discussed in the context of simulated earthquake data. Applications to two data sets are discussed.     

Comparison of the cuscore, GLRT and cusum control charts for detecting a dynamic mean change

Although statistical process control (SPC) techniques have been focused mostly on detecting constant mean shifts, dynamic and time-varying process changes frequently occur in the monitoring of feedback-controlled and autocorrelated processes. In this research, the performances of cumulative score (Cuscore), generalized likelihood ratio test (GLRT), and cumulative sum (CUSUM) charts in detecting a dynamic mean change that finally approaches a steady-state value are compared. Theoretical results in average run length (ARL) comparison are provided. From the theretical study we find that, when the steady-state value is greater or less than a critical value,Rδ/2+δ/2, the Cuscore and CUSUM charts have a different performance in detecting the mean change. We prove also that the GLRT has the best performance among the three charts in detecting any mean change for which the steady-state value is not equal to δ or δR, when the in-control ARL is large.     

On a Test Statistic for Linear Trend

Let {W(s)} _s 0 be a standard Wiener process. The supremum of the squared Euclidian norm Y (t)², of the R²-valued process Y(t)=(1/t W(t), {12/t ³ int_{0 t} s dW (s)– {3/t} W(t)), t [, 1], is the asymptotic, large sample distribution, of a test statistic for a change point detection problem, of appearance of linear trend. We determine the asymptotic behavior P {sup _t [, 1] Y(t)² > u as u , of this statistic, for a fixed (0,1), and for a moving = (u) 0 at a suitable rate as u . The statistical interest of our results lie in their use as approximate test levels.