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Showing 2 results for Likelihood Ratio Test
Modeling and Monitoring Social Ntwork in term of Longitudinal Data
Ebrahim Mazrae Farahani, Reza Baradaran Kazemzade, Amir Albadvi, Babak Teimourpour,
Volume 29, Issue 3 (9-2018)
Abstract
Studying the social networks plays a significant role in everyone’s life. Recent studies show the importance and increasing interests in the subject by modeling and monitoring the communications between the network members as longitudinal data. Typically, the tendency for modeling the social networks with considering the dependency of an outcome variable on the covariates is growing recently. However, these studies fail in considering the possible correlation between the responses in the modeling of social networks. Our study use generalized linear mixed models (GLMMs) (also referred to as random effects models) to model the social network according to the attributes of nodes in which the nodes take a role of random effect or hidden effect in the modeling. The likelihood ratio test (LRT) statistics is implemented to detect change points in the simulated network streams. Also, in the simulation studies, we applied root mean square Error (RMSE) and standard deviation criteria for choosing an appropriate distribution for simulation data. Also, our simulation studies demonstrates an improvement in the average run length (ARL) index in comparison to the previous studies.
 
Phase I Monitoring of Multivariate Ordinal Based Processes: The MR and LRT Approaches (A Real Case Study in Drug Dissolution Process)
Ahmad Hakimi, Hiwa Farughi, Amirhossein Amiri, Jamal Arkat,
Volume 33, Issue 1 (3-2022)
Abstract 
In some statistical processes monitoring (SPM) applications, relationship between two or more ordinal factors is shown by an ordinal contingency table (OCT) and it is described by the ordinal Log-linear model (OLLM). Newton-Raphson algorithm methods have also been used to estimate the parameters of the log-linear model. In this paper, the OLLM based processes is monitored using MR and likelihood ratio test (LRT) approaches in Phase I. Some simulation studies are applied to performance evaluation of the proposed approaches in terms of probability of signal under step shifts, drifts and the presence of outliers. Results show that, by imposing the small and moderate shifts in the ordinal log-linear model parameters, the MR statistic has better performance than LRT. In addition, a real case study in dissolution testing in pharmaceutical industry is employed to show the application of the proposed control charts in Phase I. 

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