First-order multivariate integer-valued autoregressive model with multivariate mixture distributions

The univariate integer-valued time series has been extensively studied, but literature on multivariate integer-valued time series models is quite limited and the complex correlation structure among the multivariate integer-valued time series is barely discussed. In this study, we proposed a first-order multivariate integer-valued autoregressive model to characterize the correlation among multivariate integer-valued time series with higher flexibility. Under the general conditions, we established the stationarity and ergodicity of the proposed model. With the proposed method, we discussed the models with multivariate Poisson-lognormal distribution and multivariate geometric-logitnormal distribution and the corresponding properties. The estimation method based on EM algorithm was developed for the model parameters and extensive simulation studies were performed to evaluate the effectiveness of proposed estimation method. Finally, a real crime data was analyzed to demonstrate the advantage of the proposed model with comparison to the other models

The growth of non-c-axis-oriented ferroelectric BLT thin films on silicon using ZnO buffer layer

Lanthanum-doped bismuth titanate (BLT) thin films were grown on buffered Si substrates using a RF magnetron sputtering system. Electrically conducting ZnO layers were used as an effective buffer layer to facilitate the growth of the ferroelectric thin films. X-ray diffraction data shows the Aurivilius phase structure with the highest diffraction peak (1 1 7), indicating non-c-axis-oriented microstructure. Random oriented plate-like grains were observed using scanning electron microscopy. The ferroelectric nature of the film was proved by ferroelectric domain switching under an electrical field