Abstract: Identification of representative regimes of wave height and direction under different wind conditions is complicated by issues that relate to the specification of the joint distribution of variables that are defined on linear and circular supports and the occurrence of missing values. We take a latent-class approach and jointly model wave and wind data by a finite mixture of conditionally independent Gamma and von Mises distributions. Maximum-likelihood estimates of parameters are obtained by exploiting a suitable EM algorithm that allows for missing data. The proposed model is validated on hourly marine data obtained from a buoy and two tide gauges in the Adriatic Sea.
Abstract: Hyperplane fitting factor rotations perform better than conventional rotations in attaining simple structure for complex configurations. Hyperplane rotations are reviewed and then compared using familiar exam es from the literature selected to vary in complexity. Included is a new method for fitting hyperplanes, hypermax, which updates the work of Horst (1941) and Derflinger and Kaiser (1989). Hypercon, a method for confirmatory target rotation, is a natural extension. These performed very well when compared with selected hyperplane and conventional rotations. The concluding sections consider the pros and cons of each method.
Factor analysis (FA) is the most commonly used pattern recognition methodology in social and health research. A technique that may help to better retrieve true information from FA is the rotation of the information axes. The purpose of this study was to evaluate whether the selection of rotation type affects the repeatability of the patterns derived from FA, under various scenarios of random error introduced, based on simulated data from the Standard Normal distribution. It was observed that when applying promax non - orthogonal rotation, the results were more repeatable as compared to the orthogonal rotation, irrespective of the level of random error introduced in the model.
Abstract: Mixture of Weibull distributions has wide application in modeling of heterogeneous data sets. The parameter estimation is one of the most important problems related to mixture of Weibull distributions. In this pa per, we propose a L-moment estimation method for mixture of two Weibull distributions. The proposed method is compared with maximum likelihood estimation (MLE) method according to the bias, the mean absolute error, the mean total error and completion time of the algorithm (time) by sim ulation study. Also, applications to real data sets are given to show the flexibility and potentiality of the proposed estimation method. The com parison shows that, the proposed method is better than MLE method.