Modeling correlated ordinal response data is usually more complex than the case of continuous and binary responses. Existing literature lacks an appropriate approach to modeling such data. For small sample sizes, however) these models lose their appeal since their inferences arc based OIl large samples. In this work, the Bayesian analysis of an asymmetric bivariate ordinal LATENT variable model has been developed. The LATENT response variable has been chosen to follow the generalized bivariate Gumble distribution. Using some specific priors and MCMC algorithms the regression parameters were estimated. As an application, a data set concerning Diabetic Retinopathy in 116 patients have been analyzed. This data set includes the disease status of each eye for patients as an ordinal response and a number of explanatory VARIABLES some of which are common to both eyes and the rest are organ specific.

Trips are an inseparable aspect of our lives, and nowadays their purposes have been changed and their number has increased. Most of the trips are done by cars, which make lots of harmful influences on our environment (such as pollution, global warming, and lack of energy sources), and they also bring about negative impacts on the economy and society. In order to have sustainable development, transportation managers should make long-term investments and policies on public transportation and active travel modes. In view of that, in addition to socio-economic VARIABLES and attributes of trips, we investigate the effect of LATENT psychological VARIABLES such as attitude, perceived behavioral control, subjective norms, and intention on escorting elementary school trips by employed parents. For this purpose, we analyzed and modeled the data of 4000 questionnaires filled by the parents of students of Tehran’ s schools. Results obtained from estimating and calibrating the ordered and multinomial Logit models, show that the VARIABLES of perceived distance to school, children age, frequency of using the car to school, similarity between start time, and route travel in work and educational trips, intention, perceived behavioral control, existence of a proper person in the family for escorting children, suitable evaluation of one other person in family by parent and choosing public mode in case of similarity between start time, and route travel in work and educational trips in both models were statistically significant. Therefore, we can increase motivation by carrying out programs such as: explaining the importance of environmental and traffic issues, encouraging students and parents to do Supervisional Walking Bus (SWB), building residential settlements near parents’ office and students school, creating safe routes for walking and cycling and increasing the safety of neighborhoods; these programs can increase the probability of using active modes by students.

Today, the development of urbanization and the trend of increasing population in cities and the growing dependence of citizens on private cars, has caused many problems such as congestion increased accidents and environmental pollution. One of the most basic proposed solutions to the problem of congestion and air pollution in the world is the use of city trains as a means of public transport. In order to enjoy the benefits of this travel method as much as possible, it is necessary to know the factors influencing its choice. In the present study, from the theory of behavioral inclination and using the information of 260 questionnaires distributed among users of Tehran city train line 2 (Sadeghieh-Farhangsara) and applying the method of structural equations to identify hidden factors affecting the choice of transportation system Tehran city rail is launched. The results indicate the positive effect of four LATENT VARIABLES of service quality, perceived value, engagement, and satisfaction on behavioral desire. This means that with the increase of each of these VARIABLES, the behavioral tendency of people to use line 2 of the Tehran city train increases. Among the mentioned VARIABLES, the conflict has the most, and perceived value has the least effect on behavioral tendency. The results of this study can be used by transportation policymakers to encourage more people to use the city train and enjoy its benefits.

Statistical monitoring of dynamic networks is a major topic of interest in complex social systems. Many researches have been conducted on modeling and monitoring dynamic social networks. This article proposes a new methodology for modeling and monitoring dynamic social networks for quick detection of temporal anomalies in network structures using LATENT VARIABLES. The key idea behind our proposed methodology is to determine the importance of LATENT VARIABLES in creating edges between nodes as well as observed covariates. First, LATENT space model (LSM) is used to model dynamic networks. Vector of parameters in LSM model are monitored through multivariate control charts in order to detect changes in different network sizes. Experiments on simulated social network monitoring demonstrate that our surveillance monitoring strategy can effectively detect abrupt changes between actors in dynamic networks using LATENT VARIABLES.

Spatial generalized linear mixed models are usually used for modeling non-Gaussian and discrete spatial responses. In these models, spatial correlation of the data can be considered via LATENT VARIABLES. Estimation of the LATENT VARIABLES at the sampled locations, the model parameters and the prediction of the LATENT VARIABLES at un-sampled locations are of the most important interest in SGLMM. Often the normal assumption for LATENT VARIABLES is considered just for convenient in practice. Although this assumption simplifies the calculations, in practice, it is not necessarily true or possible to be tested. In this paper, a closed skew normal distribution is proposed for the spatial LATENT VARIABLES. This distribution includes the normal distribution and also remains closed under linear conditioning and marginalization. In these models, likelihood function cannot usually be given in a closed form and maximum likelihood estimations may be computationally prohibitive. In this paper, for maximum likelihood estimation of the model parameters and predictions of LATENT VARIABLES, an approximate algorithm is introduced that is faster than the former method. The performance of the proposed model and algorithm are illustrated through a simulation study.

Background: The integrated motivational-volitional (IMV) model is the second theory based on the ideation-to-action framework. Objectives: The current study aimed to investigate the motivational phase of this model in the Iranian population. In this study, threat-to-self moderators are included cognitive emotion regulation strategies. Also, thwarted belongingness and perceived burdensomeness are considered motivational moderators. MaterialsandMethods: Atotal of 405 participants (68. 6% female,meanage: 22. 7 years) filled out several self-report questionnaires, including the Defeat Scale, Entrapment Scale, Beck Scale for Suicidal Ideation, Cognitive Emotion Regulation Questionnaire-short, Interpersonal Needs Questionnaire-15. To assess the IMV model, structural equation modeling with the interaction of LATENT VARIABLES was performed. Results: The results indicated that the overall model’, s fit was poor. Although the model explained 70% and 61% of the variance in entrapment and suicidal ideation, respectively, the pathway between entrapment and suicidal ideation was not statistically significant. The findings demonstrated that the most effective predictors of suicidal ideation were perceived burdensomeness and thwarted belongingness. Conclusions: The results add to our knowledge of what constructs are more critical in the emergence of suicidal ideation. It is hoped that the study findings will lead to a greater interest in this field of research in the future.

Removing noise from hyperspectral images is an inevitable step to improve the quality of these types of images. Many methods have been proposed by researchers in this field. Most of these methods do not address simultaneous spatial-spectral similarities. When the noise removal method applies data globally without regard to spatial-spectral similarities, it usually has a negative effect on low-level pixels; when in the spectral data, a large number of pixels have little noise and a small number of pixels are destroyed by the high level of noise. In this paper, we first extract spatial-spectral similarities in images by defining cluster-based LATENT VARIABLES. In the following, a low-rank matrix factorization method based on these LATENT VARIABLES is proposed to eliminate the noise of hyperspectral images and to improve the resistance to noise (as compared to other methods). The performance of the proposed method is compared visually with six new methods on real noise-contaminated images. For quantitative comparison, the same experiments are done on clean images combined with six types of simulated noise. The simulation results show that by applying LATENT VARIABLES in the Bayesian inference framework, the performance of the noise removal method is improved and the proposed method performs better than the other methods.

The spatial generalized linear mixed models are often used, where the LATENT VARIABLES representing spatial correlations are modeled through a Gaussian random field to model the categorical spatial data. The violation of the Gaussian assumption affects the accuracy of predictions and parameter estimates in these models. In this paper, the spatial generalized linear mixed models are fitted and analyzed by utilizing a stationary skew Gaussian random field and employing an approximate Bayesian approach. The performance of the model and the approximate Bayesian approach is examined through a simulation example, and implementation on an actual data set is presented.