Depression among Indian university students and its association with perceived university academic environment, living arrangements and personal issues.

The aim of the study is to ascertain the level of depression among university students across gender, academic stream, semesters, perception of family environment and relationship with parents, academic performance, and family income. In addition, the study examines the association between students' perceived university academic environment, living arrangements, personal issues, and depression. Seven hypotheses were formulated for verification. A total of 717 students were recruited following the multistage cluster sampling method, and data were collected by a specially designed structured questionnaire, academic achievement record and a standardized University Students Depression Inventory. Findings disclosed that 37.7%, 13.1%, and 2.4% of the students were suffering from moderate, severe, and extremely severe depression. A significant difference was found across semester, that is, semester II students reported a higher level of depression than semester III students. So far as academic stream is concerned, students from humanities and social science were found to be suffering from more depression compared to students from science and management streams. The study further disclosed that the students who reported positive views about the university academic environment and living arrangements had lower level of depression compared to their counterparts. Personal resilience's such as being able to sharing personal problems with others and doing regular exercise were found to be associated with positive mental health. The findings of the study emphasize the need for immediate mental health support services for about 15.6% of the students who were either suffering from severe or extremely severe depression at the University.

Binary classification using multivariate receiver operating characteristic curve for continuous data.

The classification scenario needs handling of more than one biomarker. The main objective of the work is to propose a multivariate receiver operating characteristic (MROC) model which linearly combines the markers to classify them into one of the two groups and also to determine an optimal cut point. Simulation studies are conducted for four sets of mean vectors and covariance matrices and a real dataset is also used to demonstrate the proposed model. Linear and quadratic discriminant analysis has also been applied to the above datasets in order to explain the ease of the proposed model. Bootstrapped estimates of the parameters of the ROC curve are also estimated.