Suchergebnisse

The relationships among self-esteem, self-efficacy, and faith in people in Chinese heroin abusers were investigated. We used 3 rating scales to survey a sample of 200 heroin abusers: the Self-esteem Scale (Rosenberg, 1965), the Self-efficacy Questionnaire for Chinese Drug Users (Geng
& Han, 2008), and the Faith in People Scale (Rosenberg, 1956). A correlation analysis and a regression analysis were employed to analyze the data. The correlations among self-esteem, self-efficacy, and faith in people were significantly positive. Self-efficacy and faith in people results
were found to be predictive of the self-esteem level of participants at a statistically significant level.

Using the structural approach to derive tax-adjusted duration for defaultable bonds under stochastic interest rate process, we thoroughly investigate the effects of personal taxes, default risk, and their interaction on bond duration. The simulation results show that default risk reduces duration, while personal taxes increase duration. Premium amortization and discount accretion further enhance the positive impact of personal taxes on duration. The interactive effect of default risk and personal taxes on duration depends on which effect dominates. Also, the tax effect on duration changes with bond features, bond issuers' debt policy, and interest rate level. Our empirical results validate the positive tax effect on duration by showing that duration of corporate bonds is significantly longer than that of municipal counterparts with the same credit rating, coupon rate, and maturity. Our study provides timely information on how to accurately measure interest rate risk under the current circumstance that interest rate is increasing substantially from the low level and personal taxes are expected to increase.

AbstractThe analysis of network data has become an increasingly prominent and demanding field across multiple research fields including data science, health, and social sciences, requiring the development of robust models and efficient computational methods. One well-established and widely employed modeling approach for network data is the Exponential Random Graph Model (ERGM). Despite its popularity, there is a recognized necessity for further advancements to enhance its flexibility and variable selection capabilities. To address this need, we propose a novel hierarchical Bayesian adaptive lasso model (BALERGM), which builds upon the foundations of the ERGM. The BALERGM leverages the strengths of the ERGM and incorporates the flexible adaptive lasso technique, thereby facilitating effective variable selection and tackling the inherent challenges posed by high-dimensional network data. The model improvements have been assessed through the analysis of simulated data, as well as two authentic datasets. These datasets encompassed friendship networks and a respondent-driven sampling dataset on active and healthy lifestyle awareness programs.