In this paper, we propose efficient Bayesian methods to analyze longitudinal ordinal data with missing values using multivariate probit models. Longitudinal ordinal data with substantial missing values are ubiquitous in many scientific fields. Specifically, we develop the Markov chain Monte Carlo (MCMC) sampling methods based on the non-identifiable multivariate probit models and further compare their performance with the one based on the identifiable multivariate probit models. We carried out our investigation through simulation studies, which show that the proposed methods can handle substantial missing values and the method with marginalizing the redundant parameters based on the non-identifiable model outperforms the others in the mixing and convergences of the MCMC sampling components. We then present an application using data from the Russia Longitudinal Monitoring Survey-Higher School of Economics (RLMS-HSE).
In this study we assess the extent to which teaching and learning standards meet the criteria set by the education evaluation committee in Jordan. The viewpoints of students and teachers at Yarmouk University were considered. To fulfill the investigation's aims, the researcher employed a descriptive study design and utilized a quantitative technique of three domains: (attributes about graduates and educational results, curriculum, and the quality of instruction and student assessment), using the questionnaire as the primary instrument for data collection. The research involved a sample of (501) students and faculty members, comprising both male and female individuals from Yarmouk University. The participants were chosen through a random technique. The study utilized an independent sample t-test to assess the statistical significance of the effects of gender and participation position on the criteria for learning and teaching. The study's findings indicate that several elements, such as compliance with graduate qualities, educational outcomes, curriculum design, and the quality of instruction and assessment standards, influence the standards of teaching and learning. The results demonstrate that there is no statistically significant variation in the levels of teaching and learning standards when accounting for gender and participation position factors.
Model fitting and risk estimation are an important everyday aspect of a successful financial institution. Consequently, in this paper, we discuss risk quantification of the South African Industrial Index (also known as J520) using 22 standard light- and heavy-tailed statistical distributions. Given the importance of the J520 index (since it has the highest market capitalization in the Johannesburg Stock Exchange), investors may have a very keen interest in fully understanding the loss and gain returns characteristics and underlying statistical distribution’s properties, including tail properties. Thus, an in-depth goodness-of-fit evaluation is conducted by assessing six different tests (i.e. Kolmogorov-Smirnov, Anderson- Darling, Cramer von Mises, negative log-likelihood, Akaike information criterion, Bayesian information criterion) as well as two risk measures (i.e., value-at-risk and tail value-at-risk) are computed and interpreted within the context of J520 index. It is observed that the best distribution to fit to loss returns are the inverse Burr or transformed beta while for the gain returns it is either transformed gamma, inverse Burr or generalized beta distributions. The latter distributions strike a better balance with respect to excellent goodness-of-fit and risk measures that are very close to the corresponding ones for the empirical distribution. Given our findings, it may not be advisable for investors to hold very long positions in the J520 index since loss returns have much higher leptokurtic and heavy-tailed as compared to the lighter-tailed gain returns. Therefore, the growth shares observed over the long term indicate that a more prudent strategy would be to consider shorting the index. By doing so, investors could better align their strategies with the highly likely potential for substantial drawdowns inherent in this market.
Financial forecasting using deep knowledge and linear regression methods is explored in this study, which looks at the association between financial metrics and bank presentation from 2003 to 2012. By analyzing a complete dataset, the research identifies macroeconomic variables such as interest rates and rise, together with crucial success drivers such as GDP growth, leverage, and liquidness ratios. We can investigate linear and non-linear connections by mixing several mechanism learning models, such as ARIMA and Random Woodland. However, different banks' risk profiles and strategic prospects can be better understood through situation analysis and PCA-based clustering. The findings prove that rising GDP is a robust measure of economic wealth, but inflation cuts into pays. Institutions that are vulnerable to interest rate variations benefit from interest rates. Gathering shows that various banks adopt separate financial strategies, and scenario analysis shows that monetary results are very sensitive to changes in influence. By faithfully depicting financial doubt, the proposed models demonstrate pliability even during economically unstable times like the 2008 monetary crisis. This research emphasizes the importance of proactive risk organization and specialized forecasting tactics; upcoming studies should use hybrid modeling methodologies and join more macroeconomic variables. In the ever-changing world of finance, these advances aim to improve the correctness of predictions and planned decision-making.
In this paper, we explore the impact of price misrepresentation on pre-emption cases. It examines the legal and practical challenges faced when claiming recovery based on inaccurate prices and discusses the appropriate legal means to challenge forgery. The findings highlight the pre-emptor's right to challenge price falsehood in cases of increased prices and the absence of a specific legal method for filing an appeal. The study recommends clarifying the requirements for challenging price falsity and providing acceptable forms of evidence. Finally, it contributes to a better understanding of the topic and suggests improvements in the process.
