Statistical models are useful in explaining and forecasting real-world occurrences. Various extended distributions have been widely employed for modeling data in a variety of fields throughout the last few decades. In this article we introduce a new extension of the Kumaraswamy exponential (KE) model called the Kavya–Manoharan KE $\left(KMKE\right)$ distribution. Some statistical and computational features of the $KMKE$ distribution including the quantile ($QUA$) function, moments ($M{O}_m$s), incomplete $M{O}_m$s ($INM{O}_m$s), conditional $M{O}_m$s ($COM{O}_m$s) and $M{O}_m$ generating functions are computed. Classical maximum likelihood and Bayesian estimation approaches are employed to estimate the parameters of the KMKE model. The simulation experiment examines the accuracy of the model parameters by employing Bayesian and maximum likelihood estimation methods. We utilize two real datasets related to food chain data in this work to demonstrate the importance and flexibility of the proposed model. The new KMKE proposed distribution is very flexible, more so than numerous well-known distributions.

In this article, we introduce a new extension of the power Lomax (PLo) model by combining the type II exponentiated half-logistic class of statistical models and the PLo model. The new suggested statistical model called type II exponentiated half-logistic-PLo (TIIEHL-PLo) model. However, the new TIIEHL-PLo model is more flexible and applicable than the PLo model and some extensions of THE PLo model, especially those in environmental and medical fields. Some general statistical properties of the TIIEHL-PLo model are computed. Six different estimation approaches, namely maximum likelihood (ML), least-square (LS), weighted least-squares (WLS), maximum product spacing (MPS), Cramér–von Mises (CVM), and Anderson–Darling (AD) estimation approaches, are utilized to estimate the parameters of the TIIEHL-PLo model. The simulation experiment examines the accuracy of the model parameters by employing six different methodologies of estimation. In this study, we analyze three real datasets from the environmental and medical fields to highlight the relevance and adaptability of the proposed approach. The newly suggested model is exceptionally adaptable and outperforms several well-known statistical models.

This study investigates the evolving causal relationship between government revenue and expenditure in Algeria from 1980 to 2022, a period marked by significant fiscal policy shifts. Unlike previous research assuming a static relationship, we employ a bootstrap rolling window approach and spectral Granger-causality test to capture time-varying causality. These methods illuminate the dynamic nature of fiscal interactions in Algeria's changing economic landscape. Our findings robustly support the tax-spend hypothesis over extended periods, revealing that government revenues significantly influence expenditure patterns. Notably, from 1995 to 2003, we observe a positive effect on spending, coinciding with fiscal contraction and rationalization efforts. Conversely, despite declining tax revenues from 2005 to 2008, an expansionary fiscal policy increased spending, indicative of financial illusion. Interestingly, we also find intermittent short-term evidence supporting both the spend-tax and fiscal synchronization hypotheses, highlighting the complex and multifaceted nature of Algeria's fiscal dynamics. The spectral Granger-causality tests further corroborate these findings, particularly in the medium term. This study contributes to the literature on fiscal policy dynamics in resource-dependent economies and offers valuable insights for policymakers navigating Algeria's complex fiscal landscape. Our results underscore the importance of adaptive fiscal strategies that account for the time-varying nature of revenue-expenditure relationships, particularly in the face of oil price volatility and economic diversification efforts.

This study measuring impact of human capital empowerment (HCE) contributes improve financial performance sustainability (FPS) in banking sector. The study focused on testing two hypotheses about human capital empowerment (HCE) contributor's and improving financial performance in banking sector. Additionally, contribution of human capital empowerment (HCE) and improving financial performance sustainability (FPS) of Faisal Islamic Bank of Sudan. Study used inductive and deductive techniques to identified problem nature, study dimensions and examine study hypotheses by historical and descriptive analytical models to analyzing study data. Study data was analyzed by (SPSS) and (AMOS) packages by one-sample T-test and analysis of variance test. Unilateral, study reached a set of results, most important of: a significant difference between human capital empowerment (HCE) contributor's and financial performance sustainability (FPS) improvement in banking sector.