In this study, two new distributions are developed by compounding Sine-Weibull and zero-truncated geometric distributions. The quantile and ordinary moments of the distributions are obtained. Plots of the hazard rate functions of the distributions show that the distributions exhibit nonmonotonic failure rates. Also, plots of the densities of the distributions show that they exhibit decreasing, skewed, and approximately symmetric shapes, among others. Mixture and nonmixture cure rate models based on these distributions are also developed. The estimators of the parameters of the cure rate models are shown to be consistent via simulation studies. Covariates are introduced into the cure rate models via the logit link function. Finally, the performance of the distributions and the cure rate and regression models is demonstrated using real datasets. The results show that the developed distributions can serve as alternatives to existing models for survival data analyses.

This study analyzed the impact of macroeconomic variables (manufacturing, real exchange rate, government expenditure, and gross fixed capital formation) on GDP growth in Ghana. Utilizing secondary data from the World Development Indicators of the World Bank (1991–2021), we employed a hierarchical Bayesian linear model with interaction effects to assess these relationships. The results indicate that the real exchange rate, manufacturing, and government expenditure have a positive influence on GDP growth, while gross fixed capital formation exhibits a moderately negative effect. To enhance economic growth, it is crucial to optimize capital investments, bolster export competitiveness through targeted policies, and invest in manufacturing innovation. These findings offer actionable insights for policymakers aiming to stimulate economic growth in Ghana.

This article developed an estimator for finite population mean under probability‐proportional‐to‐size sampling in the presence of extreme values. Theoretical properties such as bias, variance, and consistency are derived. Monte Carlo simulations were performed to assess the consistency and efficiency of the proposed estimator. It is found that the proposed estimator is more efficient than the competing estimators for all values of c between 0 and 1. The gain in precision of the proposed estimator is much higher than that of its competitors for small values of c. Empirical applications of the proposed estimator are illustrated using three real data sets, and the results revealed that the proposed estimator performed better than the conventional and Sarndal (1972) estimators.

The existence of non-linear relationship between study and auxiliary variable may show a false positive or negative correlation between them. This challenge coupled with nonresponse will lead to bias estimate and wrong conclusions. Therefore, this study developed an improved estimator for finite population mean when nonignorable nonresponse is present. With the aid of auxiliary variable, using stratified random sampling, the study proposed an estimator using the direct generalized ratio estimator. Statistical properties of the proposed estimator such as bias and mean square error up to second order of approximation were obtained. Simulation and empirical studies were performed on the derived statistical properties of the proposed estimator using linear and nonlinear populations. The simulation and empirical studies indicated that the proposed estimator (\(\hat{R}\)_DG,2,i) works better with nonlinear population. The numerical demonstrations further indicated that proposed estimator was found to be less bias and more consistent but less ecient in terms of mean square error when compared with that of Ashutosh [1]. Therefore, the referenced estimator may prove to be useful when the focus is on inference. However, when emphasis shifts to estimation, the proposed estimator (\(\hat{R}\)_DG,2,i) is preferred.