Preliminary test and Stein-type shrinkage LASSO-based estimators

Peer Reviewe

Preliminary test and Stein-type shrinkage LASSO-based estimators

Suppose the regression vector-parameter is subjected to lie in a subspace hypothesis in a linear regression model. In situations where the use of least absolute and shrinkage selection operator (LASSO) is desired, we propose a restricted LASSO estimator. To improve its performance, LASSO-type shrinkage estimators are also developed and their asymptotic performance is studied. For numerical analysis, we used relative efficiency and mean prediction error to compare the estimators which resulted in the shrinkage estimators to have better performance compared to the LASSO

Bivariate noncentral distributions: An approach via the compounding method

This paper enriches the existing literature of bivariate noncentral distributions by proposing bivariate noncentral generalised chi-square and F distributions via the employment of the compounding method with Poisson probabilities. This method has been used to a limited extent to obtain univariate noncentral distributions; this study extends some results in literature to the corresponding bivariate setting. The process which is followed to obtain such bivariate noncentral distributions is systematically described and motivated. Some distributions of composites (univariate functions of the dependent components of the bivariate distributions) are derived, in particular the product, ratio, and proportion. Furthermore, an example of possible application is given and discussed to illustrate the versatility of the proposed models

A spatial analysis of COVID-19 reported cases in the Gauteng province, South Africa: Identifying wards to be targeted early in future infectious diseases outbreak

The COVID-19 pandemic caused major disruptions and contributed to the loss of livelihoods and income. The pandemic also provided public health and health systems policy shifts towards better promotion and protection in responding to such disasters and emergencies. Due to differing effects of socio-economic infectious disease vulnerabilities and pre-pandemic levels of preparedness for health emergencies, health system strengthening requires targeted and ununiform implementation. We employ spatial statistical methods on the COVID-19 confirmed cases in identifying wards that could be targeted for strengthening health security in the Gauteng Province, South Africa. In this way, the identified high-risk wards would be more effective and prepared to respond to future pandemics and emergencies.Comment: 21 pages, 10 figures and 9 table

Uncovering a generalised gamma distribution: from shape to interpretation

In this paper, we introduce the flexible interpretable gamma (FIG) distribution which has been derived by Weibullisation of the body-tail generalised normal distribution. The parameters of the FIG have been verified graphically and mathematically as having interpretable roles in controlling the left-tail, body, and right-tail shape. The generalised gamma (GG) distribution has become a staple model for positive data in statistics due to its interpretable parameters and tractable equations. Although there are many generalised forms of the GG which can provide better fit to data, none of them extend the GG so that the parameters are interpretable. Additionally, we present some mathematical characteristics and prove the identifiability of the FIG parameters. Finally, we apply the FIG model to hand grip strength and insurance loss data to assess its flexibility relative to existing models