Unit upper truncated Weibull distribution with extension to 0 and 1 inflated model - Theory and applications.

A two-parameter unit distribution and its regression model plus its extension to 0 and 1 inflation is introduced and studied. The distribution is called the unit upper truncated Weibull (UUTW) distribution, while the inflated variant is called the 0-1 inflated unit upper truncated Weibull (ZOIUUTW) distribution. The UUTW distribution has an increasing and a J-shaped hazard rate function. The parameters of the proposed models are estimated by the method of maximum likelihood estimation. For the UUTW distribution, two practical examples involving household expenditure and maximum flood level data are used to show its flexibility and the proposed distribution demonstrates better fit tendencies than some of the competing unit distributions. Application of the proposed regression model demonstrates adequate capability in describing the real data set with better modeling proficiency than the existing competing models. Then, for the ZOIUUTW distribution, the CD34+ data involving cancer patients are analyzed to show the flexibility of the model in characterizing inflation at both endpoints of the unit interval.

Time series and power law analysis of crop yield in some east African countries.

We carry out a time series analysis on the yearly crop yield data in six east African countries (Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda) using the autoregressive integrated moving average (ARIMA) model. We describe the upper tail of the yearly crop yield data in those countries using the power law, lognormal, Fréchet and stretched exponential distributions. The forecast of the fitted ARIMA models suggests that the majority of the crops in different countries will experience neither an increase nor a decrease in yield from 2019 to 2028. A few exceptional cases correspond to significant increase in the yield of sorghum and coffee in Burundi and Rwanda, respectively, and significant decrease in the yield of beans in Burundi, Kenya and Rwanda. Based on Vuong's similarity test p-value, we find that the power law distribution captured the upper tails of yield distribution better than other distributions with just one exceptional case in Uganda, suggesting that these crops have the tendency for producing high yield. We find that only sugar cane in Somalia and sweet potato in Tanzania have the potential of producing extremely high yield. We describe the yield behaviour of these two crops as black swan, where the "rich getting richer" or the "preferential attachment" could be the underlying generating process. Other crops in Burundi, Kenya, Somalia, Tanzania, Uganda and Rwanda can only produce high but not extremely high yields. Various climate adaptation/smart strategies (use of short-duration pigeon pea varieties, use of cassava mosaic disease resistant cassava varieties, use of improved maize varieties, intensive manuring with a combination of green and poultry manure, early planting, etc) that could be adapted to increase yields in east Africa are suggested. The paper could be useful for future agricultural planning and rates calibration in crop risk insurance.

Benzo[A]Pyrene Biodegradation by Multiple and Individual Mesophilic Bacteria under Axenic Conditions and in Soil Samples.

To date, only a handful of bacterial strains that can independently degrade and utilize benzo[a]pyrene (BaP) as the sole carbon source has been isolated and characterized. Here, three new bacterial strains-JBZ1A, JBZ2B, and JBZ5E-were isolated from contaminated soil and, using 16S rRNA sequencing, were identified as Brad rhizobium japonicum, Micrococcus luteus, and Bacillus cereus, respectively. The growth ability of each individual strain and a consortium of all strains in the presence of BaP (4-400 µmol·L-1, pH 7, 37 °C) was identified by the doubling time (dt). The results illustrate that dt decreased with increasing BaP concentrations for individual strains and the consortium. The optimum growth conditions of the consortium were 37 °C, 0.5% NaCl (w/v), and pH 7. Under these conditions, the degradation rate was 1.06 µmol·L-1·day-1, whereas that of individual strains ranged from 0.9 to 0.38 µmol·L-1·day-1. B. cereus had the strongest contribution to the consortium's activity, with a degradation rate of 0.9 µmol·L-1·day-1. The consortium could also remove BaP spiked with soil but at a lower rate (0.01 µmol L-1.day-1). High-performance liquid chromatography-high-resolution tandem mass spectrometry permitted the detection of the metabolites of these strains, and a biodegradation pathway is proposed.

Forecasting Value at Risk and Expected Shortfall of Foreign Exchange Rate Volatility of Major African Currencies via GARCH and Dynamic Conditional Correlation Analysis.

Research on the exchange rate volatility and dynamic conditional correlation of African currencies/financial markets interdependence appears to be limited. In this paper, we employ GARCH models to characterize the exchange rate volatility of eight major African currencies. The variation of interdependence with respect to time is described using the DCC-GARCH model. From the results of the DCC, remarkable variations in correlations through time across these countries are observed with the correlations varying from low to moderate, suggesting that African economies are generally governed by certain economic factors and are vastly regulated. These regulations, including exchange rate misalignment led to sluggish and negative growth in most of the African countries. For instance, persistent misalignment can cause high levels of inflation, for example, undervaluation. Overvaluation can lead to trade imbalances and they can in turn create macroeconomic instability and balance of payment problems. Given these results, we suggest that policy makers should revamp and adopt state resilience so as to reduce the negative effect of exchange rate misalignment on economic growth.

Socio-economic and demographic impacts on the full awareness of the methods for controlling/preventing the spread of COVID-19 among social media users in some African countries at the onset of the pandemic.

OBJECTIVE: In Africa, most countries continue to battle COVID-19 with cases of newly infected still being recorded. In this note, we investigate how socioeconomic and demographic factors affected individuals awareness on the methods for controlling/preventing the spread of COVID-19 in some parts of Africa at the onset of the pandemic. RESULTS: Based on regression modelling, we find that having full awareness does not depend on religious affiliation. Men, urban dwelling, holding bachelors or higher degrees, operating multiple social media accounts or being employed are associated with having full awareness of the recommended practices for the prevention and control of COVID-19 at the early stage of the pandemic. No occupation, business or older people are associated with not having full awareness.

Compound distributions for financial returns.

In this paper, we propose six Student's t based compound distributions where the scale parameter is randomized using functional forms of the half normal, Fréchet, Lomax, Burr III, inverse gamma and generalized gamma distributions. For each of the proposed distribution, we give expressions for the probability density function, cumulative distribution function, moments and characteristic function. GARCH models with innovations taken to follow the compound distributions are fitted to the data using the method of maximum likelihood. For the sample data considered, we see that all but two of the proposed distributions perform better than two popular distributions. Finally, we perform a simulation study to examine the accuracy of the best performing model.

A Bayesian latent process spatiotemporal regression model for areal count data.

Model-based approaches for the analysis of areal count data are commonplace in spatiotemporal analysis. In Bayesian hierarchical models, a latent process is incorporated in the mean function to account for dependence in space and time. Typically, the latent process is modelled using a conditional autoregressive (CAR) prior. The aim of this paper is to offer an alternative approach to CAR-based priors for modelling the latent process. The proposed approach is based on a spatiotemporal generalization of a latent process Poisson regression model developed in a time series setting. Spatiotemporal dependence in the autoregressive model for the latent process is modelled through its transition matrix, with a structured covariance matrix specified for its error term. The proposed model and its parameterizations are fitted in a Bayesian framework implemented via MCMC techniques. Our findings based on real-life examples show that the proposed approach is at least as effective as CAR-based models.