Financial Data and a New Generalization of the Skew-T Distribution

This work introduces a three-parameter hybrid model named the exponentiated half logistic skew-t distribution using the exponentiated half logistic generalised distributions. The hybrid model is appropriate for modelling skewed, heavy-and-long-tail datasets. The theoretical properties of the new model were investigated. Simulation studies performed to evaluate the finite sample performance of the parameter estimates using selected true parameter values showed that the estimates approached the true values as the sample size increased. The hybrid model efficacy, applicability and flexibility were demonstrated using the Nigeria inflation rate dataset, and the result indicated that the hybrid model outperformed several competing distributions

Inverse Odd Weibull Generated Family of Distribution

This work proposes an inverse odd Weibull (IOW) family of distributions for a lifetime distributions. Some mathematical properties of this family of distribution were derived. Survival, hazard, quantiles, reversed hazard, cumulative, odd functions, kurtosis, skewness, order statistics and entropies of this new family of distribution were examined. The parameters of the family of distributions were obtained by maximum likelihood. The behavior of the estimators were studied through simulation. The flexibility and importance of the distribution by means of real data set applications were emphasized

Inverse Odd Weibull Generated Family of Distribution

This work proposes an inverse odd Weibull (IOW) family of distributions for a lifetime distributions. Some mathematical properties of this family of distribution were derived. Survival, hazard, quantiles, reversed hazard, cumulative, odd functions, kurtosis, skewness, order statistics and entropies of this new family of distribution were examined. The parameters of the family of distributions were obtained by maximum likelihood. The behavior of the estimators were studied through simulation. The flexibility and importance of the distribution by means of real data set applications were emphasized.</jats:p

A Modified Binary Pigeon-Inspired Algorithm for Solving the Multi-dimensional Knapsack Problem

Abstract The pigeon-inspired optimization algorithm is a category of a newly proposed swarm intelligence-based algorithm that belongs to the population-based solution technique. The MKP is a class of complex optimization problems that have many practical applications in the fields of engineering and sciences. Due to the practical applications of MKP, numerous algorithmic-based methods like local search and population-based search algorithms have been proposed to solve the MKP in the past few decades. This paper proposes a modified binary pigeon-inspired optimization algorithm named (Modified-BPIO) for the 0 - 1 multidimensional knapsack problem (MKP). The utilization of the binary pigeon-inspired optimization (BPIO) for solving the multidimensional knapsack problem came with huge success. However, it can be observed that the BPIO converges prematurely due to lost diversity during the search activities. Given the above, the crossover operator is integrated with the landmark component of the BPIO to improve the diversity of the solution space. The MKP benchmarks from the Operations Research (OR) library are utilized to test the performance of the proposed binary method. Experimentally, it is concluded that the proposed Modified-BPIO has a better performance when compared with the BPIO and existing state-of-the-arts that worked on the same MKP benchmarks.</jats:p