CIRD Logo
CIRD Publications
Centre for International Research Development
Open Access Peer Reviewed Monthly Est. 2014

Academic Journal of Statistics and Mathematics

(AJSM)
ISSN (Print): 4052-392X | ISSN (Online): 5730-7151
6.53
Impact Factor
11
H-Index
634+
Articles
Home About Journal Editorial Board Current Issue Archives Submit to Journal
📄 Acad. J. Stat. Math. — Volume 23, Issue 2 (2037)
Academic Journal of Statistics and Mathematics  |  Volume 23, Issue 2  |  2037  |  6 Articles
Open Access Double-Blind Peer Reviewed ISSN: 5730-7151 (Online)
📋 Articles — Volume 23, Issue 2
Research Article Pages 1–20
Article 1 of 6

Copula-Based Dependence Structures in Financial Risk Modelling

Owusu, E.B. & Mensah, P.

This study investigates copula-Based Dependence Structures in Financial Risk Modelling. Using rigorous quantitative and qualitative methodologies, the research examines key variables and their interrelationships within the relevant theoretical framework. Findings reveal significant implications for policy, practice, and future research. The…

DOI: 10.15580/AJSM.2037.2.001  •  Keywords: Copula, Dependence, Financial Risk, Tail Dependence  • 
View Abstract 📥 PDF Cite Share
Research Article Pages 21–32
Article 2 of 6

A New Class of Probability Distributions for Modelling Extreme Events

da Silva, R.M., Pereira, J.C. & Santos, L.

This study investigates a New Class of Probability Distributions for Modelling Extreme Events. Using rigorous quantitative and qualitative methodologies, the research examines key variables and their interrelationships within the relevant theoretical framework. Findings reveal significant implications for policy, practice, and…

DOI: 10.15580/AJSM.2037.2.002  •  Keywords: Probability, Extreme Value Theory, Distribution, Tail Risk  • 
View Abstract 📥 PDF Cite Share
Research Article Pages 33–45
Article 3 of 6

Robust Regression Methods for Heteroscedastic Panel Data

Okonkwo, P.C. & Adeyemi, F.B.

This study investigates robust Regression Methods for Heteroscedastic Panel Data. Using rigorous quantitative and qualitative methodologies, the research examines key variables and their interrelationships within the relevant theoretical framework. Findings reveal significant implications for policy, practice, and future research. The…

DOI: 10.15580/AJSM.2037.2.003  •  Keywords: Regression, Heteroscedasticity, Panel Data, Robust Methods  • 
View Abstract 📥 PDF Cite Share
Research Article Pages 46–60
Article 4 of 6

Spectral Methods for Solving Partial Differential Equations in Fluid Dynamics

Park, S.J. & Kim, D.H.

This study investigates spectral Methods for Solving Partial Differential Equations in Fluid Dynamics. Using rigorous quantitative and qualitative methodologies, the research examines key variables and their interrelationships within the relevant theoretical framework. Findings reveal significant implications for policy, practice, and…

DOI: 10.15580/AJSM.2037.2.004  •  Keywords: Spectral Methods, PDE, Fluid Dynamics, Numerical Analysis  • 
View Abstract 📥 PDF Cite Share
Research Article Pages 61–78
Article 5 of 6

Bayesian Estimation of Stochastic Differential Equations with Applications

Patel, R.K., Sharma, A. & Singh, V.

This study investigates bayesian Estimation of Stochastic Differential Equations with Applications. Using rigorous quantitative and qualitative methodologies, the research examines key variables and their interrelationships within the relevant theoretical framework. Findings reveal significant implications for policy, practice, and future research.…

DOI: 10.15580/AJSM.2037.2.005  •  Keywords: Bayesian, Stochastic Differential Equations, Estimation, MCMC  • 
View Abstract 📥 PDF Cite Share
Research Article Pages 79–90
Article 6 of 6

Time Series Forecasting with Long Short-Term Memory Networks

Garcia, M.L., Rodriguez, J. & Lopez, C.

This study investigates time Series Forecasting with Long Short-Term Memory Networks. Using rigorous quantitative and qualitative methodologies, the research examines key variables and their interrelationships within the relevant theoretical framework. Findings reveal significant implications for policy, practice, and future research.…

DOI: 10.15580/AJSM.2037.2.006  •  Keywords: Time Series, LSTM, Forecasting, Deep Learning  • 
View Abstract 📥 PDF Cite Share
← Vol. 23, No. 1 📁 All Issues Next Issue →