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Richard Minkah

Statistics and Actuarial Science

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Research Summary

(inferred from publications by AI)

The researcher's work focuses on advancing statistical methodologies, particularly extreme value theory and Pareto-type distributions, to address challenges in analyzing heavy-tailed data across diverse fields such as insurance, hydrology, and environmental science. Their research emphasizes developing robust estimators for tail indices under various censoring conditions and missing data scenarios, aiming to provide reliable methods for risk assessment and predictive modeling in these domains.

Research Themes

All Papers

A size-of-loss model for the negatively skewed insurance claims data: applications, risk analysis using different methods and statistical forecasting(2022)
A Novel Model for Quantitative Risk Assessment under Claim-Size Data with Bimodal and Symmetric Data Modeling(2023)
Robust estimation of Pareto-type tail index through an exponential regression model(2021)
On Extreme Value Index Estimation under Random Censoring(2018)
Assessing the Performance of the Discrete Generalised Pareto Distribution in Modelling Non-Life Insurance Claims(2021)
Assessing groundwater quality in peri-urban Accra, Ghana: Implications for drinking and irrigation purposes(2022)
Endemic grasshopper species distribution in an agro-natural landscape of the Cape Floristic Region, South Africa(2017)
Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm(2021)
Robust Extreme Quantile Estimation for Pareto-Type tails through an Exponential Regression Model(2022)
Robust extreme quantile estimation for Pareto-type tails through an exponential regression model(2023)
An Enhanced Method for Tail Index Estimation under Missingness(2021)
Tail Index Estimation of the Generalised Pareto Distribution using a Pivot from a Transformed Pareto Distribution(2020)
A Simulation Comparison of Estimators of Conditional Extreme Value Index under Right Random Censoring(2018)
Constant versus Covariate Dependent Threshold in the Peaks-Over Threshold Method(2018)
A Reduced-Bias Weighted least square estimation of the Extreme Value Index(2021)
A reduced-bias weighted least squares estimation of the extreme value index(2024)
Bayesian Estimation of Presidential Elections in Ghana: A Validation Approach(2022)
The unfolding mystery of the numbers: First and second digits based comparative tests and its application to Ghana’s elections(2023)
Baseline comparative analysis and review of election forensics: Application to Ghana's 2012 and 2020 presidential elections(2023)
An application of extreme value theory to the management of a hydroelectric dam(2016)
Shrinkage Methods for Estimating the Shape Parameter of the Generalized Pareto Distribution(2023)
On statistics of extremes under random censoring(2016)
A Simulation Comparison of Estimators of Conditional Extreme Value Index\n under Right Random Censoring(2017)
A Cost of Misclassification Adjustment Approach for Estimating Optimal Cut-Off Point for Classification(2024)
Comparison of Confidence Interval Estimators: an Index Approach(2017)
A Markov chain Monte Carlo (MCMC) methodology with bootstrap percentile estimates for predicting presidential election results in Ghana(2015)
Estimation of the Tail Index of Pareto‐Type Distributions Using Regularisation(2022)
Assessing the Performance of the Discrete Generalised Pareto Distribution in Modelling Non-Life Insurance Claims(2020)
Evaluating the impact of misspecified spatial neighboring structures in Bayesian CAR models(2024)

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.