arXiv subjects

Sameera Hewage

Publications and source records attributed to Sameera Hewage.

9 recordsLinked to original sources

arXiv2026

Comparing Two Categorical Gini Correlations with Applications to Classification Problems

This article proposes an inferential framework for comparing predictor importance in classification problems with categorical response variables. The approach is based on the categorical Gini correlation (CGC) proposed by Dang et al. (2020), a measure of dependence between numerical predictors and categorical outcomes. Predictor importance is evaluated by testing differences in CGCs across competing predictor groups. The proposed methodology accommodates predictors of arbitrary and unequal dimensions and allows for dependence between predictor groups. Asymptotic normality of the test statistic is established under both the null and alternative hypotheses, and the resulting test is shown to be consistent. In addition to deriving the asymptotic distribution, a nonparametric bootstrap procedure is developed as an alternative approach to inference. Simulation studies, along with applications to breast cancer and human activity recognition datasets, demonstrate the effectiveness of the proposed framework.

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arXiv2026

Bayesian Inference for Joint Tail Risk in Paired Biomarkers via Archimedean Copulas with Restricted Jeffreys Priors

We propose a Bayesian copula-based framework to quantify clinically interpretable joint tail risks from paired continuous biomarkers. After converting each biomarker margin to rank-based pseudo-observations, we model dependence using one-parameter Archimedean copulas and focus on three probability-scale summaries at tail level $\alpha$: the lower-tail joint risk $R_L(\theta)=C_\theta(\alpha,\alpha)$, the upper-tail joint risk $R_U(\theta)=2\alpha-1+C_\theta(1-\alpha,1-\alpha)$, and the conditional lower-tail risk $R_C(\theta)=R_L(\theta)/\alpha$. Uncertainty is quantified via a restricted Jeffreys prior on the copula parameter and grid-based posterior approximation, which induces an exact posterior for each tail-risk functional. In simulations from Clayton and Gumbel copulas across multiple dependence strengths, posterior credible intervals achieve near-nominal coverage for $R_L$, $R_U$, and $R_C$. We then analyze NHANES 2017--2018 fasting glucose (GLU) and HbA1c (GHB) ($n=2887$) at $\alpha=0.05$, obtaining tight posterior credible intervals for both the dependence parameter and induced tail risks. The results reveal markedly elevated extremal co-movement relative to independence; under the Gumbel model, the posterior mean joint upper-tail risk is $R_U(\alpha)=0.0286$, approximately $11.46\times$ the independence benchmark $\alpha^2=0.0025$. Overall, the proposed approach provides a principled, dependence-aware method for reporting joint and conditional extremal-risk summaries with Bayesian uncertainty quantification in biomedical applications.

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arXiv2025

Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification

Clinical and gene-expression models predict breast cancer outcomes, but simple linear fusion ignores dependence between their risk scores. Using METABRIC, we tested whether modeling the joint distribution of clinical and gene-expression scores improved stratification of 5-year cancer-specific mortality. We defined clinical and mRNA-expression predictor views, trained classifiers, and obtained out-of-fold probabilities through 5-fold cross-validation. The scores were transformed into pseudo-observations on (0,1)^2 and used to fit Gaussian, Clayton, Gumbel, and Frank copulas. The clinical model discriminated better than the gene-expression model (AUC 0.783 vs 0.721). Frank had the smallest goodness-of-fit statistic, with Gaussian performing similarly. Copula fusion did not improve ROC-AUC over the clinical model. However, joint score groups showed clear survival differences, with patients scoring high on both views having the poorest outcomes. Competing-risks analysis showed the same pattern for cancer-death incidence. We also conducted an external evaluation in independent TCGA data using shared predictors and a harmonized 5-year overall-mortality endpoint. Copula-fused, individual, and simple-fusion scores showed comparable discrimination with overlapping confidence intervals. All received the same METABRIC-based recalibration. No gene met the prespecified stability criterion under repeated cross-validated permutation importance, so gene-level findings were treated as exploratory. Copulas provide an explicit, interpretable description of dependence between clinical and gene-expression risk scores and support descriptive joint-group analyses. This methodological study does not establish superior prediction, validated clinical risk categories, or clinical utility.

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arXiv2025

Bag of Coins: A Statistical Probe into Neural Confidence Structures

Modern neural networks often produce miscalibrated confidence scores and struggle to detect out-of-distribution (OOD) inputs, while most existing methods post-process outputs without testing internal consistency. We introduce the Bag-of-Coins (BoC) probe, a non-parametric diagnostic of logit coherence that compares softmax confidence $\hat p$ to an aggregate of pairwise Luce-style dominance probabilities $\bar q$, yielding a deterministic coherence score and a p-value-based structural score. Across ViT, ResNet, and RoBERTa with ID/OOD test sets, the coherence gap $\Delta=\bar q-\hat p$ reveals clear ID/OOD separation for ViT (ID ${\sim}0.1$-$0.2$, OOD ${\sim}0.5$-$0.6$) but substantial overlap for ResNet and RoBERTa (both ${\sim}0$), indicating architecture-dependent uncertainty geometry. As a practical method, BoC improves calibration only when the base model is poorly calibrated (ViT: ECE $0.024$ vs.\ $0.180$) and underperforms standard calibrators (ECE ${\sim}0.005$), while for OOD detection it fails across architectures (AUROC $0.020$-$0.253$) compared to standard scores ($0.75$-$0.99$). We position BoC as a research diagnostic for interrogating how architectures encode uncertainty in logit geometry rather than a production calibration or OOD detection method.

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arXiv2025

gcor: A Python Implementation of Categorical Gini Correlation and Its Inference

Categorical Gini Correlation (CGC), introduced by Dang et al. (2020), is a novel dependence measure designed to quantify the association between a numerical variable and a categorical variable. It has appealing properties compared to existing dependence measures, such as zero correlation mutually implying independence between the variables. It has also shown superior performance over existing methods when applied to feature screening for classification. This article presents a Python implementation for computing CGC, constructing confidence intervals, and performing independence tests based on it. Efficient algorithms have been implemented for all procedures, and they have been optimized using vectorization and parallelization to enhance computational efficiency.

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arXiv2025

CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction

Class imbalance remains a practical obstacle in the development of clinical prediction models for conditions such as diabetes mellitus, where the number of confirmed cases is often much smaller than the number of controls. The Synthetic Minority Over-sampling Technique (SMOTE) and its variants are widely used to address this imbalance, but they generate synthetic observations through local interpolation in feature space and do not explicitly model the joint dependence structure of the minority class. To address this challenge, our study introduces a copula-based data augmentation approach that estimates the minority-class dependence structure when generating synthetic samples and integrates with standard machine learning techniques. Specifically, we employ truncated vine copulas to represent multivariate dependence through a sequence of bivariate building blocks. We evaluate the proposed approach on three public diabetes datasets, namely the Pima Indians Diabetes dataset, the Iraqi Diabetes dataset, and the CDC BRFSS 2015 Diabetes Health Indicators dataset, which together cover a range of sample sizes, dimensionalities, and imbalance regimes. For each dataset, five resampling strategies are compared across five classifiers using a 5 by 2 cross validation protocol with Dietterich's paired t test. Our findings suggest that CopulaSMOTE can improve minority-class recovery in larger tabular diabetes datasets, particularly the CDC BRFSS dataset, but its advantages depend on the classifier and evaluation metric.

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arXiv2025

A2 Copula-Driven Spatial Bayesian Neural Network For Modeling Non-Gaussian Dependence: A Simulation Study

In this paper, we introduce the A2 Copula Spatial Bayesian Neural Network (A2-SBNN), a predictive spatial model designed to map coordinates to continuous fields while capturing both typical spatial patterns and extreme dependencies. By embedding the dual-tail novel Archimedean copula viz. A2 directly into the network's weight initialization, A2-SBNN naturally models complex spatial relationships, including rare co-movements in the data. The model is trained through a calibration-driven process combining Wasserstein loss, moment matching, and correlation penalties to refine predictions and manage uncertainty. Simulation results show that A2-SBNN consistently delivers high accuracy across a wide range of dependency strengths, offering a new, effective solution for spatial data modeling beyond traditional Gaussian-based approaches.

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arXiv2025

A Copula Based Supervised Filter for Feature Selection in Diabetes Risk Prediction Using Machine Learning

Effective feature selection is critical for robust and interpretable predictive modeling in medicine, especially when risk factors matter most in extreme patient strata. Many standard selectors emphasize average associations and can miss predictors whose relevance is concentrated in the distribution tails. We propose a computationally efficient supervised filter based on a Gumbel-copula implied upper-tail concordance score (lambda U), defined as a monotone transformation of Kendall's tau, to rank features by their tendency to be simultaneously extreme with the positive class. We compare against four common baselines (Mutual Information, mRMR, ReliefF, and L1/Elastic-Net) across four classifiers on two diabetes datasets: a large-scale public health survey (CDC, N=253,680) and a clinical benchmark (PIMA, N=768). Analyses include statistical testing, permutation importance, and robustness checks. On CDC, the proposed selector is the fastest and reduces 21 features to 10 (approx 52%). This yields a small but statistically significant trade-off relative to using all features, while performing better than standard filters (Mutual Information, mRMR) and comparably to the strong ReliefF baseline. On PIMA (8 predictors), the resulting ranking attains the highest ROC-AUC numerically, though paired DeLong tests show no significant differences versus strong baselines; PIMA therefore serves as a ranking-only sanity check in a low-dimensional setting. Across both datasets, the lambda U-based selector highlights clinically coherent predictors and provides an efficient, interpretable screening step that can complement standard feature-selection methods in public health and clinical risk prediction.

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arXiv2023

Jackknife empirical likelihood confidence intervals for the categorical Gini correlation

The categorical Gini correlation, $ρ_g$, was proposed by Dang et al. to measure the dependence between a categorical variable, $Y$ , and a numerical variable, $X$. It has been shown that $ρ_g$ has more appealing properties than current existing dependence measurements. In this paper, we develop the jackknife empirical likelihood (JEL) method for $ρ_g$. Confidence intervals for the Gini correlation are constructed without estimating the asymptotic variance. Adjusted and weighted JEL are explored to improve the performance of the standard JEL. Simulation studies show that our methods are competitive to existing methods in terms of coverage accuracy and shortness of confidence intervals. The proposed methods are illustrated in an application on two real datasets.

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