arXiv subjects

Feridun Tasdan

Publications and source records attributed to Feridun Tasdan.

3 recordsLinked to original sources

arXiv2026

Evaluation of Circular Logistic Regression Models with Asymmetric Link Functions

Circular (directional) data arise whenever observations are measured as angles on the unit circle, such as wind direction, time of day, or calendar phase, and require statistical methods that respect the periodicity of the domain $[0; 2\pi)$. While circular-linear and linear-circular regression models are well established, regression models for a binary or binomial response observed jointly with a circular predictor remain largely undeveloped, with the sole closely related study restricted to the symmetric logit link. This paper develops and evaluates a circular logistic regression framework in which the linear predictor is expressed through the cosine and sine of the circular covariate, and compares the performance of symmetric link functions (logit, probit) against asymmetric alternatives (complementary log-log, Cauchit, and a skew-logit power link) under a generalized linear model formulation. A Monte Carlo simulation generates circular predictors from the von Mises distribution under two concentration regimes and evaluates model fit using the Akaike Information Criterion (AIC) and deviance. The methodology is illustrated with two real data sets: daily rainfall occurrence and wind direction recorded in Macomb, Illinois, and monthly earthquake counts in Western Anatolia, Turkiye, the latter used to connect the binary circular model to the related circular Poisson regression framework for count outcomes. Results indicate that the choice of link function matters most when the circular predictor is broadly dispersed and the response is markedly unbalanced; under high concentration of the predictor, symmetric links are preferred and asymmetric links are prone to instability. Practical guidelines and directions for future software development are discussed.

stat.ME↗

arXiv2026

Smoothed Rank-Based Regression Estimation Using Wilcoxon Score Functions

This article proposes an improved rank based regression estimator obtained by replacing the ordinary integer ranks in the Wilcoxon rank-score regression procedure with smoothed ranks derived from a smoothed empirical cumulative distribution function. The smoothed ranks are computed via a continuous, nondecreasing kernel distribution function H that provides a differentiable approximation to the classical indicator function used in standard rank regression. Substituting these smoothed ranks into the Wilcoxon score function yields a new estimator for the slope parameter(s) of the simple and multiple linear regression model. We show that the proposed estimator inherits the robustness properties of classical rank regression while providing improved efficiency under heavy tailed error distributions and better handling of tied observations. A Wald type hypothesis test for the regression coefficients is derived and its asymptotic normality is established. A Monte Carlo simulation study compares new estimator with the ordinary least-squares (OLS) estimator, the classical Wilcoxon rank regression estimator, and the Theil and Sen estimator under several error distributions including the normal, Laplace, Cauchy, and contaminated normal. The proposed estimator achieves relative efficiencies at or above those of classical rank regression uniformly across all scenarios considered, with notable gains in the presence of outliers and heavy-tailed errors.

stat.ME↗

arXiv2025

Enhanced Rank-Based Correlation Estimation Using Smoothed Wilcoxon Rank Scores

This article proposes an improved version of the Spearman rank correlation based on using Wilcoxon rank score function. A smoothed empirical cumulative distribution function (ecdf)computes the smoothed ranks and replaces the regular ranks in the Wilcoxon rank score function. The smoothed Wilcoxon rank scores are then used for estimation of the Spearman's correlation. The proposed approach is similar to the Spearman's rho estimator which uses ranks of the random samples of X and Y but the proposed method improves Spearman's approach such as handling ties and gaining higher efficiency under monotone associations. A Wald type hypothesis test has been proposed for the new estimator and the asymptotic properties are shown.

stat.ME↗