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- MakaleYalnız künye2026Scopus SJRQ2A bias-corrected partial bernstein copula approach for nonparametric regression
Susam, Selim Orhun
Multivariate Bernstein polynomials yield smooth, boundary–bias–free estimators for copulas and their derivatives. The classical partial Bernstein copula derivative of Janssen et al. (2016) suffers from an uncorrected first–order bias that can dominate the mean squared error (MSE) in moderate samples. We introduce a simple double–difference correction that removes the entire leading bias term while increasing the variance by a small fixed multiplicative factor only. When this bias–reduced partial derivative is plugged into the copula based regression integral, the resulting estimator achieves substantially smaller bias and uniformly lower MSE than the classical version. Analytic bias–variance expansions are provided, and a comprehensive Monte Carlo study shows up to 50% reductions in MSE across a range of Bernstein copula degrees. A real data application to the mcycle dataset further demonstrates the practical advantages of the method, with the smoothing parameters selected by least squares cross validation (LSCV).
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İncele - MakaleYalnız künye2025Scopus SJRQ4Spearman dependence function-based goodness-of-fit test for the gene's relation
Susam, Selim Orhun; Hudaverdi, Burcu
A gene network represents the relationship between different groups of genes with various functions, aiming to depict how genes collaborate and influence each other’s activities within a biological system. This relationship can be effectively explained using copulas. Therefore, it is crucial to determine which copula best fits the gene data and provide the most accurate explanation of the relationships between gene groups. In this study, our objective is to introduce a Spearman dependence function-based goodness-of-fit test using Bernstein polynomial approximation. We apply this test to identify a copula model that can effectively explain the relationships between gene groups. A Monte Carlo simulation study is conducted to assess the performance of the proposed test. Next, we analyse histone gene groups using data from yeast cell regulation, as provided by Eisen et al. (1998). Specifically, we investigate the dependence model structures of gene interactions for eight histone genes.
AtıfScopus2WoS2OpenAlex2İncele - MakaleYalnız künye2024Scopus SJRQ2Stress–strength reliability for electricity systems based on the Bernstein copula approximation
Susam, Selim Orhun; Hudaverdi, Burcu
Electricity is a critical resource for modern society, and its availability is essential for various aspects of daily life, from powering homes to supporting industrial processes and infrastructure. Ensuring a reliable supply of electricity is vital for economic stability and the well-being of a population. As the world moves toward more sustainable and renewable energy sources, understanding the reliability of these sources compared to traditional fossil fuels is crucial. In this study, we develop a methodology that enables the measurement of stress–strength reliability when electricity consumption is considered as the strength variable, positioned between electricity generation from renewable energy sources and fossil fuels as stress variables. We achieve this using the copula-based reliability with Bernstein copula approximation. The methodology is applied to the U.S. monthly electricity data. The result shows that the current rates of electricity output from fossil fuels and renewables will not be sufficient to meet the demand for electricity.
AtıfScopus2WoS3OpenAlex3İncele - MakaleYalnız künye2023Scopus SJRQ2On the copula-based reliability of stress-strength model under bivariate stress
Hudaverdi, Burcu; Susam, Selim Orhun
In this paper, we consider the stress-strength reliability (Formula presented.) where the strength Y of a component lies between the dependent stress variables (Formula presented.) and (Formula presented.). We propose a copula-based approach for stress-strength reliability having bivariate stress. We obtain R for Farlie-Gumbel-Morgenstern copula with Burr III marginals. Also, we propose a Bernstein copula approximation for evaluating R under the stress-strength setup. We present empirical and maximum likelihood-based estimation procedures and compare their performances by Monte Carlo simulation. We apply the proposed approach to chemical and overt diabetes data for illustration purpose.
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