Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ2/r/munzur/1504
  A Bernstein polynomial estimation of the moment generating function

  Susam, Selim Orhun

  We study Bernstein polynomial smoothing for nonparametric estimation of the moment generating function (MGF) of a distribution supported on (Formula presented.). We prove strong consistency of the Bernstein-smoothed MGF estimator and show that, when the smoothing degree m grows sufficiently fast, it shares the same (Formula presented.) asymptotic normal distribution as the empirical MGF estimator, with variance (Formula presented.). A second-order bias–variance expansion reveals that Bernstein smoothing yields a bias of order (Formula presented.) and a variance-reduction term of order (Formula presented.). This leads to the optimal smoothing rate (Formula presented.) for pointwise mean squared error (MSE) minimization. Simulation results confirm that the Bernstein estimator is especially effective in challenging boundary-dominated settings, notably for U-shaped beta models and for larger positive t values. Overall, Bernstein polynomial smoothing provides a simple, theoretically justified, and practically effective tool for nonparametric MGF estimation on compact supports.

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• MakaleYalnız künye2026Scopus SJRQ2/r/munzur/1350
  A 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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• MakaleYalnız künye2026Scopus SJRQ3/r/munzur/1250
  A multi-parametric unit distribution via Bernstein polynomials

  Susam, Selim Orhun

  This study develops the method of constructing a multi-parametric distribution on the unit interval using Bernstein polynomials and investigates its key statistical properties. The performance of the proposed distribution’s parameter estimators was evaluated by Monte Carlo simulation under both maximum-likelihood (MLE) and minimum-distance frameworks, with the MLE approach generally yielding lower estimation error. A real data example illustrates the advantages of the construction method. The results reveal that the Bernstein distribution provides superior goodness-of-fit to the dataset compared with competing models.

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/913
  Risk Assessment via Hazard Scenario in Cereal Production Under Rising CO

  Susam, Selim Orhun; Peker, Ayse Esra; Hudaverdi, Burcu

  Global food security has been seriously threatened by air pollution and climate change. The impact of CO on crop output is complicated because of its interactions with temperature, nutrient availability, and other pollutants, even though it is necessary for photosynthesis. This study develops a copula-based hazard modeling framework to assess how increasing CO concentrations affect cereal production risk across multiple crops. By integrating hazard scenario analysis with localized dependence measures, we estimate both failure probabilities and dynamic associations between CO emissions and cereal yields. A novel localized version of Kendall’s implemented via a Bernstein copula approximation, enables flexible modeling of heterogeneous and nonlinear dependence structures. As the relationship between CO emissions and crop-specific yields changes in both density and direction, it may indicate asymmetries or nonlinearities that are missed by measures of global dependence. Empirical results reveal distinct crop-specific responses. While CO levels enhance yields for rice, millet, and sugar beet, they reduce or destabilize production for rye and lentil, indicating asymmetric sensitivities. Failure probabilities increase with the time horizon, reflecting the accumulation of climate-related production risk. The findings emphasize the significance of integrating probabilistic hazard modeling into climate-resilient agricultural planning and food security policies, as crop-specific risk exposure has increased in recent time periods.

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  AtıfWoS1·OpenAlex1

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• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/632
  Bernstein-smoothed estimation and bootstrap inference for the lower-tail Spearman's rho curve

  Lyu, Guanjie; Ouimet, Frédéric; Susam, Selim O.

  This paper studies a Bernstein-smoothed plug-in estimator for the lower-tail Spearman's rho curve, a rank-based measure of local concordance defined through a normalized copula integral over the lower-left square (Formula presented.). The estimator applies the lower-tail Spearman functional to the empirical Bernstein copula and introduces a degree parameter that controls finite-sample regularization. The contribution is target-specific estimation and inference for the lower-tail Spearman's rho curve rather than a new general-purpose copula estimator. We establish uniform strong consistency on compact intervals away from zero whenever (Formula presented.). Under a target-specific integrated Bernstein-bias condition and (Formula presented.), we derive functional weak convergence with the same first-order Gaussian limit as the empirical copula-based estimator; this degree regime includes (Formula presented.). For inference, we justify a smoothed beta bootstrap based on sampling from the empirical beta copula and construct pointwise confidence intervals from the absolute centred bootstrap root. Monte Carlo experiments for the Farlie–Gumbel–Morgenstern, Gaussian, Clayton, and Frank copulas assess pointwise coverage over the threshold grid and show that Bernstein smoothing generally reduces integrated variance and often lowers mean integrated squared error under weak to moderate dependence. Additional common-sample experiments compare the proposed estimator with the empirical beta and empirical checkerboard Bernstein copula estimators in terms of pointwise error, integrated error, computation time, selected degrees, numerical stability, and pointwise coverage. A sensitivity analysis shows that (Formula presented.) is a simple theoretically admissible default that avoids the most severe oversmoothing. A descriptive application to the Loss–ALAE insurance claims data illustrates how the lower-tail Spearman's rho curve can reveal local dependence features that are not captured by the global Spearman correlation.

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• MakaleYalnız künye2025Scopus SJRQ2/r/munzur/2288
  Copula-based conditional reliability with application to rocket motor data

  Hudaverdi, Burcu; Susam, Selim Orhun; Chesneau, Christophe

  In this paper, we construct conditional reliability in the context of stress-strength models, taking into account the relationship between applied stress and strength. The Bernstein copula approximation is used to calculate the conditional reliability values. Real data from a rocket motor experiment are considered to evaluate the conditional reliability strength of a motor case and the required stress endurance for the motor.

  AtıfScopus4·WoS5·OpenAlex5

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• MakaleYalnız künye2025Scopus SJRQ4/r/munzur/2244
  Spearman 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.

  AtıfScopus2·WoS2·OpenAlex2

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• MakaleYalnız künye2025Scopus SJRQ2/r/munzur/2147
  A flexible parameter estimation method for the Farlie-Gumbel-Morgenstern copula: a simulation study

  Susam, Selim Orhun

  This research presents a minimum distance method based on the Bernstein empirical copula process for estimating the dependence parameter of the Farlie-Gumbel-Morgenstein copula. Also, this study examines its performance through a comprehensive Monte Carlo simulation study. The findings in this paper demonstrate that selecting the optimal degree of the Bernstein empirical copula has a significant impact on the performance of the new estimation method. The proposed estimator achieves optimal performance in terms of mean squared error (MSE) for the parameter range (Formula presented.) in small sample sizes, while outside this range, its performance varies depending on the degree of the Bernstein empirical copula. Finally, we apply the new method to real data for illustrative purposes.

  AtıfScopus3·WoS4·OpenAlex3

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• MakaleYalnız künye2024Scopus SJRQ2/r/munzur/2246
  Stress–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.

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  AtıfScopus2·WoS3·OpenAlex3

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• MakaleYalnız künye2024Scopus SJRQ3/r/munzur/1933
  On the Construction of a Semiparametric Family of Bivariate Copulas Using the Opposite Diagonal Section of Copulas

  Susam, Selim Orhun; Chesneau, Christophe

  In this article, a new semiparametric family of bivariate copulas is proposed and studied. The copulas of this family innovate in their construction; they are based on an original univariate generating function involving the opposite diagonal section of intermediary copulas. Several of their crucial properties are examined. In the practical part, we use them to estimate the reliability parameter in a stress-strength-dependent setting, including an estimation approach based on the Bernstein copula. Finally, their applicability and good performance are confirmed using a data set related to the logarithmic concentrations of two chemical elements in water samples.

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