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- MakaleAçık Erişim2026Scopus SJRQ2Risk 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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İncele - MakaleAçık Erişim2026Scopus SJRQ2Bernstein-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.
- MakaleYalnız künye2024Scopus SJRQ3On 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.
AtıfScopus1WoS1OpenAlex1İncele - MakaleYalnız künye2023Scopus SJRQ2A weighted independence test based on smooth estimation of Kendall distribution
Susam, Selim Orhun; Hudaverdi, Burcu
In this study, we introduce a nonparametric test of independence which is based on a weighted Cramér-von Mises distance of the Bernstein estimate of the Kendall distribution function. We examine the power and the size of the new weighted test and, we compare it with the non-weighted nonparametric tests of independence through a Monte Carlo simulation study. The simulation results indicate that both of the choices of the weights and the polynomial degree has a significant effect on the test power. Finally, the procedure is applied to a dataset on chemical elements in water samples for the purpose of illustration.
AtıfScopus1WoS1OpenAlex1İncele - MakaleAçık Erişim2022TAIL DEPENDENCE ESTIMATION BASED ON SMOOTH ESTIMATION OF DIAGONAL SECTION
Susam, Selim Orhun
This paper is mainly developed around the diagonal section which is strongly related to tail dependence coefficients as defined in Nelsen [19]. Hence, we propose a flexible method for estimating tail dependence coefficients based on the new smooth estimation of the diagonal section based on the Bernstein polynomial approximation. To assess the performance of the new estimators we conduct the Monte-Carlo simulation study. As a result of the simulation study, both estimators perform satisfactory performance. Also, the estimation methods are illustrated by real data examples.
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İncele - MakaleAçık Erişim2022Scopus SJRQ3A multi-parameter Generalized Farlie-Gumbel-Morgenstern bivariate copula family via Bernstein polynomial
Susam, Selim Orhun
In this paper, we are proposing a flexible method for constructing a bivariate generalized Farlie-Gumbel-Morgenstern (G-FGM) copula family. The method is mainly developed around the function ϕ(t) (t ∈ [0, 1]), where ϕ is the generator of the G-FGM copula. The proposed construction method has useful advantages. The first of which is the direct relationship between the ϕ function and Kendall’s tau. The second advantage is the possibility of constructing a multi-parameter G-FGM copula which allows us to better harmonize empirical instruction with the model. The construction method is illustrated by three real data examples.
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