Abstract Bias correction methods are used to adjust simulations from global and regional climate models use them in informed decision‐making. Here we introduce a semi‐parametric quantile mapping (SPQM) method bias‐correct daily precipitation. This uses parametric probability distribution describe observations an empirical for simulations. Bias‐correction techniques typically the bias between ob...

To provide insight into patient-level disease dynamics from data collected at irregular time intervals, this work extends applications of semi-parametric clustering for temporal mining. In the semi-parametric clustering framework, Markovian models provide useful parametric assumptions for modeling temporal dynamics, and a non-parametric method is used to cluster the temporal abstractions instea...

AIM For the purpose of cost modeling, the semi-parametric single-index two-part model was utilized in the paper. Furthermore, as functional gastrointestinal diseases which are well-known as common causes of illness among the society people in terms of both the number of patients and prevalence in a specific time interval, this research estimated the average cost of functional gastrointestinal d...

reference curves are useful tools to monitor children’s growth status and can promote growth velocity in infants. in this regard, various parametric and semi-parametric methods are frequently used in the last decades. in the present paper, nonparametric quintile regression method is used as a powerful and applicable methodology to estimate height curves and normal values of height-for-age in ch...

Nowadays, one of the most important topics in risk management of banks, financial, and credit institutions is credit risk management. In this research, the researchers used survival analytic methods for credit risk modeling in terms of the conditional distribution function of default time. As a practical task, the authors considered the reward credit portfolio of Tose'e Ta'avon Bank of Guilan P...

A semi parametric profil~ likelihood method is proposed for estimation of sample selection models. The method is a two step scoring semi parametric estimation procedure based on index formulation and kernel density estimation. Under some regularity conditions, the estimator is asymptotically normal. This method can be applied to estimation of general sample selection models with multiple regime...

In this paper, the author designs an efficient method for imputing iteratively missing target values with semiparametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in e...

A semi-parametric algorithm for identification of Hammerstein systems in the presence of correlated noise is proposed. The procedure is based on the non-parametric kernel regression estimator and the standard least squares. The advantages of the method in comparison with the standard non-parametric approach are discussed. Limit properties of the proposed estimator are studied, and the simulatio...