Linear regression estimation can be adversely affected by multicollinearity and outliers in both the response and predictor variables. Ridge regression addresses multicollinearity but remains sensitive to outliers, whereas robust M-estimation reduces the influence of response outliers without directly addressing multic...
Muhammad Junaid, I. S. Dar, Hasnain Iftikhar et al.· Stats· 0 citations
Foreign exchange rate forecasting is difficult, as its time series have complex nonlinear and nonstationary dynamics. To this end, the paper proposes a hybrid forecasting framework based on kernel regression decomposition that combines nonparametric trend extraction with linear and nonlinear forecasting models. In the...
Hasnain Iftikhar, Said Farooq Shah, F. Almuhayfith et al.· Symmetry· 0 citations
High-dimensional gene expression datasets in chemometric and biomedical research present significant challenges for machine learning because the number of genes greatly exceeds the number of available samples, increasing the risk of overfitting and reducing classification reliability. Existing gene selection methods ar...
Sehran Hassan, Alamgir, Hasnain Iftikhar et al.· Entropy· 0 citations
A novel hybrid linear/nonlinear forecasting model based on a kernel regression decomposition framework, which exploits nonparametric kernel smoothing methods and linear/nonlinear forecasting models to improve forecast accuracy and predictability in cryptocurrency markets.
Hasnain Iftikhar, Said Farooq Shah, Paulo Canas Rodrigues et al.· IEEE Access· 0 citations
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