Volatile Compounds Selection via Quantile Correlation and Composite Quantile Correlation: A Whiting Case Study

The freshness and quality indices of whiting (Merlangius merlangus) influenced by a large number of chemical volatile compounds, are here analyzed in order to select the most relevant compounds as predictors for these indices. The selection process was performed by means of recent statistical variable selection methods, namely robust model-free feature screening, based on quantile correlation and composite quantile correlation. On the one hand, compounds 2-Methyl-1-butanol, 3-Methyl-1-butanol, Ethanol, Trimethylamine, 3-Methyl butanal, 2-Methyl-1-propanol, Ethylacetate, 1-Butanol and 2,3-Butanedione were identified as major predictors for the freshness index and on the other hand, compounds 3-Methyl-1-butanol, 2-Methyl-1- butanol, Ethanol, 3-Methyl butanal, 3-Hydroxy-2-butanone, 1-Butanol, 2,3-Butane- dione, 3-Pentanol, 3-Pentanone and 2-Methyl-1-propanol were identified as major predictors for the quality index.

Feature Screening for High-Dimensional Survival Data via Censored Quantile Correlation

This paper proposes a new sure independence screening procedure for high-dimensional survival data based on censored quantile correlation(CQC).This framework has two distinctive features:1)Via incorporating a weighting scheme,our metric is a natural extension of quantile correlation(QC),considered by Li(2015),to handle high-dimensional survival data;2)The proposed method not only is robust against outliers,but also can discover the nonlinear relationship between independent variables and censored dependent variable.Additionally,the proposed method enjoys the sure screening property under certain technical conditions.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors.

Asymmetric nexus between commercial policies and consumption‑based carbon emissions:new evidence from Pakistan

The current study extends the previous literature by exploring the effects of a newly discovered driver,i.e.,import taxes(as a proxy for commercial policies),on the consumption-based carbon emissions(CCO2e)for 1990Q1-2017Q4.For empirical analysis,several tests and methods,including Augmented Dickey–Fuller unit root test,Zivot–Andrews unit root test,asymmetric cointegration bound testing approach,non-linear ARDL,Wald-test,Granger causality test and wavelet quantile correlation(WQC)method are utilized.Furthermore,NARDL technique estimates reveal that contractionary commercial policy enhances the environmental quality by disrupting the detrimental effects of CCO2e.However,expansionary commercial policy escalates the environmental pollution by boosting the carbon emissions.Also,the exports and the renewable energy improve the ecological quality;however,GDP deteriorates the atmospheric quality by increasing the CCO2e.Besides,WQC method and the trivariate Granger causality test are deployed to confirm the robustness of the results.Based on the findings,some crucial policies are also recommended for sustainable and green development in Pakistan.