In case of sensitive research, estimation of mean is a major concern in survey studies and regression estimators utilizing traditional regression coefficient are the most favored choices for it. Recently, Zaman and Bulut [ 2018 Zaman, T. , and H. Bulut. 2018. Modified ratio estimators using robust regression methods. Communications in Statistics - Theory and Methods 48:2039–48. doi: 10.1080/03610926.2018.1441419. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar] . Modified ratio estimators using robust regression methods. Communications in Statistics - Theory and Methods, DOI:10.1080/03610926.2018.1441419] have developed a class of ratio-type estimators for the mean estimation of non-sensitive variable utilizing robust regression coefficients. In this paper, we have generalized their family of estimators to the case where the study variable refers to sensitive issues which produces measurement errors due to non-responses and/or untruthful reporting. These errors may be reduced by enhancing respondent cooperation through scrambled response methods that mask the true value of the sensitive variable. Hence, two scrambled response models Pollock and Bek ( 1976 Pollock, K. H. , and Y. Bek. 1976. A comparison of three randomized response models for quantitative data. Journal of the American Statistical Association 71 (356):884–6. doi: 10.1080/01621459.1976.10480963. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar] ), Bar-Lev, Bobovitch, and Boukai ( 2004 Bar-Lev, S. K. , E. Bobovitch, and B. Boukai. 2004. A note on randomized response models for quantitative data. Metrika 60 (3):255–60. doi: 10.1007/s001840300308. [Crossref], [Web of Science ®] , [Google Scholar] ) are discussed for the purposes of this article. We have also developed a class of robust-regression type estimators in case of sensitive research. Some estimators belonging to the class are shown and the mean square errors are determined. Theoretical and empirical illustration is done through real and artificial data sets for assessing the performance of adapted and proposed class.

Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single‐stage model (without denoised and decomposed), the hybrid two‐stage model (with denoised), and existing three‐stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three‐stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one‐stage and two‐stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition.

The current study aimed to explore some important insights into the breast cancer mortality (BCM) trends and projections among four Asian countries by using five advanced stochastic mortality models. BCM data over 28 years from 1990–2017 with ages 20–84 were retrieved from the Global Burden of Disease (GBD) Study 2017 for four Asian countries, namely, China, India, Pakistan, and Thailand. Five stochastic mortality models with the family of generalized age-period-cohort were implemented to find the present and future BCM trends in these four Asian countries. Based on Cairns-Blake-Dowd (CBD) model and Lee-Carter model (LCM), overall, results revealed that BCM increased with the passage of time. Aging factor was the most influential factor of elevated BCM in each Asian country under consideration. Projection of BCM showed that mortality rates might continue to grow with time, especially in older ages in each Asian country under study. The highest forecasted BCM rates were observed in Pakistan as compared to other countries. The obvious increase in BCM suggested that earlier tactics should be implemented to reduce the subsequent morbidity and mortality due to breast cancer. The last but not least, some additional tactics to mitigate the BCM in older ages must be adopted.

One of the most useful indicators of relative dispersion is the coefficient of variation. The characteristics of the coefficient of variation have contributed to its widespread use in most scientific and academic disciplines, with real life applications. The traditional estimators of the coefficient of variation are based on conventional moments; therefore, these are highly affected by the presence of extreme values. In this article, we develop some novel calibration-based coefficient of variation estimators for the study variable under double stratified random sampling (DSRS) using the robust features of linear (L and TL) moments, which offer appropriate coefficient of variation estimates. To evaluate the usefulness of the proposed estimators, a simulation study is performed by using three populations out of which one is based on the COVID-19 pandemic data set and the other two are based on apple fruit data sets. The relative efficiency of the proposed estimators with respect to the existing estimators has been calculated. The superiority of the suggested estimators over the existing estimators are clearly validated by using the real data sets.