Browsing by Author "Ashutosh Ashutosh"

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Design based study for adjusting non-response while estimating population mean
(Springer, 2024) Ajeet Kumar Singh; Ashutosh Ashutosh; Jayendra Kumar Singh
In the present study, we proposed a class of estimators for estimating a finite population mean in the presence of non-response to the study variable. We set out to investigate their properties under the polynomial regression model (PRM) modelling approach. Some of the special cases of the class were discussed separately to show how some non-response versions of the existing estimators can be generated and studied from the general class. The comparison of the model-based mean square errors of the estimators under different settings of the considered model was illustrated with the help of some empirical data. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2024.
Simulation analysis of non-respondent information in context of small domain
(Elsevier Ltd, 2024) Ashutosh Ashutosh; Marius Stefan; Piyush Kant Rai; Walid Emam; Soofia Iftikhar; Malik Muhammad Anas
In the real-world, there are various situations when all units are not accessible of the respondent called unit non-response. The effect of unit non-response is a tricky matter for estimating the total number of unit. The present work highlights the interest about subpopulations (domains) in two affairs: i. if domains total of the supportive information is accessible ii. if domains total of the supportive variable does not access. The government needs to be introducing the actual facilities in these small domains. The supportive information is used to find out the estimate of the non respondent information and to apply this information for desired domains. Sometimes, it has been found that the accessible auxiliary variable for the domains might be positive shape. Therefore, it develops an appropriate model that has positive skewness. The present context highlighted the indirect method using a power-based estimation with calibration approach. By combining power based estimation and calibration technique, it is possible to obtain more accurate estimates for intended small domains. Even the supportive information is positively biased. This approach helps us in mitigating the effect of non-respondent and improving the overall reliability of the estimators. The simulation was conducted for different sizes 70 and 90 when nonresponse variable in the study variable. The results show that investigated power-based estimate provides better option over relevant exponential, ratio, and generalized regression estimators for intended domains. © 2024 The Authors
Simulation study of small domain with calibration approach
(John Wiley and Sons Ltd, 2022) Ashutosh Ashutosh; Usman Shahzad; Nadia H. Al-Noor; Piyush Kant Rai
Some of the flexible models are available in the statistics literature are exponential and power distribution. They are very helpful in the modeling for situations when the shape and distribution of interested variables are positively skewed. We are presenting the calibration based estimates of domain total for exponential and the power model under such prevailing circumstances. Two different situations are considered: (i) domain total of the auxiliary variable is known and (ii) domain total of the auxiliary variable is unknown. To overcome the difficulties, to get calibration weights under second situation, two phase sampling technique is utilized. The Newton–Raphson method of approximation is used to estimate the Lagrange's multiplier. In addition, the efficiency of the proposed estimator has been given with support of the proposed weights under shortest Chi-square distance function. A numerical value based on the simulation study in terms of absolute relative bias and simulated relative standard error have been given using a real data of the Sweden municipality 1984 for comparison of the estimators. Finding shows that the proposed power function based calibration estimator is more efficient than the existing calibration based estimators of the domain total in both the situations. © 2022 John Wiley & Sons, Ltd.