KHALID UL ISLAM RATHER
Verified @gmail.com
SKUAST-JAMMU MAIN CAMPUS
RESEARCH, TEACHING, or OTHER INTERESTS
Statistics, Probability and Uncertainty, Statistics and Probability, Modeling and Simulation, Numerical Analysis
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Yashpal Singh Raghav, Abdullah Ali H. Ahmadini, Ali M. Mahnashi, and Khalid Ul Islam Rather
Elsevier BV
Khalid Ul Islam Rather, Tanveer Ahmad Tarray, Olumide Sunday Adesina, Adedayo Funmi Adedotun, Toluwalase Janet Akingbade, and Onuche G. Odekina
International Information and Engineering Technology Association
Etebong P. Clement, Idorenyin O. Etukudoh, Khalid UI Islam Rather, and Victoria M. Akpan
The Netherlands Press
This study proposed a new ratio estimator for estimating population mean in stratified double sampling using the principle of multivariate calibration weightings. The bias and Mean Square Error (MSE) expressions for the proposed estimator are obtained under large sample approximation. Analytical results showed that under certain prescribed conditions, the new estimator is more efficient than all related existing estimators under review. The relative performances of the new estimator with a corresponding Global Estimator were evaluated through a simulation study. Numerical and simulation results proved the dominance of the new estimator.
Khalid Ul İslam RATHER and Cem KADILAR
Gazi University Journal of Science
We propose a novel family of estimators for the population mean under non-response and obtain the MSE equation of the suggested estimator for each situation in theory. These theoretical conditions are applied to three popular data sets in literature and we see that the suggested estimators are more efficient than the traditional estimators, such as ratio, regression estimators, in Case 1; whereas, in Case 2, the suggested estimators are also more efficient than the Unal-Kadilar exponential estimators that are more efficient than the traditional estimators for the same data sets.
Eda Gizem Koçyiğit and Khalid Ul Islam Rather
Springer Science and Business Media LLC
Afshan Tabassum, M. Iqbal Jeelani, Manish Sharma, Khalid Ul Islam Rather, Imran Rashid, and Mansha Gul
Agricultural Research Communication Center
Background: In this study 11 height and diameter prediction models were fitted and evaluated for Himalayan Chir pine (Pinus roxburghii) in Jammu region of UT J and K (India). The data were collected from 50 permanent sample plots in uneven aged stands of Pinus roxburghii and total of 500 individual tree height and diameter measurement were used for this study. At initial stage all the models fitted resulted in significant coefficients, besides various selection criteria’s were also used to test the predictive performance of fitted models. The results of these criteria were generated from various libraries of R studio (version 3.5.1, 2018). The models were further cross validated and results revealed Manfred (MG) and Michaelis-Menten2 (MJ) models described the highest amount of height variation in terms of fit statistics and more crucially with lowest prediction error rate as compared to other models. Methods: The study was carried out in Jammu region of UT J and K (India). Data used in this study were collected on 50 permanent sample plots of 0.25 ha in size.In order to achieve stipulated objectives, Height diameter data on 500 trees from Jammu forest division was utilized in this study. Result: The summary statistics of height and diameter variable and the overall summary of the coefficients of various height and diameter models in Jammu forest division are presented. Almost all the coefficients of the statistical models were statistically significant which is an indication that fitted models are capturing the height diameter relationship an important aspect in context to biological realism.
Khalid Ul Islam Rather, Eda Gizem Koçyiğit, Ronald Onyango, and Cem Kadilar
Public Library of Science (PLoS)
In this article, a new robust ratio type estimator using the Uk’s redescending M-estimator is proposed for the estimation of the finite population mean in the simple random sampling (SRS) when there are outliers in the dataset. The mean square error (MSE) equation of the proposed estimator is obtained using the first order of approximation and it has been compared with the traditional ratio-type estimators in the literature, robust regression estimators, and other existing redescending M-estimators. A real-life data and simulation study are used to justify the efficiency of the proposed estimators. It has been shown that the proposed estimator is more efficient than other estimators in the literature on both simulation and real data studies.
Kuldeep Kumar Tiwari, Sandeep Bhougal, Sunil Kumar, and Khalid Ul Islam Rather
Springer Science and Business Media LLC
Rather Khalid, Eda Gizem KOÇYİĞİT, and Ceren ÜNAL
Pakistan Journal of Statistics and Operation Research
In this study, we adapted the families of estimators from Ünal and Kadilar (2021) using the exponential function for the population mean in case of non-response for simple random sampling for the estimation of the mean of the population with the RSS (ranked set sampling) method. The equations for the MSE and the bias of the adapted estimators are obtained for RSS and it in theory shows that the proposed estimator is additional efficient than the present RSS mean estimators in the literature. In addition, we support these theoretical results with real COVID-19 real data and conjointly the simulation studies with different distributions and parameters. As a result of the study, it was observed that the efficiency of the proposed estimator was better than the other estimators.