Browsing by Author "Supriya Khare"

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Almost Unbiased Variance Estimators Under the Simultaneous Influence of Non-response and Measurement Errors
(Springer Science and Business Media Deutschland GmbH, 2022) Ahmed Audu; Rajesh Singh; Supriya Khare; N.S. Dauran
This paper addresses the problem of biases in variance estimators under the simultaneous influence of both measurement and non-response errors. Three classes of estimators are suggested and their properties are studied up to first order of approximation. Also, their efficiency conditions over some existing estimators are established. Resultant almost unbiased estimators of the proposed estimators are obtained and their properties are discussed. Theoretical results reveal that the suggested class of estimators under the measurement and non-response errors are more efficient. Results of simulation study also indicate superiority of suggested estimators over other estimators considered in the study. © 2022, Grace Scientific Publishing.
An exponential type estimator for finite population variance
(DAV College, 2019) Rajesh Singh; Sat Gupta; Supriya Khare
In this paper, we propose an exponential type estimator for finite population variance under simple random sampling without replacement using an auxiliary variable, which is highly correlated with the study variable. Mean square error of the proposed estimator is derived up to first order of approximation. Efficiency of the proposed estimator is compared with other existing estimators both theoretically and numerically. Results indicate that the proposed estimator is more efficient than the existing estimators considered here. © 2019 DAV College. All rights reserved.
Developing calibration estimators for population mean using robust measures of dispersion under stratified random sampling
(Glowny Urzad Statystyczny, 2021) Ahmed Audu; Rajesh Singh; Supriya Khare
In this paper, two modified, design-based calibration ratio-type estimators are presented. The suggested estimators were developed under stratified random sampling using information on an auxiliary variable in the form of robust statistical measures, including Gini's mean difference, Downton's method and probability weighted moments. The properties (biases and MSEs) of the proposed estimators are studied up to the terms of first-order approximation by means of Taylor's Series approximation. The theoretical results were supported by a simulation study conducted on four bivariate populations and generated using normal, chi-square, exponential and gamma populations. The results of the study indicate that the proposed calibration scheme is more precise than any of the others considered in this paper. © 2021 Glowny Urzad Statystyczny. All rights reserved.
Estimation of population mean for logarithmic observation under Poisson distributed study and auxiliary variates
(University of Salento, 2020) Supriya Khare; Rajesh Singh; Prayas Sharm
Use of auxiliary information is always suggested at the planning and estimation stage to make the estimators perform more eggiciently.Estimation using auxiliary information is common in sampling literature but using distribution of study and auxiliary information at the estimation stage is uncommon and found useful especially when dealing with rare variable. This study utilizes the auxiliary information and Poisson distributed variates for proposing the log-type estimator and another generalized estimator for fnite population mean under simple random sampling without replacement. The Mean Square Error expressions of the proposed estimators are obtained.It is revealed from empirical (point estimation and interval estimation) & theoretical study that use of log type estimators along with suitable auxiliary information for Poisson distributed variates excels the performance of estimators in terms of effciency. © Universitá del Salento
ESTIMATION OF POPULATION VARIANCE IN LOG - PRODUCT TYPE ESTIMATORS UNDER DOUBLE SAMPLING SCHEME
(River Publishers, 2019) Prabhakar Mishra; Rajesh Singh; Supriya Khare
It is experienced that auxiliary information when suitably incorporated yields more efficient and precise estimates. Mishra et al. (2017) have introduced a log type estimator for estimating unknown population mean using ancillary information in simple random sampling. Here we propose an improved log-product type estimator for population variance under double sampling. Properties of the estimators are studied both mathematically and numerically. © 2019 Journal of Reliability and Statistical Studies. All rights reserved.
EXPONENTIAL TYPE ESTIMATOR FOR MISSING DATA UNDER IMPUTATION TECHNIQUE
(Universidad de La Habana, 2022) Rajesh Singh; Prabhakar Mishra; Ahmed Audu; Supriya Khare
In this paper, we suggest an exponential type estimator for estimation of population mean for missing data under suggested imputation techniques. Family of proposed estimator is obtained for missing data. Expression for Bias and MSE’s are acquired in the form of population parameters up to the terms of first order of approximation. Theoretical results depict the superiority of proposed estimator and its family over other estimators. The empirical study in support of theoretical results is also included to verify the results numerically. © 2022 Universidad de La Habana. All rights reserved.
New regression-type compromised imputation class of estimators with known parameters of auxiliary variable
(Taylor and Francis Ltd., 2023) Ahmed Audu; Rajesh Singh; Supriya Khare
In this paper, we have proposed a regression-type compromised imputation methods free of unknown parameters. The properties (biases and MSEs) of the proposed class of estimators are derived up to first order approximation using Taylor series approach. Also, the conditions for which the proposed estimators are more efficient than other estimators considered in the study were established. Results of numerical illustration using both real and simulated data revealed that the proposed estimators are more efficient and practicable than exiting estimators considered in the study. © 2021 Taylor & Francis Group, LLC.
On the estimation of finite population variance for a mail survey design in the presence of non-response using new conventional and calibrated estimators
(Taylor and Francis Ltd., 2022) Ahmed Audu; Ran Vijay Kumar Singh; Olatunji Olawoyin Ishaq; Supriya Khare; Rajesh Singh; Adedayo Amos Adewara
In this paper, we proposed new conventional unbiased and calibration estimators for estimating finite population variance for a Mail Survey Design characterized by the presence of non-response in practice. The properties of the proposed estimators are studied theoretically and numerically. Empirical studies were conducted using two each of existing and simulated data to illustrate the performance of proposed estimators over existing ones. The results of both theoretical and numerical comparison depicted the superiority of proposed estimators for estimating population variance for a Mail Survey Design characterized by non-response. © 2022 Taylor & Francis Group, LLC.
On the utilization of known coefficient of variation and preliminary test of significance in the estimation of population mean
(DAV College, 2019) B.B. Khare; Utkarsh; Supriya Khare
In this paper, it is shown that an estimator of the population mean using known coefficient of variation and a preliminary test of significance for the variance 2 y 0 2 is found to be better than the usual regression estimator. © 2019 DAV College. All rights reserved.
The general classes of estimators for population mean under stratified two phase random sampling in the presence of non-response
(DAV College, 2020) Rajesh Singh; Supriya Khare; B.B. Khare; P.S. Jha
In this paper, we have proposed six classes of estimators for different cases of non-response for a heterogeneous population under the stratified random sampling using auxiliary information with unknown population mean. The properties of the proposed estimators have been examined and studied for fixed sample size and the members of the proposed classes are also defined. Also, a numerical study has been included in support of the enhanced efficiency of the proposed estimator. © 2020 DAV College. All rights reserved.