Browsing by Author "Bhushan, S"

Now showing 1 - 8 of 8
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    Design based synthetic imputation methods for domain mean
    (2024) Bhushan, S; Kumar, A; Pokhrel, R
    In real life, situations may arise when the available data are insufcient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic efort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to frst order approximation. The efciency conditions are determined and a thorough simulation study is carried out using artifcially generated data sets. An application is included with real data that further supports this study.
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    Design based synthetic imputation methods for domain mean
    (2024) Bhushan, S; Kumar, A; Phokhrel, R; Bakr, M; Mekiso, G
    In real life, situations may arise when the available data are insufcient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic efort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to frst order approximation. The efciency conditions are determined and a thorough simulation study is carried out using artifcially generated data sets. An application is included with real data that further supports this study.
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    Efficient imputation methods in case of measurement errors
    (2024-02) Kumar, A; Bhushan, S; Shukla, S; Bakr, M
    This manuscript develops few efficient difference and ratio kinds of imputations to handle the situation of missing observations given that these observations are polluted by the measurement errors (ME). The mean square errors of the developed imputations are studied to the primary degree approximation by adopting Taylor series expansion. The proposed imputations are equated with the latest existing imputations presented in the literature. The execution of the proposed imputations is assessed by utilizing a broad empirical study utilizing some real and hypothetically created populations. Appropriate remarks are made for sampling respondents regarding practical applications
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    Enhanced direct and synthetic estimators for domain mean with simulation and applications
    (2024-07) Kumar, A; Bhushan, S; Pokhrel, R; Emam, W; Tashkandy, Y; Khan, M
    This article considers the issue of domain mean estimation utilizing bivariate auxiliary information based enhanced direct and synthetic logarithmic type estimators under simple random sampling (SRS). The expressions of mean square error (MSE) of the proposed estimators are provided to the 1๐‘ ๐‘ก order approximation. The efficiency criteria are derived to exhibit the dominance of the proposed estimators. To exemplify the theoretical results, a simulation study is conducted on a hypothetically drawn trivariate normal population from ๐‘… programming language. Some applications of the suggested methods are also provided by analyzing the actual data from the municipalities of Sweden and acreage of paddy crop in the Mohanlal Ganj tehsil of the Indian state of Uttar Pradesh. The findings of the simulation and real data application exhibit that the proposed direct and synthetic logarithmic estimators dominate the conventional direct and synthetic mean, ratio, and logarithmic estimators in terms of least MSE and highest percent relative efficiency.
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    Logarithmic Type Direct and Synthetic Estimators for Domain Mean Using Simple Random Sampling
    (2024-01) Bhushan, S; Kumar, A; Pokhrel, R
    In this article, we propose logarithmic type direct and synthetic estima tors for the estimation of domain mean under simple random sampling. The properties such as bias and mean square error of the proposed direct and synthetic estimators are obtained up to rst order approximation. The e - ciency conditions are obtained under which the proposed direct and synthetic estimators outperform their conventional counterparts. The performance of the proposed direct and synthetic estimators is examined with the help of comprehensive computational study using real and arti cially drawn popu lations. Some appropriate suggestions are also provided to the surveyors.
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    Novel imputation methods under stratified simple random sampling
    (2024-04) Kumar, A; Bhushan, S; Mustafa, M; Aldallal, R; Aljohani, H; Almulhim, F
    This paper addresses some classes of combined and separate imputation methods (CSIMs) of the population mean under stratified simple random sampling (SSRS) along with their characteristics. To the best of our knowledge, these imputation methods (IMs) have yet not been studied by any author under SSRS, hence these IMs are called โ€˜novelโ€™. In addition, the existing CSIMs are distinguished as the members of the suggested CSIMs, respectively. The theoretical conditions under which the proposed IMs perform better are obtained by comparing the proposed IMs with the existing IMs. To validate the theoretical findings, the numerical and simulation studies are conducted on real and artificial populations, respectively.
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    Novel logarithmic imputation procedures using multi auxiliary information under ranked set sampling
    (2024) Kumar, A; Bhushan, S; Emam, W; Tashkandy, Y; Khan, M
    Ranked set sampling (RSS) is known to increase the efciency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study.
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    Small area estimation using design based direct and synthetic logarithmic estimators
    (2024-05) Kumar, A; Bhushan, S; Pokhrel, R; Emam, W
    In this article, we propose some direct and synthetic logarithmic estimators for estimating the domain mean of small area based on a simple random sampling design. The mean square error expressions of the proposed direct and synthetic estimators are obtained to first order approximation. The efficiency conditions are obtained under which the proposed direct and synthetic estimators dominate their conventional aspirants. The performances of the suggested direct and synthetic logarithmic estimators are examined by a comprehensive simulation study carried out on some artificially drawn symmetric and asymmetric populations. Furthermore, a real data application of the suggested methods is also provided as a case study using the paddy crop acreage data for small domains, where small domains are the revenue inspector circles (RIC) in Mohanlalganj tehsil, Uttar Pradesh, India.