Some improved and alternative imputation methods for finite population mean in presence of missing information

Singh, G. N.; Pandey, Awadhesh K.; Sharma, A. K.

doi:10.1080/03610926.2020.1713375

Cited by 17 publications

(3 citation statements)

References 20 publications

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“…Utilizing the auxiliary information on both occasions, Feng andZou (1997), Birader and, Singh and Karna (2009), Singh and Priyanka (2010), Singh and Sharma (2014), Singh et al (2015), Singh et al (2018), Singh et al (2020) have proposed varieties of estimators of population mean on current (second) occasions in two occasions successive sampling.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Estimators and Effective Estimation Procedures for Population Variance under Missing at Random Data in Successive Sampling

Anup Kumar Sharma,

Alok Kumar Singh

2023

JMASM

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This paper explores some potent estimation procedures of population variance under missing at random situations in two-occasion successive sampling. Information on auxiliary variables has been involved in supporting for effective estimation of some chain-type exponential and regression estimators under the assumption of sampling units on which information of study variables cannot be obtained due to missing at random. The non-respondents follow the Binomial type of distribution in the estimation. The proposed estimators are compared with the competent estimators under a complete response of sample units. The Empirical studies support theoretical results in the present probability of non-respondent. Results have been interpreted and suitable recommendations are made for data practitioners.

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Section: Introductionmentioning

confidence: 99%

Enhanced Estimators and Effective Estimation Procedures for Population Variance under Missing at Random Data in Successive Sampling

Anup Kumar Sharma,

Alok Kumar Singh

2023

JMASM

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“…Imputation techniques in the survey literature are from [3,5,[14][15][16][17][18][19][20][21], among others. Some recent works in the area of imputation and estimation of population mean have been done in [22][23][24][25][26][27][28][29] and others.…”

Section: Introductionmentioning

confidence: 99%

An Exponential‐Cum‐Sine‐Type Hybrid Imputation Technique for Missing Data

Bhattacharyya

Singh

Jawa

et al. 2021

Computational Intelligence and Neuroscience

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In this study, a new exponential-cum-sine-type hybrid imputation technique has been proposed to handle missing data when conducting surveys. The properties of the corresponding point estimator for population mean have been examined in terms of bias and mean square errors. An extensive simulation study using data generated from normal, Poisson, and Gamma distributions has been conducted to evaluate how the proposed estimator performs in comparison to several contemporary estimators. The results have been summarized, and discussion regarding real-life applications of the estimator follows.

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“…Some popular imputation methods include mean, regression, hot deck, cold deck, and nearest neighbor methods of imputation, among others. Imputation techniques in the survey literature are due to [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27], among others.…”

Section: Introductionmentioning

confidence: 99%

Some Classes of Logarithmic‐Type Imputation Techniques for Handling Missing Data

Pandey

Singh

Bhattacharyya

et al. 2021

Computational Intelligence and Neuroscience

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In this manuscript, three new classes of log-type imputation techniques have been proposed to handle missing data when conducting surveys. The corresponding classes of point estimators have been derived for estimating the population mean. Their properties (Mean Square Errors and bias) have been studied. An extensive simulation study using data generated from normal, Poisson, and Gamma distributions, as well as real dataset, has been conducted to evaluate how the proposed estimator performs in comparison to several contemporary estimators. The results have been summarized, and discussion regarding real-life applications of the estimator follows.

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Some improved and alternative imputation methods for finite population mean in presence of missing information

Cited by 17 publications

References 20 publications

Enhanced Estimators and Effective Estimation Procedures for Population Variance under Missing at Random Data in Successive Sampling

Enhanced Estimators and Effective Estimation Procedures for Population Variance under Missing at Random Data in Successive Sampling

An Exponential‐Cum‐Sine‐Type Hybrid Imputation Technique for Missing Data

Some Classes of Logarithmic‐Type Imputation Techniques for Handling Missing Data

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