Optimality of ratio and regression type estimators using dual of auxiliary variable under non response

Pandey, Awadhesh K.; Usman, M.; Singh, G. N.

doi:10.1016/j.aej.2021.03.031

Cited by 11 publications

(12 citation statements)

References 11 publications

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“…Multiple logistic regression model was used to evaluate the association between anxiety and various factors during COVID-19 [16] , [17] , [18] , [19] . Models’ coefficients were estimated by utilizing different methods such as maximum likelihood method, Bayesian method and least square method [9] , [20] , [21] , [22] , [23] .…”

Section: Introductionmentioning

confidence: 99%

Logistic regression analysis for studying the impact of home quarantine on psychological health during COVID-19 in Saudi Arabia

Jawa¹

2022

Alexandria Engineering Journal

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During the Coronavirus disease (COVID-19) period, the world witnessed a complete closure of all aspects of social life, and people must go for home quarantine to limit the spread of Corona virus disease. This study aims to use multiple logistic regression model (MLR) to identify the impact of home quarantine on psychological stability of individuals and its relationship to various factors. Maximum likelihood is used to estimate the parameters of MLR. Hosmer-Lemeshow method and Wald statistic are calculated to assess the significance of MLR. Cross sectional study was carried out and MLR was fitted to the collected data. This study applied to 846 residents of Makkah region during the COVID-19 period. High percentage of respondents felt psychological stability during the period of home quarantine. Logistic regression model showed that education level and psychological disorders during home quarantine were the significant risk factors for psychological stability during COVID-19. In addition, the positive impact of the home quarantine that people became more attention to their health, more interested to self-development and the families become close to each other. On the other hand, people on private employee or unemployed had psychological disorders more than others due to home quarantine and decrease their income.

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Section: Introductionmentioning

confidence: 99%

Logistic regression analysis for studying the impact of home quarantine on psychological health during COVID-19 in Saudi Arabia

Jawa¹

2022

Alexandria Engineering Journal

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“…Further, Grover and Kaur 9 proceeded by formulating generalized family of exponent‐based estimators encompassing numerous existing main stream estimators as member of the resultant class. The appreciation of these groundbreaking contributions can be witnessed through streams of research efforts targeting the variants of the afore‐mentioned estimation schemes, see, for example, Haq et al, 10 Muneer et al, 11 Irfan et al, 12 Zaman and Toksoy, 13 and Pandey et al 14 …”

Section: Introductionmentioning

confidence: 99%

“…member of the resultant class. The appreciation of these groundbreaking contributions can be witnessed through streams of research efforts targeting the variants of the afore-mentioned estimation schemes, see, for example, Haq et al, 10 Muneer et al, 11 Irfan et al, 12 Zaman and Toksoy, 13 and Pandey et al 14 Nowadays, the dual use of auxiliary information under the argument of maximum use of available information to support the estimation procedures is gaining momentum. Haq et al 10 notable contribution proposing the employment of ranks of auxiliary variable along with usual norm of considering mean can be considered as a mile stone in this regard.…”

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confidence: 99%

Empirical distribution function based dual use of auxiliary information for the improved estimation of finite population mean

Hussain¹,

Ullah

Cheema

et al. 2022

Concurrency and Computation

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This research primarily aims at the development of a new estimation scheme exploiting the argument of dual use of auxiliary information. The objectives are obtained by materializing the new family of estimators, where the dual use of Supplementary Information is substantiated with the launch of the empirical distribution function of the auxiliary variable. The comparative performance evaluation of the newly devised formation is enumerated with respect to the most efficient method, to the best of our knowledge till to date, of Haq et al. along with other promising families of Hussain and Haq and Grover and Kaur. The elaborative account of contemporary advents of the newly proposed family are documented throughout the article.

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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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Optimality of ratio and regression type estimators using dual of auxiliary variable under non response

Cited by 11 publications

References 11 publications

Logistic regression analysis for studying the impact of home quarantine on psychological health during COVID-19 in Saudi Arabia

Logistic regression analysis for studying the impact of home quarantine on psychological health during COVID-19 in Saudi Arabia

Empirical distribution function based dual use of auxiliary information for the improved estimation of finite population mean

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

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