L-Moments and calibration based variance estimators under double stratified random sampling scheme: an application of covid-19 pandemic

Coronavirus disease 2019 is the most crucial issue of all countries worldwide as it poses a threat and risk to people in many aspects such as health and economy.Since each country's development level, economy, and infrastructure differ, countries' struggle against COVID-19 varies. Therefore, understanding the COVID-19 risk levels of countries can be crucial in determining possible strategies to take specific measures for those at the highest risk. Also, determining the risk level of countries can be more critical than estimates, such as the number of cases and deaths, as the level of risk alone can be an informative indicator for all such issues. Unlike most studies, this study concentrates on evaluating and estimating the COVID-19 risk level of countries. This study proposes two families of multivariate exponential estimators using two auxiliary attributes. Theoretically, the mean square error (MSE) equations of all proposed exponential estimators are obtained and compared with existing estimators. Some exceptional cases of the multivariate exponential estimators are regarded and compared with MSE values of proposed multivariate exponential estimators. As a result, the multivariate exponential estimators provide more efficient results than all other estimators. These theoretical findings are supported by a numerical illustration using real dataset. K E Y W O R D SCOVID-19 risk, exponential estimator, mean square error, risk assessment, two auxiliary attributes INTRODUCTIONThe World Health Organization (WHO) declared on March 11, 2020, that coronavirus disease 2019 (COVID-19) could be considered a pandemic. 1Since then, COVID-19 has caused a global crisis affecting people's health, well-being and lifestyle, and the world economy. As of January 16, 2021, there have been 92,506,811 confirmed COVID-19 cases and 2,001,773 reported deaths globally. 2 However, the number of cases and deaths varies from country to country. The main reason for this may be that each country differs in population density, cultural habits, health services, protective measures, and infrastructure. 3Management and control of COVID-19 depend primarily on the health system of a country. 4 A robust health system plays a determining role in countries' preparedness and responses to pandemics. 5 In addition, socioeconomic factors are crucial in the spread of COVID-19. [6][7][8][9] Such parameters, health system, and socioeconomic vulnerability affect the risk level of countries. It is usual for COVID-19 to spread brutally in the least developed and most vulnerable countries (countries with the highest risk). 10 In this regard, evaluating and determining the COVID-19 risk level of countries can be crucial as it is an informative indicator for most issues, including the number of COVID-19 cases and mortality rates.

mentioning

confidence: 99%

A new exponential estimators for analysis of COVID‐19 risk

Zaman

Sağır

Şahin

2021

“…Similarly, the expressions of linear moments for

X

can be written using the same population and sample linear moments structure as for

Y

. Shahzad et al 12,13 provides more detail on L‐moments.…”

Section: Linear Moments and Calibration Based CV Estimatorsmentioning

confidence: 99%

“…Supplementary information might be utilized productively at the design/planning phase and at the estimation phase, or at both phases. Instances of supplementary information utilized at the estimation phase to expand the productivity of the appraisals of population characteristics incorporate, for example, the customary ratio, regression and relapse strategies and, the calibration method (Deville and Särndal 10 ; Koyuncu 11 ; Shahzad et al, 12,13 Özgül 14,15 ). In light of these encounters, and adapting calibration‐based methodology, we introduce in the current paper some novel estimators for the population CV of the variable of interest.…”

Section: Introductionmentioning

confidence: 99%

“…These moments provide outrageous outcomes in the presence of extreme (or outliers) values. For settling the issue of extreme values, a lot of research has been carried out, for mean estimation using ratio, regression, and robust regression techniques (Zaman and Bulut, 2,3 and Zaman et al 21 ), for variance estimation using ratio and regression strategies (Abid et al 22 ), for variance estimation using calibration technique (Shahzad et al 12,13 ). However, none of the work was carried out for the estimation of CV in presence of extreme events, which is one of the major RMD.…”

Section: Introductionmentioning

confidence: 99%

“…where 21 ), for variance estimation using ratio and regression strategies (Abid et al 22 ), for variance estimation using calibration technique (Shahzad et al 12,13 ). However, none of the work was carried out for the estimation of CV in presence of extreme events, which is one of the major RMD.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation of coefficient of variation using linear moments and calibration approach for nonsensitive and sensitive variables

Shahzad

Ahmad

Hanif

et al. 2022

Self Cite

Assessment of coefficient of variation (CV) is of major importance in numerous examinations. However, the appearance of extreme observations raises concerns about the outcomes of CV estimates based on conventional moments. So, motivated by some recent developments in finite sampling theory, we propose some new estimators of CV based on the properties of linear moments (L-moments and Trimmed L-moments), which are highly robust whenever outliers or extreme observations appear in a dataset.The proposed estimators are initially established on the premise that the variable of interest is nonsensitive which deals with the subjects that do not embarrass respondents when asked about them explicitly. These estimators are also applied to situations where the variable of interest is associated with sensitive issues that cause measurement errors resulting from nonresponse or unreliable reporting where such issues can be mitigated by increasing respondent participation by scrambled response models which obscure the true value of the sensitive variable. Four models are considered for this article: additive, multiplicative, mixed, and combined additive-multiplicative models. Finally, in both nonsensitive and sensitive settings, real-life data sets are employed to undertake simulation-based analysis. In all cases, the proposed estimators considerably increased efficiency.

Estimation of population mean under probability proportional to size sampling with and without measurement errors

Sinha

Khanna

2022

This research paper deals with the problem of estimating population mean of study variable using information of auxiliary variable under varying probability and measurement error. Motivated by Kadilar and Cingi, App Math Lett, 2006, 19, 75 and Zaman, Stat Trans‐New Ser, 2020, 21, 159, generalized ratio and product enhanced regressed exponential (ERE) estimators are proposed to estimate the population mean using different auxiliary parameters under the sampling of probability proportional to size (pps) and extended for the case of measurement error. Further, following the strategies of Naik and Gupta, J Ind Soc Agr Stat, 1996, 48, 151; Jhajj et al., Pak J Stat, 2006, 23, 1; Singh et al., Auxiliary Information and A Priori Values in Construction of Improved Estimators, Renaissance High Press, 2007; and Solanki and Singh, Chil J Stat, 2013, 4, 3, conventional ratio, product, regression, generalized and many other types of estimators have been adopted under pps sampling with and without measurement error. Up to the first order of large sample approximation, the bias and mean square error (MSE) of the suggested ERE and adopted estimators are derived and their properties have been studied. Comparisons have been made theoretically and empirically to comprehend the merits of recommended estimators over the conventional and adopted estimators.