scholarly journals Use of Nonconventional Dispersion Measures to Improve the Efficiency of Ratio-Type Estimators of Variance in the Presence of Outliers

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 16 ◽  
Author(s):  
Farah Naz ◽  
Tahir Nawaz ◽  
Tianxiao Pang ◽  
Muhammad Abid
Keyword(s):  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Auxiliary Variables ◽  
Absolute Deviation ◽  
Gini Mean Difference ◽  
Skewness Coefficient ◽  
Probability Weighted Moment ◽  
Dispersion Measures ◽  
Coefficient Of Skewness

The use of auxiliary information in survey sampling to enhance the efficiency of the estimators of population parameters is a common phenomenon. Generally, the ratio and regression estimators are developed by using the known information on conventional parameters of the auxiliary variables, such as variance, coefficient of variation, coefficient of skewness, coefficient of kurtosis, or correlation between the study and auxiliary variable. The efficiency of these estimators is dubious in the presence of outliers in the data and a nonsymmetrical population. This study presents improved variance estimators under simple random sampling without replacement with the assumption that the information on some nonconventional dispersion measures of the auxiliary variable is readily available. These auxiliary variables can be the inter-decile range, sample inter-quartile range, probability-weighted moment estimator, Gini mean difference estimator, Downton’s estimator, median absolute deviation from the median, and so forth. The algebraic expressions for the bias and mean square error of the proposed estimators are obtained and the efficiency conditions are derived to compare with the existing estimators. The percentage relative efficiencies are used to numerically compare the results of the proposed estimators with the existing estimators by using real datasets, indicating the supremacy of the suggested estimators.

scholarly journals On Estimation of Distribution Function Using Dual Auxiliary Information under Nonresponse Using Simple Random Sampling

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Saddam Hussain ◽  
Mi Zichuan ◽  
Sardar Hussain ◽  
Anum Iftikhar ◽  
Muhammad Asif ◽  
...  
Keyword(s):  
Random Sampling ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Distribution Functions ◽  
Supplementary Information ◽  
Auxiliary Variables ◽  
Estimation Of Distribution

In this paper, we proposed two new families of estimators using the supplementary information on the auxiliary variable and exponential function for the population distribution functions in case of nonresponse under simple random sampling. The estimations are done in two nonresponse scenarios. These are nonresponse on study variable and nonresponse on both study and auxiliary variables. As we have highlighted above that two new families of estimators are proposed, in the first family, the mean was used, while in the second family, ranks were used as auxiliary variables. Expression of biases and mean squared error of the proposed and existing estimators are obtained up to the first order of approximation. The performances of the proposed and existing estimators are compared theoretically. On these theoretical comparisons, we demonstrate that the proposed families of estimators are better in performance than the existing estimators available in the literature, under the obtained conditions. Furthermore, these theoretical findings are braced numerically by an empirical study offering the proposed relative efficiencies of the proposed families of estimators.

scholarly journals Estimation of finite population distribution function with dual use of auxiliary information under non-response

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243584
Author(s):  
Sardar Hussain ◽  
Sohaib Ahmad ◽  
Sohail Akhtar ◽  
Amara Javed ◽  
Uzma Yasmeen
Keyword(s):  
Distribution Function ◽  
Finite Population ◽  
Population Distribution ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Distribution Functions ◽  
Auxiliary Variables ◽  
Sample Mean ◽  
Sample Distribution

In this paper, we propose two new families of estimators for estimating the finite population distribution function in the presence of non-response under simple random sampling. The proposed estimators require information on the sample distribution functions of the study and auxiliary variables, and additional information on either sample mean or ranks of the auxiliary variable. We considered two situations of non-response (i) non-response on both study and auxiliary variables, (ii) non-response occurs only on the study variable. The performance of the proposed estimators are compared with the existing estimators available in the literature, both theoretically and numerically. It is also observed that proposed estimators are more precise than the adapted distribution function estimators in terms of the percentage relative efficiency.

scholarly journals Estimation of finite population mean using dual auxiliary variable for non-response using simple random sampling

2021 ◽  
Vol 7 (3) ◽  
pp. 4592-4613
Author(s):  
Sohaib Ahmad ◽  
◽  
Sardar Hussain ◽  
Muhammad Aamir ◽  
Faridoon Khan ◽  
...  
Keyword(s):  
Random Sampling ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Sample Mean ◽  
Population Mean ◽  
New Family ◽  
Variable Bias ◽  
Mean Square Errors ◽  
The Given

<abstract><p>This paper addresses the issue of estimating the population mean for non-response using simple random sampling. A new family of estimators is proposed for estimating the population mean with auxiliary information on the sample mean and the rank of the auxiliary variable. Bias and mean square errors of existing and proposed estimators are obtained using the first order of measurement. Theoretical comparisons are made of the performance of the proposed and existing estimators. We show that the proposed family of estimators is more efficient than existing estimators in the literature under the given constraints using these theoretical comparisons.</p></abstract>

scholarly journals The Estimation of Finite Population Variance Under Stratified Sampling Technique

2021 ◽  
Author(s):  
Uzma Yasmeen ◽  
Muhammad Noor-ul-Amin
Keyword(s):  
Finite Population ◽  
Auxiliary Information ◽  
Sampling Technique ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
Auxiliary Variables ◽  
Population Variance ◽  
Variance Estimators ◽  
Usual Estimator ◽  
Study Population

The efficiency of the study variable can be improved by incorporating the information from the known auxiliary variables. Usually two techniques ratio and regression estimation are used with the help of auxiliary information in different approaches to acquire the high precision of the estimators. Considering the very heterogeneous population to get the size of the sample it may be originating impossible to get a sufficiently accurate and precise estimate by taking the simple random sampling technique from the complete population. Occasionally taking sample issue may differ significantly in different part of the entire population. For example, under study population consists of people living in apartments, own homes, hospitals and prisons or people living in plain regions and hill regions so in such situations the stratified sampling is one of the most commonly used approach to get a representative sample in survey sampling from different cross units of the population. The present study is set out on the recommendation of generalized variance estimators for finite population variance incorporating stratified sampling scheme with the information of single and two transformed auxiliary variables. The expressions of bias and mean square error (MSE) are obtained for the advised exponential type estimators. The conditions are obtained for which the anticipated estimators are better than the usual estimator. An empirical and simulation study is conducted to prove the superiority of the recommended estimator.

RATIO ESTIMATION OF THE POPULATION MEAN USING AUXILIARY INFORMATION UNDER THE OPTIMAL SAMPLING DESIGN

2020 ◽  
pp. 1-12
Author(s):  
Chunxian Long ◽  
Wangxue Chen ◽  
Rui Yang ◽  
Dongsen Yao
Keyword(s):  
Sampling Design ◽  
Auxiliary Information ◽  
Cost Effective ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Ranked Set Sampling ◽  
Study Variable ◽  
Population Mean ◽  
Optimal Sampling Design ◽  
Extreme Ranked Set Sampling

Cost-effective sampling design is a problem of major concern in some experiments especially when the measurement of the characteristic of interest is costly or painful or time-consuming. In this article, we investigate ratio-type estimators of the population mean of the study variable, involving either the first or the third quartile of the auxiliary variable, using ranked set sampling (RSS) and extreme ranked set sampling (ERSS) schemes. The properties of the estimators are obtained. The estimators in RSS and ERSS are compared to their counterparts in simple random sampling (SRS) for normal data. The numerical results show that the estimators in RSS and ERSS are significantly more efficient than their counterparts in SRS.

scholarly journals Two-stage sampling in the estimation of growth parameters and percentile norms: sample weights versus auxiliary variable estimation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
George Vamvakas ◽  
Courtenay Norbury ◽  
Andrew Pickles
Keyword(s):  
Parameter Estimation ◽  
Structural Equation ◽  
Growth Parameters ◽  
Growth Models ◽  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Auxiliary Variables ◽  
Growth Data ◽  
Sampling Weights ◽  
Two Stage

Abstract Background The use of auxiliary variables with maximum likelihood parameter estimation for surveys that miss data by design is not a widespread approach, despite its documented improved efficiency over traditional approaches that deploy sampling weights. Although efficiency gains from the use of Normally distributed auxiliary variables in a model have been recorded in the literature, little is known about the effects of non-Normal auxiliary variables in the parameter estimation. Methods We simulate growth data to mimic SCALES, a two-stage survey of language development with a screening phase (stage one) for which data are observed for the whole sample and an intensive assessments phase (stage two), for which data are observed for a sub-sample, selected using stratified random sampling. In the simulation, we allow a fully observed Poisson distributed stratification criterion to be correlated with the partially observed model responses and develop five generalised structural equation growth models that host the auxiliary information from this criterion. We compare these models with each other and with a weighted growth model in terms of bias, efficiency, and coverage. We finally apply our best performing model to SCALES data and show how to obtain growth parameters and population norms. Results Parameter estimation from a model that incorporates a non-Normal auxiliary variable is unbiased and more efficient than its weighted counterpart. The auxiliary variable method is capable of producing efficient population percentile norms and velocities. Conclusions The deployment of a fully observed variable that dominates the selection of the sample and correlates strongly with the incomplete variable of interest appears beneficial for the estimation process.

scholarly journals Finite Population Distribution Function Estimation Using Auxiliary Information Under Simple Random Sampling

2021 ◽  
Vol 3 (1) ◽  
pp. 29-38
Author(s):  
Sohaib Ahmad ◽  
Sardar Hussain ◽  
Sohail Ahmad
Keyword(s):  
Distribution Function ◽  
Random Sampling ◽  
Finite Population ◽  
Population Distribution ◽  
Auxiliary Information ◽  
Simple Random Sampling ◽  
Auxiliary Variable ◽  
Supplementary Information ◽  
Function Estimation ◽  
Distribution Function Estimation

In this paper, a new estimator for estimating the finite population distribution function(DF) are propose using supplementary information on the DF of the auxiliary variable under simple random sampling. A comparative study is conducted to compare, theoretically and numerically, the adapted distribution function estimators of Cochran (1940), Murthy (1967), Bahl and Tuteja (1991), Rao (1991), Singh et al. (2009) and Grover and Kaur (2014) with the proposed estimators. It is found that the proposed estimators always perform better than the adapted estimators in terms of MSE and percentage relative efficiency.

scholarly journals Datos agregados para corregir los sesgos de no respuesta y de cobertura en encuestas

2020 ◽  
pp. 39
Author(s):  
Pablo Cabrera-Álvarez
Keyword(s):  
Auxiliary Information ◽  
Auxiliary Variable ◽  
Aggregate Data ◽  
Nonresponse Bias ◽  
Individual Data ◽  
Auxiliary Variables ◽  
Coverage Bias ◽  
El Sistema ◽  
Coverage Problems ◽  
No Respuesta

En las últimas décadas la incidencia creciente de los sesgos de no respuesta y cobertura en las encuestas han puesto en entredicho la capacidad de inferir los resultados a la población. Una forma extendida de corregir los sesgos de no respuesta y cobertura en las encuestas es el uso de ponderaciones que equilibran la muestra final de entrevistados. La construcción de ponderaciones requiere información auxiliar, totales poblacionales que estén disponibles para los que responden y para los que no cooperan. En este trabajo, a partir de simulaciones estadísticas, se comprueba la capacidad de la información agregada para corregir el sesgo de no respuesta. Para ello se comparan el ajuste con datos individuales y el sistema de datos agregados, dando como resultado que el uso de datos agregados puede ser útil si se cumplen tres requisitos: 1) la variable estimada está agrupada, 2) la variable estimada y la auxiliar están correlacionadas y 3) la probabilidad de completar la encuesta está relacionada con la variable auxiliar.In the last decades the effect of nonresponse and coverage bias in surveys have questioned the ability of inferring the results to the population. An extended procedure used to correct nonresponse and coverage problems is the use of weights to balance the sample of respondents. However auxiliary information available for respondents and nonrespondents is required to compute weights. In this paper statistical simulations are used to test the potential of aggregate data to correct nonresponse bias. This research compares individual data adjustments to the use of auxiliary aggregate data. The results show the use of aggregate data can improve survey representativity if three requirements are met: 1) the dependent variable is grouped, 2) the dependent and auxiliary variables are correlated and 3) the auxiliary variable is correlated with response propensities.

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