This paper proposes an improved estimation method for the population coefficient of variation, which uses information on a single auxiliary variable. The authors derived the expressions for the mean squared error of the proposed estimators up to the first order of approximation. It was demonstrated that the estimators proposed by the authors are more efficient than the existing ones. The results of the study were validated by both empirical and simulation studies.
Synthetic estimators are known to produce estimates of population mean in areas where no sampled data are available, but such estimates are usually highly biased with invalid confidence statements. This paper presents a calibrated synthetic estimator of the population mean which addresses these problematic issues. Two known special cases of this estimator were obtained in the form of combined ratio and combined regression synthetic estimators, using selected tuning parameters under stratified sampling. In result, their biases and variance estimators were derived. The empirical demonstration of the usage involving the proposed calibrated estimators shows that they provide better estimates of the population mean than the existing estimators discussed in this study. In particular, the estimators were examined through simulation under three distributional assumptions, namely the normal, gamma and exponential distributions. The results show that they provide estimates of the mean displaying less relative bias and greater efficiency. Moreover, they prove more consistent than the existing classical synthetic estimator. The further evaluation carried out using the coefficient of variation provides additional confirmation of the calibrated estimator's advantage over the existing ones in relation to small area estimation.
This paper considers the problem of estimating the population mean Y of the study variate y using information on auxiliary variate x. We have suggested a generalized version of Bahl and Tuteja (1991) estimator and its properties are studied. It is found that asymptotic optimum estimator (AOE) in the proposed generalized version of Bahl and Tuteja (1991) estimator is biased. In some applications, biasedness of an estimator is disadvantageous. So applying the procedure of Singh and Singh (1993) we derived an almost unbiased version of AOE. A numerical illustration is given in the support of the present study.
This paper addresses the problem of an alternative approach to estimating the population mean of the study variable with the help of the auxiliary variable under stratified random sampling. The properties of the suggested estimator have been studied under large sample approximation. It has been demonstrated that the suggested estimator is more efficient than other considered estimators. To judge the merits of the proposed estimator, an empirical study has been carried out to support the present study.
The problem of estimation of finite population mean on the current occasion based on the samples selected over two occasions has been considered. In this paper, first a chain ratio-to-regression estimator was proposed to estimate the population mean on the current occasion in two-occasion successive (rotation) sampling using only the matched part and one auxiliary variable, which is available in both the occasions. The bias and mean square error of the proposed estimator is obtained. We proposed another estimator, which is a linear combination of the means of the matched and unmatched portion of the sample on the second occasion. The bias and mean square error of this combined estimator is also obtained. The optimum mean square error of this combined estimator was compared with (i) the optimum mean square error of the estimator proposed by Singh (2005) (ii) mean per unit estimator and (iii) combined estimator suggested by Cochran (1977) when no auxiliary information is used on any occasion. Comparisons are made both analytically as well as empirically by using real life data.
This paper advocates the improved separate ratio exponential estimator for population mean of the study variable y using the information based on auxiliary variable x in stratified random sampling. The bias and mean squared error (MSE) of the suggested estimator have been obtained upto the first degree of approximation. The theoretical and numerical comparisons are carried out to show the efficiency of the suggested estimator over sample mean estimator, usual separate ratio and separate product estimator.
Estimation of the population average in a finite population by means of sampling strategies dependent on an auxiliary variable highly correlated with a variable under study is considered. The sample is drawn with replacement on the basis of the probability distribution of an order statistic of the auxiliary variable. Observations of the variable under study are the values of the concomitant of the order statistic. The mean of the concomitant values is the estimator of a population mean of the variable under study. The expected value and the variance of the estimator are derived. The limit distributions of the considered estimators were considered. Finally, on the basis of simulation analysis, the accuracy of the estimator is considered.
Estimation of the population average in a finite and fixed population on the basis of the conditional simple random sampling design dependent on order statistics of the auxiliary variable is studied. The sampling scheme implementing the sampling design is proposed. The inclusion probabilities are derived. The well known Horvitz-Thompson statistic under the conditional simple random sampling designs is considered as the estimator of population mean. Moreover, it was shown that the Horvitz-Thompson estimator under some particular cases of the conditional simple random sampling design is more accurate than the ordinary mean from the simple random sample.
The paper deals with the problem of estimation of a domain means in a finite and fixed population. We assume that observations of a multidimensional auxiliary variable are known in the population. The proposed estimation strategy consists of the well known Horvitz-Thompson estimator and the non-simple sampling design dependent on a synthetic auxiliary variable whose observations are equal to the values of a depth function of the auxiliary variable distribution. The well known spherical and Mahalanobis depth functions are considered. A sampling design is proportionate to the maximal order statistic determined on the basis of the synthetic auxiliary variable observations in a simple sample drawn without replacement. A computer simulation analysis leads to the conclusion that the proposed estimation strategy is more accurate for domain means than the well known simple sample means.
Estimation of the population mean in a finite and fixed population on the basis of the conditional simple random sampling design dependent on order statistics (quantiles) of an auxiliary variable is considered. Properties of the well-known Horvitz-Thompson and ratio type estimators as well as the sample mean are taken into account under the conditional simple random sampling designs. The considered examples of empirical analysis lead to the conclusion that under some additional conditions the proposed estimation strategies based on the conditional simple random sample are usually more accurate than the mean from the simple random sample drawn without replacement.
In this paper the case of a conditional sampling design proportional to the sum of two order statistics is considered. Several strategies including the Horvitz-Thompson estimator and ratio-type estimators are discussed. The accuracy of these estimators is analyzed on the basis of computer simulation experiments.
Estimation of the population average in a finite population by means of sampling strategy dependent on the sample quantile of an auxiliary variables is considered. The sampling design is proportionate to the determinant of the matrix dependent on some quantiles of an auxiliary variables. The sampling scheme implementing the sampling design is proposed. The derived inclusion probabilities are applied to estimation the population mean using the well known Horvitz-Thompson estimator. Moreover, the regression estimator is defined as the function of the coefficient dependent on the quantiles of the auxiliary variables. The properties of this estimator under the above defined sampling design are studied. The considerations are supported by empirical examples.
PL
Problem oceny wartości średniej z wykorzystaniem danych o wszystkich wartościach cech pomocniczych jest rozważany. W tym celu znany estymator regresyjny zależny od wielu zmiennych pomocniczych jest wykorzystywany. W odróżnieniu od zwykłego podejścia znanego w metodzie reprezentacyjnej do oceny parametrów regresji są wykorzystywane kwantyle jednej ze zmiennych dodatkowych. Otrzymane na tym polu wyniki są adoptowane do konstrukcji predytorów wartości średniej w nadpopulacji. Wyprowadzono również wariancje różnych odmian proponowanych predykatorów.
Problem dotyczy oceny wartości średnie (globalnej) zmiennej w populacji ustalonej I skończonej. Zakład się, że z góry są znane w populacji wartości dodatniej zmiennej pomocniczej. Do estymacji użyto strategia kwantylowej zależnej m.in. od planu losowania proporcjonalnego do nieujemnej funkcji kwantyla z próby zmiennej pomocniczej. Ponadto, brano pod uwagę estymator Horvitza- Thompsona oraz estymator ilorazowy. Porównanie dokładności przeprowadzono na podstawie symulacji komputerowej.
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