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The aim of this paper is to develop more effective methods for estimating population means in sample surveys using auxiliary attributes. To achieve this goal, we introduce a modified version of the estimators proposed by Koyuncu (2013b) and Shahzad et al. (2019), as well as a new class of estimators. We derive expressions for the bias and mean squared error of these new estimators up to the first degree of approximation. Our results show that the suggested classes of estimators perform better than other existing methods, with the lowest mean squared error under optimal conditions. We also conduct an empirical investigation to support our findings. [ABSTRACT FROM AUTHOR]

In this paper we have considered the problem of estimating the population mean Y of the study variable y using information on auxiliary variable x in stratified random sampling. A class of estimators has been proposed. The bias and mean squared error have been obtained up to first degree of approximation. Optimum condition is obtained in which the proposed class of estimators has least mean squared error. We have also compared the proposed class of estimators with some existing estimators. An empirical study is carried out in support of the present study. [ABSTRACT FROM AUTHOR]

This article introduces an upgraded family of dual estimators for the population mean of main variable using some known supplementary information. To construct the family of estimators, a suitable transformation on the study variable as well as auxiliary variable has been used. The sampling features of the proposed class of estimators have been studied up to first order under large sample approximation. Several existing estimators have been observed and found as particular members of the proposed family of estimators. The optimum values of the characterizing scalars of the suggested family of estimators are obtained through the method of maxima–minima. The least value of the Mean Squared Error (MSE) of the proposed class of estimators is also obtained for these optimal values of the characterizing constants. The proposed family of estimators is compared theoretically with the other competing estimators of population mean. To judge the performance of the suggested family of estimators over others, numerical illustration has also been given. The estimator with minimum MSE is recommended for practical utility in different areas of applications. The results show the suggested estimator performs better than the competing estimators, therefore it may be used in different applications. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient stratified randomized response model based on Chang et al.'s (2004) model. We have obtained the variance of the proposed estimator of πs, the proportion of the respondents in the population belonging to a sensitive group, under proportional and Neyman allocations. It is shown that the estimator based on the proposed model is more efficient than the Chang et al.'s (2004) estimator under both proportional as well as Neyman allocations, Hong et al.'s (1994) estimator and Kim and Warde's (2004) estimator. Numerical illustration and pictorial representation are given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper advocates the problem of estimating the population mean Ȳ of the study variable y using information on two auxiliary variables (x, z). We have suggested a class of estimators for population mean Ȳ of the study variable y based on the knowledge of the population means (X, Z ) of auxiliary variables (x, z), respectively. The bias and mean squared error of the proposed class estimators are obtained under large sample approximation. We have obtained the optimum conditions at which the suggested class of estimators is more efficient than the usual unbiased estimator y , st ratio and product estimators and the one recently proposed by Tailor et al. (2012). An empirical study is carried out in the favour of present study. [ABSTRACT FROM AUTHOR]

This paper suggests a two-parameter ratio-product-ratio type exponential estimator for a finite population mean in simple random sampling without replacement (SRSWOR) following the methodology in the studies of Singh and Espejo (2003) and Chami et al (2012). The bias and mean squared error of the suggested estimator are obtained to the first degree of approximation. The conditions are obtained in which suggested estimator is more efficient than the sample mean, classical ratio and product estimators, ratio-type and product type exponential estimators. An empirical study is given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper an estimator of the finite population mean using auxiliary information in sample surveys has been proposed. The bias and mean squared error are obtained under large sample approximation. It has been shown that the proposed estimator performs better than some recently published estimators. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimating the population varianceS2yof the study variableyusing the auxiliary information in sample surveys. We have suggested the (i) chain ratio-type estimator (on the lines of Kadilar and Cingi (2003)), (ii) chain ratio-ratio-type exponential estimator and their generalized version [on the lines of Singh and Pal (2015)] and studied their properties under large sample approximation. Conditions are obtained under which the proposed estimators are more efficient than usual unbiased estimators2yand Isaki (1893) ratio estimator. Improved version of the suggested class of estimators is also given along with its properties. An empirical study is carried out in support of the present study. [ABSTRACT FROM PUBLISHER]

In this paper, we have considered an estimation of the population totalYof the study variabley, making use of information on an auxiliary variablex. A class of estimators for the population totalYusing transformation on both the variables study as well as auxiliary has been suggested based on the probability proportional to size with replacement (PPSWR). In addition to many the usualPPSestimator, Reddy and Rao's (1977) estimator and Srivenkataramana and Tracy's (1979, 1984, 1986) estimators are shown to be members of the proposed class of estimators. The variance of the proposed class of estimators has been obtained. In particular, the properties of 75 estimators based on different known population parameters of the study as well as auxiliary variables have been derived from the proposed class of estimators. In support of the present study, numerical illustrations are given. [ABSTRACT FROM AUTHOR]

The present paper suggests an interesting and useful ramification of the unrelated randomized response model due to Pal and Singh (2012) [A new unrelated question randomized response model.Statistics46 (1), 99–109] that can be used for any sampling scheme. We have shown theoretically and numerically that the proposed model is more efficient than Pal and Singh (2012) model. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of estimating the population varianceS2yof the study variableyusing auxiliary information in sample surveys. We have suggested a class of estimators of the population varianceS2yof the study variableywhen the population varianceS2xof the auxiliary variablexis known. Asymptotic expressions of bias and mean squared error (MSE) of the proposed class of estimators have been obtained. Asymptotic optimum estimators in the proposed class of estimators have also been identified along with its MSE formula. A comparison has been provided. We have further provided the double sampling version of the proposed class of estimators. The properties of the double sampling version have been provided under large sample approximation. In addition, we support the present study with aid of a numerical illustration. [ABSTRACT FROM AUTHOR]

In the present article, we have proposed some classes of estimators based on transformed auxiliary variable. The biases and mean squared errors (MSEs) of the proposed estimators have been obtained. The proposed estimators have been compared with simple mean estimator when there is no matching and the optimum estimator, which is a combination of the means of the matched and unmatched portion of the sample at the second occasion. Optimum replacement policy and the efficiency of the proposed estimators have been discussed. Theoretical results are well supported with an empirical study. [ABSTRACT FROM AUTHOR]

This paper intends to put emphasis on the role of two auxiliary variables on both the occasions to improve the precision of estimates at current (second)occasion in two-occasion successive sampling. There are different situations when (i) the information on the single auxiliary variablez1is readily available on both the occasions and positively correlated with the study variabley, (ii) the information on the single auxiliary variablez2is readily available on both the occasions and negatively correlated with the study variabley, (iii) the information on two auxiliary variables (z1,z2) are available on both the occasions, with one auxiliary variablez1is positively correlated and the other negatively correlated with the study variabley; these have been discussed through proposing three estimators (one in each situation) of the population meanon the current (second) occasion and analyzing their properties. The properties of the proposed estimators have been studied and compared with the sample mean estimator when there is no matching from the previous occasion and traditional successive sampling estimator, which is a linear combination of the means of the matched and unmatched portion of the sample at the current (second) occasion. Optimal replacement policy is discussed. Empirical studies are carried out to show the domination of the recommended estimators. [ABSTRACT FROM AUTHOR]

This paper suggests a new stratified randomized response model based on Kuk's [Biometrika (1990), 77, 2, pp.436–438] model that has Neyman allocation and considerable gain in precision. It has been identified that the stratified randomized response models due to Kim and Warde (2004), Kim and Elam's (2005), and Kim and Elam's (2007) are members of the proposed model. It is shown that the proposed model is more efficient than Kuk's (1990) model both theoretically and empirically. The results of this paper are also extended in the situation when trials are repeated. [ABSTRACT FROM AUTHOR]

The crux of this article is to estimate the mean of the number of persons possessing a rare sensitive attribute based on the Mangat (1991) randomization device by utilizing the Poisson distribution in simple random sampling and stratified sampling. Properties of the proposed randomized response (RR) model have been studied along with recommendations. It is also shown that the proposed model is more efficient than that of Land et al. (2011) in simple random sampling and that of Lee et al. (2013) in stratified random sampling when the proportion of persons possessing a rare unrelated attribute is known. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

This article suggests the class of estimators of population mean of study variable using various parameters related to an auxiliary variable with its properties in simple random sampling. It has been identified that the some existing estimator/classes of estimators are members of suggested class. It has been found theoretically as well as empirically that the suggested class is better than the existing methods. [ABSTRACT FROM AUTHOR]

Taking clue from the pioneer work of Singh and Sedory (2013), we have suggested a new stratified randomized response (RR) model. The properties of the suggested stratified RR model have been studied under proportional and “Neyman” allocations. The study has been also carried out in the presence of crude prior estimates. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

This article considers the problem of estimating the population mean on the current (second) occasion using multi-auxiliary information in successive sampling over two occasions. A general class of estimators is proposed for estimating population mean on the current occasion and expressions for bias and mean square error for these estimators are obtained up to first degree of approximation. The minimum variance bound estimator in the proposed class is discussed. Many popular estimators have been shown to belong to this class. Optimum replacement policy is also discussed. Finally, the superiority of the proposed class of estimators over multivariate version of chain type ratio estimator envisaged by Singh (2005) is established empirically. [ABSTRACT FROM AUTHOR]

The crux of this paper is to estimate the mean of the number of persons possessing a rare sensitive attribute based on the Mangat (1992) randomization device by utilizing the Poisson distribution in survey sampling. It is shown that the proposed model is more efficient than Land et al. (2011) when the proportion of persons possessing a rare unrelated attribute is known. Properties of the proposed randomized response model have been studied along with recommendations. We have also extended the proposed model to stratified random sampling on the lines of Lee et al. (2013). It has been also shown that the proposed estimator is better than Lee et al.'s (2013) estimator. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of estimation of the population mean on the current (second) occasion in two-occasion successive sampling. Utilizing the readily available information on several auxiliary variables on both occasions and the information on the study variable from the previous occasion, an estimation procedure of the population mean on the current occasion has been proposed. Theoretical properties of the proposed estimator have been investigated. Optimum replacement policy to the proposed estimator has been discussed. The proposed estimator has been compared empirically with the sample mean estimator, when there is no matching and the optimum estimator which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion. Appropriate recommendations have been made for practical applications. [ABSTRACT FROM AUTHOR]

In this article, a new randomized response model has been proposed. The proposed model is found to be more efficient than the randomized response models studied by Singh (2010). The relative efficiency of the proposed model has been studied with respect to the Singh (2010) model. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this article, a new “Partial” randomized response model has been proposed. Its properties are studied both theoretically and empirically. The proposed model is proved to be more efficient than the randomized response models studied by Eichhorn and Hayre (1983) and the “Partial” randomized response model. [ABSTRACT FROM AUTHOR]

This paper considers the use of stratified random sampling with proportional as well as Neyman allocations to unrelated question randomized response strategy. It has been shown that, for the prior information given, our new model is more efficient in terms of variance (in the case of completely truthful reporting) and mean square error (in case of less than completely truthful reporting). Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population mean on the current occasion in two occasion successive sampling. Based on all the readily available information from first and second occasions, a class of estimators is proposed with its properties. It is identified that the estimator recently suggested by Singh and Homa (Journal of Statistical Theory and Practice, 7: 1, 146–155, 2013) is a member of the suggested class of estimators. The correct expression of the mean squared error/variance of the Singh and Homa (2013) estimator is given. The superiority of the suggested class of estimators is discussed with the sample mean estimator when there is no matching, the best combined estimator given in Cochran (1977, p.346) and Singh and Homa (2013) estimator. Optimum replacement policy has been discussed. Numerical illustration is given in support of the present study. [ABSTRACT FROM PUBLISHER]

In this paper, we have proposed a class of estimators of finite population variance using known values of parameters related to an auxiliary variable such as quartiles and its properties are studied in simple random sampling. The suggested class of ratio-type estimators has been compared with the usual unbiased, ratio estimators and the class of ratio-type estimators due to Singhet al.[Improved estimation of finite population variance using quartiles, Istatistik – J. Turkish Stat. Assoc. 6(3) (2013), pp. 166–121] and Solankiet al.[Improved ratio-type estimators of finite population variance using quartiles, Hacettepe J. Math. Stat. 44(3) (2015), pp. 747–754]. An empirical study is also carried out to judge the merits of the proposed estimator over other existing estimators of population variance using natural data set. It is found that the proposed class of ratio-type estimators ‘’ is superior to the usual unbiased estimatorand the estimators recently proposed by Singhet al.[Improved estimation of finite population variance using quartiles, Istatistik – J. Turkish Stat. Assoc. 6(3) (2013), pp. 166–121] and Solankiet al.[Improved ratio-type estimators of finite population variance using quartiles, Hacettepe J. Math. Stat. 44(3) (2015), pp. 747–754]. [ABSTRACT FROM AUTHOR]

In this article, we have considered the problem of estimation of population variance on current (second) occasion in two occasion successive (rotation) sampling. A class of estimators of population variance has been proposed and its asymptotic properties have been discussed. The proposed class of estimators is compared with the sample variance estimator when there is no matching from the previous occasion and the Singh et al. (2013) estimator. Optimum replacement policy is discussed. It has been shown that the suggested estimator is more efficient than the Singh et al. (2013) estimator and a usual unbiased estimator when there is no matching. An empirical study is carried out in support of the present study. [ABSTRACT FROM PUBLISHER]

We suggested the class of estimators of the population mean with its bias and mean square error. It has been shown that the suggested class is more efficient than the usual unbiased, ratio, product and regression estimators and estimators due to Bahl and Tuteja (1991), Singh et al. (2009), and Upadhyaya et al. (2011). In addition an empirical study also carried out to and founded that the members of suggested family also have improvement over Grover and Kaur (2011) and Shabbir and Gupta (2011) classes. Two-phase (double) sampling version of the proposed class was also given. [ABSTRACT FROM PUBLISHER]

This paper proposes an alternative two-stage stratified randomized response model based on Tracy and Osahan (1999) model that has an optimal allocation and large gain in precision. It is also shown that the proposed model is more efficient than Kim and Warde (2004) and Kim and Elam (2005) stratified randomized response models under the conditions presented in both the cases of completely truthful reporting and that of not completely truthful reporting by the respondents. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM PUBLISHER]

In this article, we propose a generalized ratio-cum-product type exponential estimator for estimating population mean in stratified random sampling. Asymptotic expression of the bias and mean squared error of the proposed estimator are obtained. Asymptotic optimum estimator in the proposed estimator has been obtained with its mean squared error formula. Conditions under which the proposed estimator is more efficient than usual unbiased estimator, combined ratio and product type estimators, Singh et al. (2008) estimators and Tailor and Chouhan (2014) estimator are obtained. An empirical study has also been carried out. [ABSTRACT FROM PUBLISHER]

This paper discusses the problem of estimating the finite population variance using auxiliary information in presence of measurement errors. We have suggested a class of estimators and its properties are studied under large sample approximation. It has been shown that the usual unbiased estimator and the estimators due to Sharma and Singh [A generalized class of estimators for finite population variance in presence of measurement errors, Journal of Modern Applied Statistical Methods, (2013), 12(2), 231-241.] are members of the proposed class of estimators. An alternative expression of the mean squared error of one the estimator due to Sharma and Singh [A generalized class of estimators for finite population variance in presence of measurement errors, Journal of Modern Applied Statistical Methods, (2013), 12(2), 231-241.] is also provided. The relative performance of various estimators has been examined through an empirical study. [ABSTRACT FROM AUTHOR]

The article suggests a class of estimators of population mean in stratified random sampling using auxiliary information with its properties. In addition, various known estimators/classes of estimators are identified as members of the suggested class. It has been shown that the suggested class of estimators under optimum condition performs better than the usual unbiased, usual combined ratio, usual combined regression, Kadilar and Cingi (2005), Singh and Vishwakarma (2006) estimators and the members belonging to the classes of estimators envisaged by Kadilar and Cingi (2003), Singh, Tailor et al. (2008), Singh et al. (2009), Singh and Vishwakarma (2010) and Koyuncu and Kadilar (2010). [ABSTRACT FROM AUTHOR]

In this article we suggest some improved version of estimators of scale parameter of Morgenstern-type bivariate uniform distribution (MTBUD) based on the observations made on the units of the ranked set sampling regarding the study variableYwhich is correlated with the auxiliary variableX, when (X,Y) follows a MTBUD. We also suggest some linear shrinkage estimators of scale parameter of Morgenstern type bivariate uniform distribution (MTBUD). Efficiency comparisons are also made in this work. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of estimating the population mean of the study variable y using information on an auxiliary variable x. A class of Exponential-Type Estimators has been suggested along with its properties under large sample approximation. It is identified that the usual Unbiased Estimator, Product-Type and Ratio-Type Exponential Estimators are members of the proposed class of Exponential-Type Estimators. It has been shown that the proposed class of Exponential-Type Estimators is more efficient than the usual Unbiased Estimator and some existing Estimators. An empirical study is carried out in support of the present study. [ABSTRACT FROM AUTHOR]

This paper suggested an alternative ratio-cum-product type class of estimators of population mean using exponentiation method in simple random sampling. Approximate bias and mean squared error formulae of the suggested class of estimators have been obtained up to the first order of approximation. Asymptotic optimum estimator in the suggested class of estimators has been obtained with its mean squared error formula. Regions of preferences have been obtained under which the suggested class of estimators has been better than the usual unbiased, ratio and product estimators and the estimators according to Singh and Agnihotri (2008). Some examples are cited with numerical study. [ABSTRACT FROM PUBLISHER]

In this paper, an improved new additive model has been proposed. The proposed model is found to be more efficient than the randomized response models studied by Gjestvang and Singh (2009) and Singh (2010). The relative efficiency of the proposed model has been studied with respect to the Gjestvang and Singh (2009) and Singh (2010) model. It is found that the envisaged model is superior to those additive models earlier considered by Gjestvang and Singh (2009) and Singh (2010).Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this article, some new randomized response models have been proposed. Properties of the proposed randomized response models have been studied. The proposed models are found to be more efficient than the randomized response models studied by Himmelfarb and Edgell [1], Gjestvang and Singh [2] and Singh [3] under certain realistic conditions. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

The crux of this paper is to estimate the mean of the number of persons possessing a rare sensitive attribute based on the Singh et al. [1] randomization device by utilizing the Poisson distribution in stratified sampling. This study also deals with the extension of the estimation reported by Singh and Tarray [2] using a Poisson distribution and an unrelated question randomized response model reported in Singh et al. [1]. In stratified sampling, the estimators are proposed when the parameter of the rare unrelated attribute is known and also when it is unknown. It is shown that the proposed models are more efficient than the model given by Lee et al. [3] in both cases, that is, when the proportion of persons possessing a rare unrelated attribute is known and that when it is unknown. When the sizes of the stratified populations are not given, other estimators are suggested using stratified double sampling. Properties of the proposed randomized response model are studied and recommendations are made. [ABSTRACT FROM AUTHOR]

In this paper, a simple and obvious procedure is presented that allows to estimate the population proportion π possessing sensitive attribute using simple random sampling with replacement (SRSWR). In addition to T, the probability that a respondent truthfully states that he or she bears a sensitive character when experienced in a direct response survey. An efficiency comparison is carried out to investigate in the performance of the proposed method. It is found that the proposed strategy is more efficient than Warner's (1965) as well as Huang's (2004) randomized response techniques under some realistic conditions. Numerical illustrations and graphical representations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

This article addresses the problem of estimating the population mean in stratified random sampling using the information of an auxiliary variable. A class of estimators for population mean is defined with its properties under large sample approximation. In particular, various classes of estimators are identified as particular member of the suggested class. It has been shown that the proposed class of estimators is better than usual unbiased estimator, usual combined ratio estimator, usual product estimator, usual regression estimator and Koyuncu and Kadilar (2009) class of estimators. The results have been illustrated through an empirical study. [ABSTRACT FROM PUBLISHER]

This article addresses the problem of the estimation of the population meanof the study variable y using auxiliary variable x in stratified random sampling. Classes of ratio and product exponential type estimators are proposed. The biases and mean squared errors of the suggested estimators are obtained upto the first order of approximation. Conditions are obtained under which the proposed estimators are better than usual unbiased estimator, conventional combined ratio and product estimators and Singh et al. (2008) estimators. Asymptotic optimum estimators (AOEs) in the proposed classes of estimators are identified alongwith their mean squared errors formulae. Estimators based on estimated optimum values are obtained with their mean squared errors expressions. An empirical study has been carried out to examine the merits of the suggested estimators over others. [ABSTRACT FROM AUTHOR]

In this paper, we suggest a class of estimators for estimating the population meanȲof the study variableYusing information onX̄, the population mean of the auxiliary variableXusing ranked set sampling envisaged by McIntyre [A method of unbiased selective sampling using ranked sets, Aust. J. Agric. Res. 3 (1952), pp. 385–390] and developed by Takahasi and Wakimoto [On unbiased estimates of the population mean based on the sample stratified by means of ordering, Ann. Inst. Statist. Math. 20 (1968), pp. 1–31]. The estimator reported by Kadilaret al. [Ratio estimator for the population mean using ranked set sampling, Statist. Papers 50 (2009), pp. 301–309] is identified as a member of the proposed class of estimators. The bias and the mean-squared error (MSE) of the proposed class of estimators are obtained. An asymptotically optimum estimator in the class is identified with its MSE formulae. To judge the merits of the suggested class of estimators over others, an empirical study is carried out. [ABSTRACT FROM AUTHOR]

Abstract: This paper defines a general procedure for estimating the population mean of the study variate based on double sampling for stratification in presence of multi-auxiliary information. Classes of combined and separate estimators have been suggested and their properties are studied under large sample approximation. A class of unstratified double sampling estimators is also proposed with its properties. Asymptotic optimum estimators in the classes are identified with their approximate variance formulae. Further the proposed classes of estimators are compared with the corresponding class of estimators based on un-stratified double sampling. All findings are encouraging and support the soundness of the proposed procedure for mean estimation. [Copyright &y& Elsevier]

Abstract: This paper addresses the problem of estimating the population mean with known population proportion of an auxiliary variable. A class of estimators is defined which includes the estimators recently proposed by Shabbir and Gupta (2007) and Abd-Elfattah et al. (2010) . The usual unbiased estimator and Naik and Gupta (1996) estimator are also the member of the proposed class of the estimators. The bias and mean square error (MSE) expressions of the proposed class are obtained up to first order of approximation. Asymptotically optimum estimator (AOE) in the class of estimators is identified alongwith its mean square error formula. The correct MSE and minimum MSE expressions of Shabbir and Gupta (2007) estimator are also given. It has been shown that the proposed class of estimators is more efficient than the usual unbiased estimator, usual linear regression estimator and estimators/classes of estimators due to Naik and Gupta (1996) , Jhajj et al. (2008) , Shabbir and Gupta (2007) estimator, Singh et al. (2008) and Abd-Elfattah et al. (2010) . The double sampling version of the proposed class of estimators is proposed alongwith its properties. Numerical illustrations are given in support of the present study. [Copyright &y& Elsevier]

This paper deals with the problem of estimating the finite population mean using auxiliary information in simple random sampling. Firstly we have suggested a correction to the mean squared error of the estimator proposed by Gupta and Shabbir (2008). Later we have proposed a ratio type estimator and its properties are studied in simple random sampling. Numerically we have shown that the proposed class of estimators is more efficient than different known estimators including Gupta and Shabbir (2008) estimator. [ABSTRACT FROM AUTHOR]

The design of this paper is to examine the problem of estimation of finite population product in mail surveys for the current occasion in the context of sampling on two occasions is attempted when there is non-response on both the occasions. Estimators for the current occasion are derived as a particular case when there is non-response on first and second occasion only. The gain in efficiency of the proposed estimate over the direct estimate using no information gathered on the first occasion is computed. The proposed strategy has been compared with other estimators. An empirical study is carried out to study the performance of the proposed strategy. [ABSTRACT FROM AUTHOR]

Abstract: This paper considers the problem of estimating the population mean of the study variate y with two auxiliary variates x and z in the presence of non-response using two-phase (double) sampling procedure. Four classes of combined regression and ratio estimators have been defined in four different situations and their properties are studied under large sample approximation. Comparisons of the suggested classes of estimators with usual unbiased estimator reported by , estimator, estimator and estimator have been made. The results obtained are demonstrated with the help of an empirical study. [Copyright &y& Elsevier]

The paper deals with the problem of estimating the general parameter P(α) { = (Ȳ0/Ȳ1α), Ȳ1 ≠ 0,α being a scalar which takes values -1, 0 and 1; and Ȳj is the population mean of the study variate yj, j = 0,1 } using double sampling for stratification (DSS) based on auxiliary information (i) when there is total response on the auxiliary variable x used in estimating the stratum weight Wh { = (Nh/N), Nh and N being the size of the hth stratum and the population size N respectively} and on the study variates (y0, y1) ; and (ii) when there is total response on the auxiliary variable x and incomplete response on the study variables (y0, y1), see Okafor (1996). Classes of estimators for the general parameter P(α) in both the situations have been proposed and their properties are studied. Asymptotic optimum estimators (AOEs) in the classes are investigated alongwith their approximate variance formulae. The present study also generalizes the work of Okafor (1996) and Singh and Vishwakarma (2007). [ABSTRACT FROM AUTHOR]

In this paper, we have suggested a family of estimators &picirc;_W for estimating π, the proportion of population having sensitive category, under unrelated question randomized response model. It is shown that the suggested class of estimators &picirc;_W of π is more efficient than Mangat et al.'s [8] estimator &picirc;_1. Since &picirc;_W bases on the unknown scalar W, it has little practical utility. By replacing W in &picirc;_W by its various estimated values, tww estimators &picirc;_W^{(i)}, i = 1, 2, have been suggested for use in practice. Exact efficiency of &picirc;_W^{(i)} have been worked out numerically. [ABSTRACT FROM AUTHOR]

Abstract: A class of unbiased estimators of the population variance is proposed when the coefficient of variation is known for each stratum. The technique of equilibrated stratification is used. It is shown that, with optimum allocation, the variance of the suggested estimator for sufficiently large sample size is less than that of the classical minimum variance unbiased estimator under distribution-free setting. Other classes of estimators are also given with similar or better practical properties. [Copyright &y& Elsevier]

In this paper we have suggested two modified estimators of population mean using power transformation. It has been shown that the modified estimators are more efficient than the sample mean estimator, usual ratio estimator, Sisodia and Dwivedi's (1981) estimator and Upadhyaya and Singh's (1999) estimator at their optimum conditions. Empirical illustrations are also given for examining the merits of the proposed estimators. Following Kadilar and Cingi (2003) the work has been extended to stratified random sampling, and the same data set has been studied to examine the performance in stratified random sampling. [ABSTRACT FROM AUTHOR]