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1.
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. 相似文献
2.
Recently, Koyuncu et al. (2013) proposed an exponential type estimator to improve the efficiency of mean estimator based on randomized response technique. In this article, we propose an improved exponential type estimator which is more efficient than the Koyuncu et al. (2013) estimator, which in turn was shown to be more efficient than the usual mean estimator, ratio estimator, regression estimator, and the Gupta et al. (2012) estimator. Under simple random sampling without replacement (SRSWOR) scheme, bias and mean square error expressions for the proposed estimator are obtained up to first order of approximation and comparisons are made with the Koyuncu et al. (2013) estimator. A simulation study is used to observe the performances of these two estimators. Theoretical findings are also supported by a numerical example with real data. We also show how to, extend the proposed estimator to the case when more than one auxiliary variable is available. 相似文献
3.
Sanaullah et al. (2014) have suggested generalized exponential chain ratio estimators under stratified two-phase sampling scheme for estimating the finite population mean. However, the bias and mean square error (MSE) expressions presented in that work need some corrections, and consequently the study based on efficiency comparison also requires corrections. In this article, we revisit Sanaullah et al. (2014) estimator and provide the correct bias and MSE expressions of their estimator. We also propose an estimator which is more efficient than several competing estimators including the classes of estimators in Sanaullah et al. (2014). Three real datasets are used for efficiency comparisons. 相似文献
4.
When a sufficient correlation between the study variable and the auxiliary variable exists, the ranks of the auxiliary variable are also correlated with the study variable, and thus, these ranks can be used as an effective tool in increasing the precision of an estimator. In this paper, we propose a new improved estimator of the finite population mean that incorporates the supplementary information in forms of: (i) the auxiliary variable and (ii) ranks of the auxiliary variable. Mathematical expressions for the bias and the mean-squared error of the proposed estimator are derived under the first order of approximation. The theoretical and empirical studies reveal that the proposed estimator always performs better than the usual mean, ratio, product, exponential-ratio and -product, classical regression estimators, and Rao (1991), Singh et al. (2009), Shabbir and Gupta (2010), Grover and Kaur (2011, 2014) estimators. 相似文献
5.
This article proposes Hartley-Ross type unbiased estimators of finite population mean using information on known parameters of auxiliary variate when the study variate and auxiliary variate are positively correlated. The variances of the proposed unbiased estimators are obtained. It has been shown that the proposed estimators are more efficient than the simple mean estimator, usual ratio estimator and estimators proposed by Sisodia and Dwivedi (1981), Kadilar and Cingi (2006), and Kadilar et al. (2007) under certain realistic conditions. Empirical studies are also carried out to demonstrate the merits of the proposed unbiased estimators over other estimators considered in this article. 相似文献
6.
Housila P. Singh 《统计学通讯:理论与方法》2017,46(2):521-531
This paper aimed at providing an efficient new unbiased estimator for estimating the proportion of a potentially sensitive attribute in survey sampling. The suggested randomization device makes use of the means, variances of scrambling variables, and the two scalars lie between “zero” and “one.” Thus, the same amount of information has been used at the estimation stage. The variance formula of the suggested estimator has been obtained. We have compared the proposed unbiased estimator with that of Kuk (1990) and Franklin (1989), and Singh and Chen (2009) estimators. Relevant conditions are obtained in which the proposed estimator is more efficient than Kuk (1990) and Franklin (1989) and Singh and Chen (2009) estimators. The optimum estimator (OE) in the proposed class of estimators has been identified which finally depends on moments ratios of the scrambling variables. The variance of the optimum estimator has been obtained and compared with that of the Kuk (1990) and Franklin (1989) estimator and Singh and Chen (2009) estimator. It is interesting to mention that the “optimum estimator” of the class of estimators due to Singh and Chen (2009) depends on the parameter π under investigation which limits the use of Singh and Chen (2009) OE in practice while the proposed OE in this paper is free from such a constraint. The proposed OE depends only on the moments ratios of scrambling variables. This is an advantage over the Singh and Chen (2009) estimator. Numerical illustrations are given in the support of the present study when the scrambling variables follow normal distribution. Theoretical and empirical results are very sound and quite illuminating in the favor of the present study. 相似文献
7.
In this paper, we propose a method to jointly incorporate measurement error and non response in the estimators of population mean using auxiliary information in simple random sampling. We have not only studied some available estimators but also suggested three new estimators in the presence of two types of non sampling errors occurring jointly: the measurement error and the non response. The expressions for the bias and mean square errors of proposed estimator have been derived. A comparative study is made among the proposed estimators, the Hansen and Hurwitz (1946) estimator, the Cochran's (1977) estimator, and the Singh and Kumar (2008) estimator. 相似文献
8.
ABSTRACTThe 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). 相似文献
9.
Xuemei Hu 《统计学通讯:理论与方法》2014,43(18):3927-3942
Semivarying-coefficient models with heteroscedastic errors are frequently used in statistical modeling. When the error is conditional heteroskedastic, Ahmad, et al. (2005) proposed a general series method to obtain an efficient estimation. In this article we study the heteroscedastic semi-varying coefficient models with a nonparametric variance function, not only use the semi-parametric efficient normal approximation method to derive a family of semi-parametric efficient estimator, but also use the semi-parametric efficient empirical likelihood method to construct the efficient empirical likelihood confidence regions. The proposed estimators retain the double robustness feature of semi-parametric efficient estimator. 相似文献
10.
Several methods using different approaches have been developed to remedy the consequences of collinearity. To the best of our knowledge, only the raise estimator proposed by García et al. (2010) deals with this problem from a geometric perspective. This article fully develops the raise estimator for a model with two standardized explanatory variables. Inference in the raise estimator is examined, showing that it can be obtained from ordinary least squares methodology. In addition, contrary to what happens in ridge regression, the raise estimator maintains the coefficient of determination value constant. The expression of the variance inflation factor for the raise estimator is also presented. Finally, a comparative study of the raise and ridge estimators is carried out using an example. 相似文献
11.
To deal with multicollinearity problem, the biased estimators with two biasing parameters have recently attracted much research interest. The aim of this article is to compare one of the last proposals given by Yang and Chang (2010) with Liu-type estimator (Liu 2003) and k ? d class estimator (Sakallioglu and Kaciranlar 2008) under the matrix mean squared error criterion. As well as giving these comparisons theoretically, we support the results with the extended simulation studies and real data example, which show the advantages of the proposal given by Yang and Chang (2010) over the other proposals with increasing multicollinearity level. 相似文献
12.
Liew (1976a) introduced generalized inequality constrained least squares (GICLS) estimator and inequality constrained two-stage and three-stage least squares estimators by reducing primal–dual relation to problem of Dantzig and Cottle (1967), Cottle and Dantzig (1974) and solving with Lemke (1962) algorithm. The purpose of this article is to present inequality constrained ridge regression (ICRR) estimator with correlated errors and inequality constrained two-stage and three-stage ridge regression estimators in the presence of multicollinearity. Untruncated variance–covariance matrix and mean square error are derived for the ICRR estimator with correlated errors, and its superiority over the GICLS estimator is examined via Monte Carlo simulation. 相似文献
13.
ABSTRACTFor a trivariate distribution, an efficient family of estimators of median of study variable using the known information on the auxiliary variables has been proposed under two-phase sampling design. The expressions for bias and its mean square error have been obtained up to first order of approximation. It has been shown that the proposed estimator has smaller bias as compared to estimator defined by Singh et al. (2006) with the same efficiency. The results have also been illustrated numerically by taking data from different populations considered in literature. 相似文献
14.
We propose a new ratio type estimator for estimating the finite population mean using two auxiliary variables in stratified two-phase sampling. Expressions for bias and mean squared error of the proposed estimator are derived up to the first order of approximation. The proposed estimator is more efficient than the usual stratified sample mean estimator, traditional stratified ratio estimator and some other stratified estimators including Bahl and Tuteja (1991), Chami et al. (2012), Chand (1975), Choudhury and Singh (2012), Hamad et al. (2013), Vishwakarma and Gangele (2014), Sanaullah et al. (2014), and Chanu and Singh (2014). 相似文献
15.
This is an interesting article that considers the question of inference on unknown linear index coefficients in a general class of models where reduced form parameters are invertible function of one or more linear index. Interpretable sufficient conditions such as monotonicity and or smoothness for the invertibility condition are provided. The results generalize some work in the previous literature by allowing the number of reduced form parameters to exceed the number of indices. The identification and estimation expand on the approach taken in previous work by the authors. Examples include Ahn, Powell, and Ichimura (2004) for monotone single-index regression models to a multi-index setting and extended by Blundell and Powell (2004) and Powell and Ruud (2008) to models with endogenous regressors and multinomial response, respectively. A key property of the inference approach taken is that the estimator of the unknown index coefficients (up to scale) is computationally simple to obtain (relative to other estimators in the literature) in that it is closed form. Specifically, unifying an approach for all models considered in this article, the authors propose an estimator, which is the eigenvector of a matrix (defined in terms of a preliminary estimator of the reduced form parameters) corresponding to its smallest eigenvalue. Under suitable conditions, the proposed estimator is shown to be root-n-consistent and asymptotically normal. 相似文献
16.
This article proposes various Searls-type ratio imputation methods (STRIM) on the lines of Ahmed et al. (2006). It is a well-known fact that the optimal ratio type estimator attains the MSE of regression estimator (or optimal difference estimator) but while using Searls-type transformation (STT) (Searls (1964)) this may not always happen. These STRIM are shown to perform better than the imputation procedures of Ahmed et al. (2006). The STRIM may even outperform the Searls type difference imputation methods (STDIM) proposed by us in our earlier work, Bhushan and Pandey (2016). This study is concluded with the numerical study along with the theoretical comparison. 相似文献
17.
This article considers several estimators for estimating the ridge parameter k for multinomial logit model based on the work of Khalaf and Shukur (2005), Alkhamisi et al. (2006), and Muniz et al. (2012). The mean square error (MSE) is considered as the performance criterion. A simulation study has been conducted to compare the performance of the estimators. Based on the simulation study we found that increasing the correlation between the independent variables and the number of regressors has negative effect on the MSE. However, when the sample size increases the MSE decreases even when the correlation between the independent variables is large. Based on the minimum MSE criterion some useful estimators for estimating the ridge parameter k are recommended for the practitioners. 相似文献
18.
Yu-Ye Zou 《统计学通讯:理论与方法》2017,46(2):1007-1023
In this article, we study global L2 error of non linear wavelet estimator of density in the Besov space Bspq for missing data model when covariables are present and prove that the estimator can achieve the optimal rate of convergence, which is similar to the result studied by Donoho et al. (1996) in complete independent data case with term-by-term thresholding of the empirical wavelet coefficients. Finite-sample behavior of the proposed estimator is explored via simulations. 相似文献
19.
Qing Wang 《统计学通讯:理论与方法》2017,46(17):8387-8400
This article considers the problem of variance estimation of a U-statistic. Following the proposal of a linearly extrapolated variance estimator in Wang and Chen (2015), we consider a second-order extrapolation technique and devise a variance estimator that is nearly second-order unbiased. Simulation studies confirm that the second-order extrapolated variance estimator has smaller bias than the linearly extrapolated variance estimator and the jackknife variance estimator across a wide selection of distributions. In addition, the proposal also yields a smaller mean squared error than its counterparts. In the end, we discuss the advantages of the proposed variance estimator in regression analysis and model selection. 相似文献
20.
Rameela Chandrasekhar 《统计学通讯:理论与方法》2014,43(14):2951-2957
Adaptive designs find an important application in the estimation of unknown percentiles for an underlying dose-response curve. A nonparametric adaptive design was suggested by Mugno et al. (2004) to simultaneously estimate multiple percentiles of an unknown dose-response curve via generalized Polya urns. In this article, we examine the properties of the design proposed by Mugno et al. (2004) when delays in observing responses are encountered. Using simulations, we evaluate a modification of the design under varying group sizes. Our results demonstrate unbiased estimation with minimal loss in efficiency when compared to the original compound urn design. 相似文献