Saddlestrapping

A method of weighted bootstrapping in the presence of auxiliary information has been studied and named as saddlestrapping because there exists a saddlepoint. Comparisons of saddlestrapping with the bootstrapping under different situations are performed and discussed. FORTRAN code for doing bootstrapping and saddlestrapping are provided. A huge scope of further studies has been suggested

Use of Two Variables Having Common Mean to Improve the Bar-Lev, Bobovitch and Boukai Randomized Response Model

A new method to improve the randomized response model due to Bar-Lev, Bobovitch and Boukai (2004) is suggested. It has been observed that if two sensitive (or non sensitive) variables exist that are related to the main study sensitive variable, then those variables could be used to construct ratio type adjustments to the usual estimator of the population mean of a sensitive variable due to Bar-Lev, Bobovitch and Boukai (2004).The relative efficiency of the proposed estimators is studied with respect to the Bar-Lev, Bobovitch and Boukai (2004) models under different situations

Estimation of Multinomial Proportions using Higher Order Moments of Scrambling Variables in Randomized Response Sampling

An extension to estimating multinomial proportions of potentially sensitive attributes in survey sampling is proposed using higher order moments of scrambling variables at the estimation stage to produce unbiased estimators. The variance and covariance expressions are derived and the relative efficiency of the proposed estimators based on scrambling variables is investigated

A family of estimators of population mean using multi-auxiliary variate and post-stratification

This paper suggests a family of estimators of population mean using multiauxiliary variate based on post-stratified sampling and its properties are studied under large sample approximation. Asymptotically optimum estimator in the class is identified alongwith its approximate variance formulae. The proposed class of estimators is also compared with corresponding unstratified class of estimators based on estimated optimum value. At the end, an empirical study has been carried out to support the proposed methodology

Stochastic Randomized Response Model for a Quantitative Sensitive Random Variable

A new stochastic randomized response model is introduced that is useful for estimating the population mean of a sensitive quantitative variable. The proposed stochastic randomized response model is an extension of the stochastic randomized response model from a qualitative sensitive variable to a quantitative variable found in Singh (2002). The stochastic nature of a randomized response device helps increase a respondent’s cooperation while collecting information on sensitive variables in a society. The Bar-Lev, Bobovitch, and Boukai (2004) model is shown to be a special case of the proposed model

Estimating Population Proportions by Means of Calibration Estimators

This paper considers the problem of estimating the population proportion of a categorical variable using the calibration framework. Different situations are explored according to the level of auxiliary information available and the theoretical properties are investigated. A new class of estimator based upon the proposed calibration estimators is also defined, and the optimal estimator in the class, in the sense of minimal variance, is derived. Finally, an estimator of the population proportion, under new calibration conditions, is defined. Simulation studies are considered to evaluate the performance of the proposed calibration estimators via the empirical relative bias and the empirical relative efficiency, and favourable results are achieved.El artículo considera el problema de la estimación de la proporción poblacional de una variable categórica usando como marco de trabajo la calibración. Se exploran diferentes situaciones de acuerdo con la información auxiliar disponible y se investigan las propiedades teóricas.. Una nueva clase de estimadores basada en los estimadores de calibración propuestos también es definida y el estimador óptimo en la clase, en el sentido de varianza mínima, es obtenido. Finalmente, un estimador de la proporción poblacional, bajo nuevas condiciones de calibración es también propuesto. Estudios de simulación para evaluar el comportamiento de los estimadores calibrados propuestos a través del sesgo relativo empírico y de la eficiencia relativa empírica son incluidos, obteniéndose resultados satisfactorios