Auxiliary Variable Research Papers - Academia.edu (original) (raw)

We propose a new ratio estimator using two auxiliary variables in simple random sampling. We obtain mean square error (MSE) equation of this estimator and theoretically show that our proposed estimator is more efficient than the... more

We propose a new ratio estimator using two auxiliary variables in simple random sampling. We obtain mean square error (MSE) equation of this estimator and theoretically show that our proposed estimator is more efficient than the traditional multivariate ratio estimator under a defined condition. In addition, we support this theoretical result with the aid of a numerical example.

Abstract: Groundwater is one of the major sources of exploitation in arid and semi-arid regions. Thus for protecting Groundwater quality, data on spatial and temporal distribution are important. Geostatistics methods are one of the most... more

Abstract: Groundwater is one of the major sources of exploitation in arid and semi-arid regions. Thus for protecting Groundwater quality, data on spatial and temporal distribution are important. Geostatistics methods are one of the most advanced techniques for interpolation of Groundwater quality. In this research, IDW, kriging and cokriging methods were used for predicting spatial distribution of some Groundwater characteristics such as: TDS, TH, EC, SAR, Cl-and SO4 2-. Data were related to 73 wells in Ardakan-Yazd plain. ...

Finite population estimation is the overall goal of sample surveys. When information regarding auxiliary variables are available, one may take advantage of general regression estimators (GREG) to improve sample estimates precision. GREG... more

Finite population estimation is the overall goal of sample surveys. When information regarding auxiliary variables are available, one may take advantage of general regression estimators (GREG) to improve sample estimates precision. GREG estimators may be derived when the relationship between interest and auxiliary variables is represented by a normal linear model. However, in some cases, such as when estimating class frequencies or counting processes means, Bernoulli or Poisson models are more suitable than linear normal ones. This paper focuses on building regression type estimators under a model-assisted approach, for the general case in which the relationship between interest and auxiliary variables may be suitably described by a generalized linear model. The finite population distribution of the variable of interest is viewed as if generated by a member of the exponential family, which includes Bernoulli, Poisson, gamma and inverse Gaussian distributions, among others. The resulting estimator is a generalized linear model regression estimator (GEREG). Its general form and basic statistical properties are presented and studied analytically and empirically, using Monte Carlo simulation experiments. Three applications are presented in which the GEREG estimator shows better performance than the GREG one.

We propose a general framework for efficient pricing via a partial differential equation (PDE) approach for exotic cross-currency interest rate (IR) derivatives, with strong emphasis on long-dated foreign exchange (FX) IR hybrids, namely... more

We propose a general framework for efficient pricing via a partial differential equation (PDE) approach for exotic cross-currency interest rate (IR) derivatives, with strong emphasis on long-dated foreign exchange (FX) IR hybrids, namely Power Reverse Dual Currency (PRDC) swaps with a FX Target Redemption (FX-TARN) provision. The FX-TARN provision provides a cap on the FX-linked PRDC coupon amounts, and once the accumulated coupon amount reaches this cap, the underlying PRDC swap terminates.Our PDE pricing framework is based on an auxiliary state variable to keep track of the total accumulated PRDC coupon amount. Finite differences on uniform grids and the Alternating Direction Implicit (ADI) method are used for the spatial and time discretizations, respectively, of the model-dependent PDE corresponding to each discretized value of the auxiliary variable. Numerical examples illustrating the convergence properties of the numerical methods are provided.

Some improved estimators of population mean has been proposed in two phase and multiphase sampling using information on two and several auxiliary variables. The mini- mum variance of the proposed estimators has been obtained. Comparison... more

Some improved estimators of population mean has been proposed in two phase and multiphase sampling using information on two and several auxiliary variables. The mini- mum variance of the proposed estimators has been obtained. Comparison of the proposed estimators has been done with some available estimators of two phase sampling that utilizes information of two and several auxiliary variables.

A new class of improved estimators for estimating finite population mean has been proposed using full information on two auxiliary variables in single and two-phase sampling. Expressions of Mean Square error and Bias for the proposed... more

A new class of improved estimators for estimating finite population mean has been proposed using full information on two auxiliary variables in single and two-phase sampling. Expressions of Mean Square error and Bias for the proposed estimators under simple random sampling without replacement (SRSWOS) have been derived. An empirical comparison of proposed class with respect to usual unbiased estimator with some well-known estimators in single and double sampling has also been made. Empirical study confirmed that the proposed class of estimators is the class of more efficient estimators under percent relative efficiency (PRE) criterion.

In this paper, a chain ratio–product type estimators has been developed for estimating population mean of the study variable using two auxiliary variables under double sampling scheme, when the information on another additional auxiliary... more

In this paper, a chain ratio–product type estimators has been developed for estimating population mean of the study variable using two auxiliary variables under double sampling scheme, when the information on another additional auxiliary character is ...

Summary. The evaluation of results from mixtures of deoxyribonucleic acid (DNA) from two or more people in crime case investigations may be improved by taking not only the qualitative but also the quantitative part of the results into... more

Summary. The evaluation of results from mixtures of deoxyribonucleic acid (DNA) from two or more people in crime case investigations may be improved by taking not only the qualitative but also the quantitative part of the results into consideration. We present a statistical likelihood approach to assess the probability of observed peak heights and peak areas information for a pair of profiles matching the DNA mixture. Furthermore, we demonstrate how to incorporate this probability in the evaluation of the weight of the evidence by a likelihood ratio approach. Our model is based on a multivariate normal distribution of peak areas for assessing the weight of the evidence. On the basis of data from analyses of controlled experiments with mixed DNA samples, we exploited the linear relationship between peak heights and peak areas, and the linear relationships of the means and variances of the measurements. Furthermore, the contribution from one individual's allele to the mean area of this allele is assumed to be proportional to the average of peak height measurements of alleles, where the individual is the only contributor. For shared alleles in mixed DNA samples, it is possible to observe only the cumulative peak heights and areas. Complying with this latent structure, we used the EM algorithm to impute the missing variables on the basis of a compound symmetry model. The measurements were subject to intralocus and interlocus correlations not depending on the actual alleles of the DNA profiles. Owing to factorization of the likelihood, properties of the normal distribution and use of auxiliary variables, an ordinary implementation of the EM algorithm solved the missing data problem.

The efficiencies of the ratio- type estimators have been increased by using linear transformation on auxiliary variable in the literature. But such type of estimators requires the additional knowledge of unknown population parameters,... more

The efficiencies of the ratio- type estimators have been increased by using linear transformation on auxiliary variable in the literature. But such type of estimators requires the additional knowledge of unknown population parameters, which restricts their applicability. Keeping in view such restrictions, we have proposed two unbiased estimators of population mean of study variable on applying linear transformation to auxiliary

In this paper exponential ratio and exponential product type estimators using two auxiliary variables are proposed for estimating unknown popoulation variance S 2 y. Problem is extended to the case of two-phase sampling. Theoretical... more

In this paper exponential ratio and exponential product type estimators using two auxiliary variables are proposed for estimating unknown popoulation variance S 2 y. Problem is extended to the case of two-phase sampling. Theoretical results are supported by an empirical study

This paper considers three ratio estimators of the population mean using known correlation coefficient between the study and auxiliary variables in simple random sample when some sample observations are missing. The suggested estimators... more

This paper considers three ratio estimators of the population mean using known correlation coefficient between the study and auxiliary variables in simple random sample when some sample observations are missing. The suggested estimators are compared with the estimators of Singh and Horn (Metrika 51:267–276, 2000), Singh and Deo (Stat Pap 44:555–579, 2003) and Kadilar and Cingi (Commun Stat Theory Methods 37:2226–2236, 2008). They are compared with other imputation estimators based on the mean or a ratio. It is found that the suggested estimators are approximately unbiased for the population mean. Also, it turns out that the suggested estimators perform well when compared with the other estimators considered in this study.

This paper proposes a new model-based approach to estimate small areas that extends the Fay–Herriot methodology. The new model is additive, with a random term to characterize the inter-area variability and a nonparametric mean function... more

This paper proposes a new model-based approach to estimate small areas that extends the Fay–Herriot methodology. The new model is additive, with a random term to characterize the inter-area variability and a nonparametric mean function specification, defined using the information on an auxiliary variable. The most significant advantage of the proposal is that it avoids the model misspecification problem. The monotonicity is the only assumption about the functional form of the relationship between the variable of interest and the auxiliary one. Estimators for the area means are derived combining “Order Restricted Inference” and standard mixed model approaches. A large simulation experiment shows how the new approach outperforms the Fay–Herriot methodology in many scenarios. Besides, the new method is applied to the Australian farms data.

This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of... more

This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the interpolation method variously called “Universal Kriging”(UK) and “Kriging with External ...

This paper proposes the use of multi-auxiliary information using quantiles and ratio and difference type estimators of the finite population distribution function to derive confidence intervals for medians. A simulation study based on... more

This paper proposes the use of multi-auxiliary information using quantiles and ratio and difference type estimators of the finite population distribution function to derive confidence intervals for medians. A simulation study based on three real populations compares its behaviour to ...

The extensive body of research that examines for (Granger, 1969) causality from exports to output for developing countries, including Bangladesh and Sri Lanka, using vector autoregressions and/or vector error correction models, is limited... more

The extensive body of research that examines for (Granger, 1969) causality from exports to output for developing countries, including Bangladesh and Sri Lanka, using vector autoregressions and/or vector error correction models, is limited in only examining for one-period ahead or direct causality; the exception is in bivariate systems. This (usually unrecognized) focus on one-period causality in multivariate systems has often