Juan Francisco Muñoz

New imputation methods for missing data using quantiles

The problem of missing values commonly arises in data sets, and imputation is usually employed to compensate for non-response. We propose a novel imputation method based on quantiles, which can be implemented with or without the presence of auxiliary information. The proposed method is extended to unequal sampling designs and non-uniform response mechanisms. Iterative algorithms to compute the proposed imputation methods are presented. Monte Carlo simulations are conducted to assess the performance of the proposed imputation methods with respect to alternative imputation methods. Simulation results indicate that the proposed methods perform competitively in terms of relative bias and relative root mean square error.

Estimators and Confidence Intervals for the Proportion Using Binary Auxiliary Information with Applications to Pharmaceutical Studies

Estimation of a proportion is commonly used in areas such as medicine, biopharmaceutical experiments, etc. Estimation of a proportion using auxiliary information has not been investigated in the literature. Ratio estimators of the population proportion and two-sided confidence intervals based upon auxiliary information are derived in this paper. Real data extracted from the Spanish National Health Survey are used to demonstrate the application of the proposed methods in the estimation of prevalences. Results derived from simulation studies show that proposed estimators are more efficient than the traditional estimator. Proposed confidence intervals outperform the alternative methods, especially in terms of interval width. A study on patients with hypertension is also considered to calculate various estimators and confidence intervals.

Quantile estimation in two-phase sampling

The estimation of quantiles in two-phase sampling with arbitrary sampling design in each of the two phases is investigated. Several ratio and exponentiation type estimators that provide the optimum estimate of a quantile based on an optimum exponent @a are proposed. Properties of these estimators are studied under large sample size approximation and the use of double sampling for stratification to estimate quantiles can also be seen. The real performance of these estimators will be evaluated for the three quartiles on the basis of data from two real populations using different sampling designs. The simulation study shows that proposed estimators can be very satisfactory in terms of relative bias and efficiency.

Variance estimation of survey estimates calibrated on estimated control totals-An application to the extended regression estimator and the regression composite estimator

Calibration on control totals is commonly used for survey weighting. It is usually assumed that these totals are values known without sampling errors. However, they can be estimated from other sources. A variance estimator that takes into account the randomness of control totals is derived. Several situations such as calibration on external sources and calibration with sampling on two occasions are investigated. The methodology proposed is general and can be implemented in various situations when control totals are estimated.

Optimum design-based ratio estimators of the distribution function

The ratio method is commonly used to the estimation of means and totals. This method was extended to the problem of estimating the distribution function. An alternative ratio estimator of the distribution function is defined. A result that compares the variances of the aforementioned ratio estimators is used to define optimum design-based ratio estimators of the distribution function. Different empirical results indicate that the optimum ratio estimators can be more efficient than alternative ratio estimators. In addition, we show by simulations that alternative ratio estimators can have large biases, whereas biases of the optimum ratio estimators are negligible in this situation.

Estimating quantiles under sampling on two occasions with arbitrary sample designs

A practical problem related to the estimation of quantiles in double sampling with arbitrary sampling designs in each of the two phases is investigated. In practice, this scheme is commonly used for official surveys, in which quantile estimation is often required when the investigation deals with variables such as income or expenditure. A class of estimators for quantiles is proposed and some important properties, such as asymptotic unbiasedness and asymptotic variance, are established. The optimal estimator, in the sense of minimizing the asymptotic variance, is also presented. The proposed class contains several known types of estimators, such as ratio and regression estimators, which are of practical application and are therefore derived. Assuming several populations, the proposed estimators are compared with the direct estimator via an empirical study. Results show that a gain in efficiency can be obtained.

Mean Estimation Under Successive Sampling with Calibration Estimators

The estimation of the finite population mean in successive occasions is investigated with calibration estimators in this article. We propose several estimators based on calibration techniques with arbitrary sampling design in each of the occasions. Asymptotic variance formulaes are derived for the proposed estimators. The properties of these estimators are studied via a simulation study and using natural populations.

Optimum ratio estimators for the population proportion

The problem of the estimation of a population proportion using auxiliary information has been recently studied by Rueda et al. (Estimators and confidence intervals for the proportion using binary auxiliary information with applications to pharmaceutical studies, J. Biopharmaceut. Statist. 21 (2011), pp. 526–554), which proposed several ratio estimators of the population proportion and studied some theoretical properties. In this paper, we define a new ratio estimator based on a linear combination of two ratio estimators defined by Rueda et al. (2011). The variance of the new estimator is calculated and it is used to obtain the optimum value into the linear combination in the sense of minimal variance. Theoretical and empirical studies show that the suggested ratio estimator performs better than alternative estimators.

An improved class of estimators of a finite population quantile in sample surveys

This work proposes a general class of estimators for a finite population quantile using auxiliary information. This information is provided by the population means of auxiliary variables. The optimum estimator in this class is derived. This result is supported with a numerical example.

On estimating quantiles using auxiliary information

Abstract We propose a transformation-based approach for estimating quantiles using auxiliary information. The proposed estimators can be easily implemented using a regression estimator. We show that the proposed estimators are consistent and asymptotically unbiased. The main advantage of the proposed estimators is their simplicity. Despite the fact the proposed estimators are not necessarily more efficient than their competitors, they offer a good compromise between accuracy and simplicity. They can be used under single and multistage sampling designs with unequal selection probabilities. A simulation study supports our finding and shows that the proposed estimators are robust and of an acceptable accuracy compared to alternative estimators, which can be more computationally intensive.