Pseudo empirical likelihood method in the presence of missing data

In this paper, we propose an estimator for the population mean when some observations on the study and auxiliary variables are missing from the sample. The proposed estimator is valid for any unequal probability sampling design, and is based upon the pseudo empirical likelihood method. The proposed estimator is compared with other estimators in a simulation study.     

A mixed-mode sensitive research on cannabis use and sexual addiction: improving self-reporting by means of indirect questioning techniques

In this article, we describe the methods employed and the results obtained from a mixed-mode “sensitive research” conducted in Spain to estimate certain aspects concerning patterns of cannabis consumption and sexual addiction among university students. Three different data-collection methods are considered and compared: direct questioning, randomized response technique and item sum technique. It is shown that posing direct questions to obtain sensitive data produces significantly lower estimates of the surveyed characteristics than do indirect questioning methods. From the analysis, it emerges that male students seem to be more affected by sex addiction than female students while for cannabis consumption there is no evidence of a predominant gender effect.     

Job Classification for the Purpose of Making Optimal Decisions Concerning Management Control

The job classification literature has been dominated by a micro‐level orientation reflective of the needs of specialists in human resources. In contrast, we take a multidisciplinary approach involving both micro‐ and macro‐level variables to propose a relatively simple system comprised of four job classes. Our model isintended to inform management as to the type and mix of controls (e.g., centralization, formalization, and various human resources policies) that could be applied to different job types to optimally promote the overall goals of the organization. Fuzzy numbers analysis is employed to illustrate the application of the system using four jobs in a car dealership in Spain. Copyright © 2012 ASAC. Published by John Wiley & Sons, Ltd.     

An approximation method to derive confidence intervals for quantiles with some applications

Standard jackknife confidence intervals for a quantile Q _y (β) are usually preferred to confidence intervals based on analytical variance estimators due to their operational simplicity. However, the standard jackknife confidence intervals can give undesirable coverage probabilities for small samples sizes and large or small values of β. In this paper confidence intervals for a population quantile based on several existing estimators of a quantile are derived. These intervals are based on an approximation for the cumulative distribution function of a studentized quantile estimator. Confidence intervals are empirically evaluated by using real data and some applications are illustrated. Results derived from simulation studies show that proposed confidence intervals are narrower than confidence intervals based on the standard jackknife technique, which assumes normal approximation. Proposed confidence intervals also achieve coverage probabilities above to their nominal level. This study indicates that the proposed method can be an alternative to the asymptotic confidence intervals, which can be unknown in practice, and the standard jackknife confidence intervals, which can have poor coverage probabilities and give wider intervals.     

Estimation of poverty measures with auxiliary information in sample surveys

The analysis of poverty measures has been receiving increased attention in recent years. This paper contributes to the literature by developing percentile ratio estimators based on the pseudo empirical likelihood method. In practice, variances of poverty measures could be not expressible by simple formulae and consequently other techniques should be used in the variance estimation stage. Assuming percentile ratios, resampling techniques are investigated in this paper. A numerical example based on data from the Spanish Household Panel Survey is taken up to illustrate how suggested procedures can perform better than existing ones. The effect of a model-misspecification on the proposed estimators is also evaluated by using simulated populations.     

EMPIRICAL ANALYSIS OF SPECULATIVE ATTACKS WITH CONTRACTIONARY REAL EFFECTS

The effects of a currency crisis on a country's economy depend on nonlinear relations among several variables that characterize the economic, financial, legal and socio‐political structure of the country at the onset of the crisis. Those effects can be associated with contractions or expansions in output. Historically, contractionary speculative attacks are more frequent. This paper uses a parametric censored heteroscedastic TOBIT model to empirically analyse how different economic and financial variables determine the real effects of a contractionary speculative attack. Variables describing the banking sector, the international trade, the severity of the crisis and foreign interest rates are found to be significant in explaining the size of currency crises' contractionary real effects. The TOBIT's results are compared with alternative modelling strategies. Copyright © 2012 John Wiley & Sons, Ltd.     

The impact of banking sector reform in a transition economy: Evidence from Kyrgyzstan

We examine the impact of financial sector reform on interest rate levels and spreads using Kyrgyz bank-level data from 1998 to 2005. We find that, in addition to macroeconomic stabilization, structural reforms to the banking sector significantly contributed to lower interest rates. In particular, our results suggest that foreign bank entry and regulatory efforts to increase average bank size were important in reducing deposit rates. In contrast, we find little evidence that banking sector reform or macroeconomic stabilization has impacted interest rate spreads.     

New Model-assisted Estimators for the Distribution Function Using the Pseudo Empirical Likelihood Method

This paper proposes using a model-assisted approach based on the pseudo empirical likelihood method to construct estimators for the finite population distribution function. It shows that the proposed sample-based estimators are genuine distribution functions that exhibit several attractive features, such as the fact that they do not depend on unknown parameters, and good performance at any argument is expected to be obtained. Consequently, estimation of other measures, such as quantiles, is a problem that is efficiently addressed by the proposed methodology and applications in various areas are therefore derived. Simulation studies based upon real and artificial populations show that the proposed estimators perform better than the existing ones. A practical situation in which the proposed estimators can be applied is also described.     

Nonparametric methods in sample surveys. Application to the estimation of cancer prevalence

We address the problem of the estimation of the population mean and the distribution function using nonparametric regression. These methods are being used in a wide range of settings and areas of research. In particular, they are a good alternative to other classical methods in the survey sampling context, since they work under the assumption that the underlying regression function is smooth. Some relevant nonparametric regression methods in survey sampling are presented. Data on breast cancer prevalence derived from 40 European countries are used to study the application of the nonparametric estimators to the estimation of cancer prevalence. Result derived from an empirical study show that nonparametric estimators have a good empirical performance in this study on cancer prevalence.     

Bayesian Hypothesis Testing: a Reference Approach

For any probability model M={p(x|θ, ω), θεΘ, ωεΩ} assumed to describe the probabilistic behaviour of data xεX, it is argued that testing whether or not the available data are compatible with the hypothesis H₀={θ=θ₀} is best considered as a formal decision problem on whether to use (a₀), or not to use (a₀), the simpler probability model (or null model) M₀={p(x|θ₀, ω), ωεΩ}, where the loss difference L(a₀, θ, ω) –L(a₀, θ, ω) is proportional to the amount of information δ(θ₀, ω), which would be lost if the simplified model M₀ were used as a proxy for the assumed model M. For any prior distribution π(θ, ω), the appropriate normative solution is obtained by rejecting the null model M₀ whenever the corresponding posterior expectation ∫∫δ(θ₀, θ, ω)π(θ, ω|x)dθdω is sufficiently large. Specification of a subjective prior is always difficult, and often polemical, in scientific communication. Information theory may be used to specify a prior, the reference prior, which only depends on the assumed model M, and mathematically describes a situation where no prior information is available about the quantity of interest. The reference posterior expectation, d(θ₀, x) =∫δπ(δ|x)dδ, of the amount of information δ(θ₀, θ, ω) which could be lost if the null model were used, provides an attractive nonnegative test function, the intrinsic statistic, which is invariant under reparametrization. The intrinsic statistic d(θ₀, x) is measured in units of information, and it is easily calibrated (for any sample size and any dimensionality) in terms of some average log‐likelihood ratios. The corresponding Bayes decision rule, the Bayesian reference criterion (BRC), indicates that the null model M₀ should only be rejected if the posterior expected loss of information from using the simplified model M₀ is too large or, equivalently, if the associated expected average log‐likelihood ratio is large enough. The BRC criterion provides a general reference Bayesian solution to hypothesis testing which does not assume a probability mass concentrated on M₀ and, hence, it is immune to Lindley's paradox. The theory is illustrated within the context of multivariate normal data, where it is shown to avoid Rao's paradox on the inconsistency between univariate and multivariate frequentist hypothesis testing.