A Predictive Estimator of the Mean with Missing Data

One of the most difficult problems confronting investigators who analyze data from surveys is how treat missing data. Many statistical procedures can not be used immediately if any values are missing. This paper considers the problem of estimating the population mean using auxiliary information when some observations on the sample are missing and the population mean of the auxiliary variable is not available. We use tools of classical statistical estimation theory to find a suitable estimator. We study the model and design properties of the proposed estimator. We also report the results of a broad-based simulation study of the efficiency of the estimator, which reveals very promising results.     

Goodness of Fit for Discrete Random Variables Using the Conditional Density

In this paper we find a new test of goodness of fit in the case of discrete random variables. The main advantage of the methodology proposed in this paper relies on the fact that given the sample, we can control the probability of the type I error, that is α, and then find the exact value of the probability of the type II error, β, associated, in some cases. The results are not asymptotic, but exact. Also a conditional test for two alternatives is obtained. We also include some simulations in order to check the power of the procedures.Mathematics Subject Classification (2000) Primary 62G10 · 62B05 · Secondary 62E10     

The contingent influence of organizational capabilities on proactive environmental strategy in the service sector: An analysis of North American and European ski resorts

Our study of 134 North American and European ski resorts examines the influence of externally focused organizational capabilities on the generation of proactive environmental strategies under contingent effects of uncertainty in the general business environment. We find that the capabilities of strategic proactivity and continuous innovation are associated with proactive environmental strategies. Managerial perceptions of uncertainty in the general business environment moderate the deployment of the capability of continuous innovation at all levels of uncertainty and stakeholder engagement at low and average levels of uncertainty. The study contributes to the resource‐based view (RBV) by illuminating an important contingency under which capabilities are likely to be deployed to generate a proactive corporate strategy. Copyright © 2007 ASAC. Published by John Wiley & Sons, Ltd.     

Calibration methods for estimating quantiles

We propose a calibrated estimator of the quantiles of sample survey data and discuss the asymptotic theory behind it. This estimator is defined for any sampling design and uses the information available on J auxiliary variables. A simulation study based on a real population is used to compare the estimator with various methods proposed previously.     

Successive Sampling to Estimate Quantiles with P-Auxiliary Variables

The successive sampling is a known technique that can be used in longitudinal surveys to estimate population parameters and measurements of difference or change of a study variable. The paper discusses the estimation of quantiles for the current occasion based on sampling in two successive occasions and using p-auxiliary variables obtained of the previous occasion. A multivariate ratio estimator from the matched portion is used to provide the optimum estimate of a quantile by weighting the estimates inversely to derived optimum weights. Its properties are studied under large–sample approximation and the expressions of the variances are established. The behavior of these asymptotic variances is analyzed on the basis of data from natural populations. A simulation study is also used to measure the precision of the proposed estimator.     

Repeated substitution method: The ratio estimator for the population variance

This paper proposes a new unbiased estimator for the population variance in finite population sample surveys using auxiliary information. This estimator has a smaller mean squared error than the conventional unbiased estimator, the ratio estimator established by Isaki (1983) and it has the same precision than the regression estimator. Furthermore, it is a much more interesting estimator from the computation viewpoint.     

Random non-response in multi-character surveys

In the present investigation, we have made first shot to propose an alternative estimator of population total, in the presence of random non-response, for multi-characteristics by using probability proportional to size and without replacement (PPSWOR) sampling when the selection probabilities are poorly correlated with the study variables. The mean square error (MSE) expressions are derived for the proposed estimator. The behavior of the proposed estimator has been examined under super population model. An empirical study has also been carried out to look into the performance of the proposed estimator. The proposed estimator has been applied to real data set.     

On estimating the median from survey data using multiple auxiliary information

A new method to derive confidence intervals for medians in a finite population is presented. This method uses multi-auxiliary information through a multivariate regression type estimator of the population distribution function. A simulation study based on four real populations shows its behaviour versus other known methods.     

Model-calibration estimation of the distribution function using nonparametric regression

Nonparametric regression has only recently been employed in the estimation of finite population parameters in a model-assisted framework. This paper proposes a new calibration estimator for the distribution function using nonparametric methods to obtain the fitted values on which to calibrate. The proposed estimator is a genuine distribution function that presents several attractive features. In terms of relative efficiency and relative bias, the behaviour of the proposed estimator is compared to other known estimators in a limited simulation study on real populations.     

Durbin’s random substitution and conditional Monte Carlo

Durbin (Biometrika 48:41–55, 1961) proposed a method called random substitution, by which a composite problem of goodness-of-fit can be reduced to a simple one. In this paper we provide a method of finding the p-value of any test statistic, for a composite goodness-of-fit problem, based on the simulation of a large number of conditional samples, using an analog of Durbin’s proposal in a reverse-type application. We analyze a Bayesian chi-square test proposed in Johnson (Ann Stat 32:2361–2384, 2004) which relies on a single randomization and relate it with Durbin’s original method. We also review a related proposal for conditional Monte-Carlo simulation in Lindqvist and Taraldsen (Biometrika 92:451–464, 2005) and compare it with our procedure. We show our method in a non-group example introduced in Lindqvist and Taraldsen (Biometrika 90:489–490, 2003).