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Models of various physical, biological, and artificial phenomena follow a power law over multiple magnitudes. This article presents a modified Pareto model with an upside-down shape and an adjustable right tail. The moments, quantiles, failure rate, mean residual life, and quantile residual life functions are examined. In addition, some stochastic ordering characteristics of the proposed model are investigated. The estimation of the parameters using the maximum likelihood estimator, the mean square error, and the Anderson–Darling estimator is explored, and a simulation study is conducted to analyze their behavior. Finally, we compare the proposed model with alternative methods for describing a dataset on the strength of carbon fiber and a dataset on customer waiting times in a bank.

Many variables encountered in practice have skewed distributions. While the sample mean is unbiased for the true mean regardless of the underlying distribution that generated the sample observations, it can be highly unstable in the context of skewed distributions. To cope with this problem, we propose an efficient estimator of the population mean based on the concept of conditional bias of a unit, which can be viewed as a measure of its influence. The idea is to reduce the impact of the sample units that have a large influence. The resulting estimator depends on a cut‐off value. We suggest selecting the cut‐off value that minimizes the maximum absolute estimated conditional bias with respect to the proposed estimator. An estimator of the mean square error is also presented. An empirical investigation comparing several estimators in terms of relative bias and relative efficiency suggests that the proposed estimator and the estimator of its mean square error perform well for a wide class of distributions. [ABSTRACT FROM AUTHOR]

Measures of economic well-being are often needed for geographically small areas, as economic indicators may be distributed unevenly among the subsets of relatively small regions. We consider small area estimation of average equivalized income. Disposable household income data are usually available only for a sample of households, typically too small to provide reliable estimates for small regions. We consider a small area estimation technique that is robust to outliers, produces results consistent with design weighted estimates obtained for larger areas and yield maps with approximately no shrinkage. The proposed methodology is applied to the Local Labour Systems in Tuscany (Italy). [ABSTRACT FROM AUTHOR]

This paper considers a dynamic extension of the classical error components model based on the ideas of structural time series models. The study concentrates on the mean square error estimation of time-dependent means by using the Kalman filter, and on the relative efficiency of these estimators as a function of both the number of observations across units and time. [ABSTRACT FROM AUTHOR]

General procedures are described to generate quantitative randomized response (RR) required to estimate the finite population total of a sensitive variable. Permitting sample selection with arbitrary probabilities a formula for the mean square error (MSE) of a linear estimator of total based on RR is noted indicating the simple modification over one that might be based on direct response (DR) if the latter were available. A general formula for an unbiased estimator of the MSE is presented. A simple approximation is proposed in case the RR ratio estimator is employed based on a simple random sample (SRS) taken without replacement (WOR). Among sampling strategies employing unbiased but not necessarily linear estimators based on RR, certain optimal ones are identified under two alternative models analogously to well-known counterparts based on DR, if available. Unlike Warner's (1965) treatment of categorical RR we consider quantitative RR here. [ABSTRACT FROM AUTHOR]