Advanced survey estimation methods for treatment of non-sampling errors 2
|Convenor||Dr Alina Matei (University of Neuchatel, Switzerland )|
|Coordinator 1||Professor Giovanna Ranalli (University of Perugia, Italy)|
We propose a new method of wage decomposition, with the intention of going beyond the simple inspection of the difference of average values. The goal is to achieve a deeper level of analysis of the phenomenon of gender wage discrimination. This new method uses calibration to generate a counterfactual distribution of wages, allowing for a proper examination throughout the entire distribution without making any assumptions about the parameters of the underlying model behind the data. We compare our method with the existing methods in the literature.
In the province of Quebec, Canada, the forest is examined through regular inventories. Requirements for the spreading and the type of trees and for the cost are difficult to manage. We show that modern and advanced sampling techniques can be used to improve the planning of the forest inventories, even if there are many requirements. Our design includes balanced sampling, highly stratified balanced sampling and sample spreading through a two stage sample. The impact of these techniques on the satisfaction of the requirements and on the precision of survey estimates is investigated using field data from a Quebec inventory.
In this paper we propose two ratio and ratio type exponential estimator for the estimation of population coefficient of variation using the auxiliary information in two-stage sampling. The properties of these estimators are derived up to first order of approximation. The efficiency conditions under which suggested estimator are more efficient, are obtained. Numerical and simulated studies are conducted to support the superiority of the estimators. Theoretically and numerically, we have found that our proposed estimator is always more efficient as compared to its competitor estimator.