Hierarchical Bayesian Approach for Addressing Multiple Objectives in Poverty Research for Small Areas.
Nowadays the information extracted from data should be the key to good policy, therefore, analysts must make the best possible use of all available information. However, data availability often is limited by cost or for other reasons. Consequently, there is the need to use data from different sources. Our goals are to develop hierarchical models and to demonstrate their ability to improve inferences about quantities for which there are meager data. When a hierarchical model can be found to re...
Using the Quality Function Deployment methodology for effective planning of teaching and learning processes.
This paper offers a reflection about the use of Quality Function Deployment (QFD) applied to a university course of accounting. The aim is to classify the most effective teaching methods (teaching strategies) with respect to specific relations: targets (stakeholders’ needs)/needs (students’ needs). In particular, learning outcomes (LO) are expressed in terms of homogeneity or heterogeneity in learning (at the beginning, underway and outbound) that a class of students shows with respect to the...
Are Musicians Entrepreneurs? A Preliminary Analysis
In this narrative literature review, we employed the grounded theory for studying the scientific debate, the contradictions, and the tensions between entrepreneurship and music activity. In particular, this work represents a preliminary study for a more in-depth future analysis of this relationship. The analysis let emerge two superordinate structures, five themes, and eight subthemes. The two superordinate structures represent the most relevant tensions we found in the analyzed articles...
Inference for big data assisted by small area methods: an application to OBEC (on-line based enterprise characteristics)
Nowadays, the availability of a huge amount of data produced by a wide range of new technologies, so-called big data, is increasing. However, data obtain- able from big data sources are often the result of a non-probability sampling process and adjusting for the selection bias is an important practical problem. In this paper, we propose a novel method of reducing the selection bias associated with the big data source in the context of Small Area Estimation (SAE). Our approach is based on data...
On bias correction in small area estimation: An M-quantile approach
In this paper we propose two bias correction approaches in order to reduce the prediction bias of the robust M-quantile predictors in small area estimation in the presence of representative outliers. A bootstrap procedure is considered for the estimation of the mean squared error. A Monte-Carlo simulation study is conducted. Results confirm that our approaches improve the efficiency and reduce the predic- tion bias of M-quantile predictors when the population contains units that may be influe...
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