A comparison study on criteria to select the most adequate weighting matrix

The practice of spatial econometrics revolves around a weighting matrix, which is often supplied by the user on previous knowledge. This is the so-called W issue. Probably, the aprioristic approach is not the best solution although, presently, there are few alternatives for the user. Our contribution focuses on the problem of selecting aWmatrix from among a finite set of matrices, all of them considered appropriate for the case. We develop a new and simple method based on the entropy corresponding to the distribution of probability estimated for the data. Other alternatives, which are common in current applied work, are also reviewed. The paper includes a large study of Monte Carlo to calibrate the effectiveness of our approach compared to others. A well-known case study is also included

Decision support model for the selection of asphalt wearing courses in highly trafficked roads

The suitable choice of the materials forming the wearing course of highly trafficked roads is a delicate task because of their direct interaction with vehicles. Furthermore, modern roads must be planned according to sustainable development goals, which is complex because some of these might be in conflict. Under this premise, this paper develops a multi-criteria decision support model based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution to facilitate the selection of wearing courses in European countries. Variables were modelled using either fuzzy logic or Monte Carlo methods, depending on their nature. The views of a panel of experts on the problem were collected and processed using the generalized reduced gradient algorithm and a distance-based aggregation approach. The results showed a clear preponderance by stone mastic asphalt over the remaining alternatives in different scenarios evaluated through sensitivity analysis. The research leading to these results was framed in the European FP7 Project DURABROADS (No. 605404).The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 605404

Public initiatives of settlement transformation. A theoretical-methodological approach to selecting tools of multi-criteria decision analysis

In Europe, the operating context in which initiatives of settlement transformation are currently initiated is characterized by a complex, elaborate combination of technical, regulatory and governance-related factors. A similar set of considerations makes it necessary to address the complex decision-making problems to be resolved through multidisciplinary, comparative approaches designed to rationalize the process and treat the elements to be considered in systematic fashion with respect to the range of alternatives available as solutions. Within a context defined in this manner, decision-making processes must often be used to obtain multidisciplinary and multidimensional analyses to support the choices made by the decision-makers. Such analyses are carried out using multi-criteria tools designed to arrive at syntheses of the numerous forms of input data needed to describe decision-making problems of similar complexity, so that one or more outcomes of the synthesis make possible informed, well thought-out, strategic decisions. The technical literature on the topic proposes numerous tools of multi-criteria analysis for application in different decision-making contexts. Still, no specific contributions have been drawn up to date on the approach to take in selecting the tool best suited to providing adequate responses to the queries of evaluation that arise most frequently in the various fields of application, and especially in the settlement sector. The objective of this paper is to propose, by formulating a taxonomy of the endogenous and exogenous variables of tools of multi-criteria analysis, a methodology capable of selecting the tool best suited to the queries of evaluation which arise regarding the chief categories of decision-making problems, and particularly in the settlement sector

On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies

The broader goal of this paper is to provide social researchers with some analytical guidelines when investigating structural equation models (SEM) with predominantly a formative specification. This research is the first to investigate the robustness and precision of parameter estimates of a formative SEM specification. Two distinctive scenarios (normal and non-normal data scenarios) are compared with the aid of a Monte Carlo simulation study for various covariance-based structural equation modeling (CBSEM) estimators and various partial least squares path modeling (PLS-PM) weighting schemes. Thus, this research is also one of the first to compare CBSEM and PLS-PM within the same simulation study. We establish that the maximum likelihood (ML) covariance-based discrepancy function provides accurate and robust parameter estimates for the formative SEM model under investigation when the methodological assumptions are met (e.g., adequate sample size, distributional assumptions, etc.). Under these conditions, ML-CBSEM outperforms PLS-PM. We also demonstrate that the accuracy and robustness of CBSEM decreases considerably when methodological requirements are violated, whereas PLS-PM results remain comparatively robust, e.g. irrespective of the data distribution. These findings are important for researchers and practitioners when having to choose between CBSEM and PLS-PM methodologies to estimate formative SEM in their particular research situation.PLS, path modeling, covariance structure analysis, structural equation modeling, formative measurement, simulation study

On the Use of Formative Measurement Specifications in Structural Equation Modeling: A Monte Carlo Simulation Study to Compare Covariance-Based and Partial Least Squares Model Estimation Methodologies

The broader goal of this paper is to provide social researchers with some analytical guidelines when investigating structural equation models (SEM) with predominantly a formative specification. This research is the first to investigate the robustness and precision of parameter estimates of a formative SEM specification. Two distinctive scenarios (normal and non-normal data scenarios) are compared with the aid of a Monte Carlo simulation study for various covariance-based structural equation modeling (CBSEM) estimators and various partial least squares path modeling (PLS-PM) weighting schemes. Thus, this research is also one of the first to compare CBSEM and PLS-PM within the same simulation study. We establish that the maximum likelihood (ML) covariance-based discrepancy function provides accurate and robust parameter estimates for the formative SEM model under investigation when the methodological assumptions are met (e.g., adequate sample size, distributional assumptions, etc.). Under these conditions, ML-CBSEM outperforms PLS-PM. We also demonstrate that the accuracy and robustness of CBSEM decreases considerably when methodological requirements are violated, whereas PLS-PM results remain comparatively robust, e.g. irrespective of the data distribution. These findings are important for researchers and practitioners when having to choose between CBSEM and PLS-PM methodologies to estimate formative SEM in their particular research situation.marketing ;

Identification of Public Objectives Related to Agricultural Sector Support

The Common Agricultural Policy (CAP) is a widely debated policy in terms of both its budget and its instruments. In order to serve the citizens of Europe properly, CAP requires optimal identification of the public objectives desired. This paper aims to analyse the relative weights that citizens assign to the various potential objectives of the CAP and to show how these can be used to improve the selection of policy instruments. As a means of identifying social preferences we used the Analytical Hierarchy Process (AHP) technique on a population sample in Castilla y León (Spain). Results show how the current policy decision process lacks mechanisms capable of identifying social preferences and thus leading to the choice of sub-optimal policies.Common Agricultural Policy, Objectives, Social preferences, AHP, Castilla y León.

Insensitive control technology development

THe investigation of two insensitive controller synthesis techniques was reported. The finite dimensional inverse approach produces a time varying insensitive controller and/or parameter identifier by constructing inverse functions derived from a finite number of input output pair relationships. The MD/IM concept relies on the information matrix theory that was developed in the estimation and identification field. The MD/IM synthesis technique is based on the hypothesis that minimizing the information matrix will reduce system identifiability and consequently system sensitivity to uncertain parameters. The controllers designed with both techniques were evaluated on a realistic C-5A aircraft flight control problem. Results indicate that the FDI controller is more suited to trajectory type problems because of its time varying nature. The MD/IM controller performed as well as the top-rated controllers of the initial effort and has direct application to aircraft flight control problems