Estimation and prediction for spatial generalized linear mixed models using high order Laplace approximation

Estimation and prediction in generalized linear mixed models are often hampered by intractable high dimensional integrals. This paper provides a framework to solve this intractability, using asymptotic expansions when the number of random effects is large. To that end, we first derive a modified Laplace approximation when the number of random effects is increasing at a lower rate than the sample size. Second, we propose an approximate likelihood method based on the asymptotic expansion of the log-likelihood using the modified Laplace approximation which is maximized using a quasi-Newton algorithm. Finally, we define the second order plug-in predictive density based on a similar expansion to the plug-in predictive density and show that it is a normal density. Our simulations show that in comparison to other approximations, our method has better performance. Our methods are readily applied to non-Gaussian spatial data and as an example, the analysis of the rhizoctonia root rot data is presented.     

A Sensitivity Analysis Approach for Some Deterministic Multi-Criteria Decision-Making Methods*

Often, data in multi-criteria decision making (MCDM) problems are imprecise and changeable. Therefore, an important step in many applications of MCDM is to perform a sensitivity analysis on the input data. This paper presents a methodology for performing a sensitivity analysis on the weights on the decision criteria and the performance values of the alternatives expressed in terms of the decision criteria. The proposed methodology is demonstrated on three widely used decision methods. These methods are the weighted sum model (WSM), the weighted product model (WPM), and the analytic hierarchy process (AHP). This paper formalizes a number of important issues on sensitivity analysis and derives some critical theoretical results. Also, a number of illustrative examples and computational experiments further illustrate the application of the proposed methodology.     

Robot Map Verification of a Graph World

In the map verification problem, a robot is given a (possibly incorrect) map M of the world G with its position and orientation indicated on the map. The task is to find out whether this map, for the given robot position and its orientation in the map, is correct for the world G. We consider the world model of a graph G = (V _G, E _G) in which, for each vertex, edges incident to the vertex are ordered cyclically around that vertex. (This also holds for the map M = (V _M, E _M.) The robot can traverse edges and enumerate edges incident on the current vertex, but it cannot distinguish vertices (and edges) from each other. To solve the verification problem, the robot uses a portable edge marker, that it can put down at an edge of the graph world G and pick up later as needed. The robot can recognize the edge marker when it encounters it in the world G. By reducing the verification problem to an exploration problem, verification can be completed in O(|V _G| × |E _G|) edge traversals (the mechanical cost) with the help of a single vertex marker which can be dropped and picked up at vertices of the graph world (G. Dudek, M. Jenkin, E. Milios, and D. Wilkes, IEEE Trans. on Robotics and Automation, vol. 7, pp. 859–865, 1991; Robotics and Autonomous Systems, vol. 22(2), pp. 159–178, 1997). In this paper, we show a strategy that verifies a map in O(|V _M|) edge traversals only, using a single edge marker, when M is a plane embedded graph, even though G may not be planar (e.g., G may contain overpasses, tunnels, etc.).     

Macroeconomic and financing determinants of out of pocket payments in health care: Evidence from selected OECD countries

In this paper, we examine the impact of macroeconomic, as well public and private health insurance financing (PHI) factors on out of pocket (OOP) healthcare expenditures, by using fixed/random effects and dynamic panel data methodology to a dataset of 26 EU and OECD countries for a period lasting from 1995 to 2013. The existing empirical literature has focused on testing the hypothesis that several macroeconomic and health financing determinants have an effect on OOP healthcare expenditures. Nevertheless, the related articles have not well tested the hypothesis concerning the potential impact of PHI financing on OOP spending. We find that public and PHI financing have a significant countervailing effect on OOP spending. Moreover, we show that unemployment rate has a significant positive impact on OOP expenditures. Sensitivity tests with variation of specifications and samples show that our findings are robust. We argue that policy-makers should give serious consideration to PHI institution; our results indicate that there is an inverse effect on OOP spending. We suggest that our examined countries have to provide financial risk protection to their citizens against OOP payments, rather than only attending health budgetary retrenchments in order to adjust public finances.     

Optimal predictive design augmentation for spatial generalised linear mixed models

A typical model for geostatistical data when the observations are counts is the spatial generalised linear mixed model. We present a criterion for optimal sampling design under this framework which aims to minimise the error in the prediction of the underlying spatial random effects. The proposed criterion is derived by performing an asymptotic expansion to the conditional prediction variance. We argue that the mean of the spatial process needs to be taken into account in the construction of the predictive design, which we demonstrate through a simulation study where we compare the proposed criterion against the widely used space-filling design. Furthermore, our results are applied to the Norway precipitation data and the rhizoctonia disease data.     

Ranking irregularities when evaluating alternatives by using some ELECTRE methods

The ELECTRE II and III methods enjoy a wide acceptance in solving multi-criteria decision-making (MCDM) problems. Research results in this paper reveal that there are some compelling reasons to doubt the correctness of the proposed rankings when the ELECTRE II and III methods are used. In a typical test we first used these methods to determine the best alternative for a given MCDM problem. Next, we randomly replaced a non-optimal alternative by a worse one and repeated the calculations without changing any of the other data. Our computational tests revealed that sometimes the ELECTRE II and III methods might change the indication of the best alternative. We treat such phenomena as rank reversals. Although such ranking irregularities are well known for the additive variants of the AHP method, it is the very first time that they are reported to occur when the ELECTRE methods are used. These two methods are also evaluated in terms of two other ranking tests and they failed them as well. Two real-life cases are described to demonstrate the occurrence of rank reversals with the ELECTRE II and III methods. Based on the three test criteria presented in this paper, some computational experiments on randomly generated decision problems were executed to test the performance of the ELECTRE II and III methods and an examination of some real-life case studies are also discussed. The results of these examinations show that the rates of the three types of ranking irregularities were rather significant in both the simulated decision problems and the real-life cases studied in this paper.