A consistent test of significance

This paper presents a test of significance consistent under nonparametric alternatives. Under the null hypothesis, a regressor has no effect on the regression model. Our statistic does not require to estimate the model on the alternative hypothesis, which is left unspecified. Hence, no smoothing techniques are required. The statistic is a weighted empirical process which resembles the Cram~r-von Mises. The asymptotic test is consistent under Pitman's alternatives converging to the null at arate n-1/2. A Monte-Cario experiment illustrates the performance ofthe test in small samples. We also inelude two applications involving biomedical and acid rain data

Solving multi-objective hub location problems by hybrid algorithms

In many logistic, telecommunications and computer networks, direct routing of commodities between any origin and destination is not viable due to economic and technolog- ical constraints. In that cases, a network with centralized units, known as hub facilities, and a small number of links is commonly used to connect any origin-destination pair. The purpose of these hub facilities is to consolidate, sort and transship e ciently any commodity in the network. Hub location problems (HLPs) consider the design of these networks by locating a set of hub facilities, establishing an interhub subnet, and routing the commodities through the network while optimizing some objective(s) based on the cost or service. Hub location has evolved into a rich research area, where a huge number of papers have been published since the seminal work of O'Kelly [1]. Early works were focused on analogue facility location problems, considering some assumptions to simplify network design. Recent works [2] have studied more complex models that relax some of these assumptions and in- corporate additional real-life features. In most HLPs considered in the literature, the input parameters are assumed to be known and deterministic. However, in practice, this assumption is unrealistic since there is a high uncertainty on relevant parameters, such as costs, demands or even distances. In this work, we will study the multi-objective hub location problems with uncertainty.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Consistent tests of conditional moment restrictions

We propose two classes of consistent tests in parametric econometric models defined through multiple conditional moment restrictions. The first type of tests relies on nonparametric estimation, while the second relies on a functional of a marked empirical process. For both tests, a simulation procedure for obtaining critical values is shown to be asymptotically valid. Finite sample performances of the tests are investigated by means of several Monte-Carlo experiments.Publicad

Asymptotic and bootstrap specification tests of nonlinear in variable econometric models

We address the issue of consistent specification testing in general econometric models definedı by multiple moment conditions. We develop two c1asses of moment conditions based tests. The first class of tests depends upon nonparametric functions that are estimated by kernel smoothers. The second class of tests depends upon a marked empirical process. Asymptotic and bootstrap versions of these tests are formally justified, and their finite sample performances are investigated by means of Monte-CarIo experiments

Solving Multi-Objective Hub Location Problems with Robustness

Hub location problems (HLP) are considered in many logistic, telecommunications, and computer problems, where the design of these networks are optimized based on some objective(s) related to the cost or service. In those cases, direct routing between any origin and destination is not viable due to economic or technological constraints. From the seminal work of O'Kelly~\cite{OKelly86}, a huge number of works have been published in the literature. Early contributions were focused on analogue facility location problems, considering some assumptions to simplify the network design. Recent works have studied more complex models by incorporating additional real-life features and relaxing some assumptions, although the input parameters are still assumed to be known in most of the HLPs considered in the literature. This assumption is unrealistic in practice, since there is a high uncertainty on relevant parameters of real problems, such as costs, demands, or even distances. Consequently, a decision maker usually prefer several solutions with a low uncertainty in their objectives functions instead of the optimum solution of an assumed deterministic objective function. In this work we use a three-objective Integer Linear Programming model of the p-hub location problem where the average transportation cost, its variance, and the processing time in the hubs are minimized. The number of variables is $O(n^4)$ where $n$ is the number of nodes of the graph. ILP solvers can only solve small instances of the problems and we propose in this work the use of a recent hybrid algorithm combining a heuristic and exact methods: Construct, Merge, Solve, and AdaptUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Consistent specification testing of quantile regression models

This paper introduces a specification testing procedure for quantile regression functions consistent in the direction of nonparametric alternatives. We consider test statistics based on a marked empirical process which does not require to estimate nonparametrically the true model. In general, the tests are not distribution free, but critical values can be consistentIy approximated using a residual based bootstrap. A small Monte Cario experiment shows that the test works fairly well in practice

Análisis sísmico de estructuras pilotadas sobre suelos viscoelásticos y poroelásticos mediante un modelo acoplado BEM/FEM

XVI Congreso Nacional de Ingeniería Mecánica, celebrado en León en 2004El análisis sísmico de estructuras pilotadas debe de representar de fonna rigurosa el comportamiento dinámico de los pilotes, en especial para grandes estructuras en las que pueden tener relevancia los fenómenos de variabilidad espacial de la excitación, interacción entre distintas cimentaciones o efectos locales debidos a la topografía y geología. Se presenta un modelo numérico 3D, en el que se acoplan elementos de contorno y elementos finitos tipo barra, que pennite modelar de una fonna completa la estructura y la cimentación por pilotaje, incluyendo el suelo como un medio viscoelástico o poroelástico. La excitación sísmica se modela como una onda incidente, de tipo SH, con diferentes ángulos de incidencia respecto a la estructura. De esta fonna se tienen en cuenta todos los efectos mencionados.Seismic analysis of structures founded on piles should properly represent dynamic behavior of piles, particularly for large structures where spatial variability of seismic excitation, interaction between foundation and local effects can take place. A 3D coupled BEM-FEM numerical model is presented coupling beam-type finite elements with boundary elements. lt allows to represent the fully soil-pile-structure system. The soil is modeled as a viscoelastic or poroelastic medium. The seismic excitation is represented as a SH-type incident wave incoming with severa! incidence angles. All mentioned effects are considered

Managing Waiting Times to Predict No-shows and Cancelations at a Children’s Hospital

Purpose: Since long waits in hospitals have been found to be related to high rates of no-shows and cancelations, managing waiting times should be considered as an important tool that hospitals can use to reduce missed appointments. The aim of this study is to analyze patients’ behavior in order to predict no-show and cancelation rates correlated to waiting times. Design/methodology/approach: This study is based on the data from a US children’s hospital, which includes all the appointments registered during one year of observation. We used the call-appointment interval to establish the wait time to get an appointment. Four different types of appointment-keeping behavior and two types of patients were distinguished: arrival, no-show, cancelation with no reschedule, and cancelation with reschedule; and new and established patients. Findings: Results confirmed a strong impact of long waiting times on patients’ appointment-keeping behavior, and the logarithmic regression was found as the best-fit function for the correlation between variables in all cases. The correlation analysis showed that new patients tend to miss appointments more often than established patients when the waiting time increases. It was also found that, depending on the patients’ appointment distribution, it might get more complicated for hospitals to reduce missed appointments as the waiting time is reduced. Originality/value: The methodology applied in our study, which combines the use of regression analysis and patients’ appointment distribution analysis, would help health care managers to understand the initial implications of long waiting times and to address improvement related to patient satisfaction and hospital performance.Peer Reviewe