Student Performance in Traditional vs. Online Format: Evidence from an MBA Level Introductory Economics Class

All previous studies comparing online and face-to-face format for instruction of economics compared courses that were either online or face-to-face format and regressed exam scores on selected student characteristics. This approach is subject to the econometric problems of self-selection omitted unobserved variables. Our study uses two methods to deal with these problems. First we eliminate self-selection bias by using students from a course that uses both instruction formats. Second, we use the exam questions as the unit of observation, and eliminate omitted variable bias by using an indicator variable for each student to capture the effect of differences in unobserved student characteristics on learning outcomes. We report the finding that students had a significantly greater chance of answering a question correctly if it came from a chapter covered online.

Bayesian adaptive designs in early phase clinical trials

There are two practical challenges in the phase I clinical trial conduct: lack of transparency to physicians, and the late onset toxicity. In my dissertation, Bayesian approaches are used to address these two problems in clinical trial designs. The proposed simple optimal designs cast the dose finding problem as a decision making process for dose escalation and deescalation. The proposed designs minimize the incorrect decision error rate to find the maximum tolerated dose (MTD). For the late onset toxicity problem, a Bayesian adaptive dose-finding design for drug combination is proposed. The dose-toxicity relationship is modeled using the Finney model. The unobserved delayed toxicity outcomes are treated as missing data and Bayesian data augment is employed to handle the resulting missing data. Extensive simulation studies have been conducted to examine the operating characteristics of the proposed designs and demonstrated the designs' good performances in various practical scenarios.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Strontium and neodymium isotope data for barite separates and pore waters, and isotope and concentration data for foraminifera and fish teeth from DSDP Leg 85 sediments

Strontium and neodymium isotopic data are reported for barite samples chemically separated from Late Miocene to Pliocene sediments from the eastern equatorial Pacific. At a site within a region of very high productivity close to the equator, 87Sr/86Sr ratios in the barite separates are indistinguishable from those of foraminifera and fish teeth from the same samples. However, at two sites north of the productivity maximum barite separates have slightly, but consistently lower (averaging 0.000062) ratios than the coexisting phases, although values still fall within the total range of published values for the contemporaneous seawater strontium isotope curve. We examine possible causes for this offset including recrystallization of the foraminifera, fish teeth or barite, the presence of non-barite contaminants, or incorporation of older, reworked deep-sea barite; the inclusion of a small amount of hydrothermal barite in the sediments seems most consistent with our data, although there are difficulties associated with adequate production and transportation of this phase. Barite is unlikely to replace calcite as a preferred tracer of seawater strontium isotopes in carbonate-rich sediments, but may prove a useful substitute in cases where calcite is rare or strongly affected by diagenesis. In contrast to the case for strontium, neodymium isotopic ratios in the barite separates are far from expected values for contemporary seawater, and appear to be dominated by an (unobserved) eolian component with high neodymium concentration and low 143Nd/144Nd. These results suggest that the true potential of barite as an indicator of paleocean neodymium isotopic ratios and REE patterns will be realized only when a more selective separation procedure is developed.

Geochemistry of basement carbonates from ODP Site 801 in the western Pacific Ocean

Many studies argue, based partly on Pb isotopic evidence, that recycled, subducted slabs reside in the mantle source of ocean island basalts (OIB) (Hofmann and White, 1982, doi:10.1016/0012-821X(82)90161-3; Weaver, 1991 doi:10.1016/0012-821X(91)90217-6; Lassiter, and Hauri, 1998, doi:10.1016/S0012-821X(98)00240-4). Such models, however, have remained largely untested against actual subduction zone inputs, due to the scarcity of comprehensive measurements of both radioactive parents (Th and U) and radiogenic daughter (Pb) in altered oceanic crust (AOC). Here, we discuss new, comprehensive measurements of U, Th, and Pb concentrations in the oldest AOC, ODP Site 801, and consider the effect of subducting this crust on the long-term Pb isotope evolution of the mantle. The upper 500 m of AOC at Site 801 shows >4-fold enrichment in U over pristine glass during seafloor alteration, but no net change to Pb or Th. Without subduction zone processing, ancient AOC would evolve to low 208Pb/206Pb compositions unobserved in the modern mantle (Hart and Staudigel, 1989 [Isotopic characterization and identification of recycled components, in: Crust/Mantle Recycling at Convergence Zones, Eds. S.R. Hart, L. Gqlen, NATO ASI Series. Series C: Mathematical and Physical Sciences 258, pp. 15-28, D. Reidel Publishing Company, Dordrecht-Boston, 1989]). Subduction, however, drives U-Th-Pb fractionation as AOC dehydrates in the earth's interior. Pacific arcs define mixing trends requiring 8-fold enrichment in Pb over U in AOC-derived fluid. A mass balance across the Mariana subduction zone shows that 44-75% of Pb but <10% of U is lost from AOC to the arc, and a further 10-23% of Pb and 19-40% of U is lost to the back-arc. Pb is lost shallow and U deep from subducted AOC, which may be a consequence of the stability of phases binding these elements during seafloor alteration: U in carbonate and Pb in sulfides. The upper end of these recycling estimates, which reflect maximum arc and back-arc growth rates, remove enough Pb and U from the slab to enable it to evolve rapidly (<<0.5 Ga) to sources suitable to explain the 208Pb/206Pb isotopic array of OIB, although these conditions fail to simultaneously satisfy the 207Pb/206Pb system. Lower growth rates would require additional U loss (29%) at depths beyond the zones of arc and back-arc magmagenesis, which would decrease upper mantle kappa (232Th/238U) over time, consistent with one solution to the "kappa conundrum" (Elliott et al., 1999, doi:10.1016/S0012-821X(99)00077-1). The net effects of alteration (doubling of l [238U/204Pb]) and subduction (doubling of omega [232Th/204Pb]) are sufficient to create the Pb isotopic signatures of oceanic basalts.

Exploring intra-commonwealth goods and services trade

A background paper for the Commonwealth Secretariat by Anirudh Shingal and Mohammad Razzaque: Existing work examining the trade effect of commonwealth membership does not account for sample selection, unobserved heterogeneity and multilateral resistance in estimation, leading to biased estimates. Our analyses improve on all these fronts. Unlike earlier work, we also consider services trade and assemble a much larger sample of trading partners (242 x 242, over 1995-2010). Commonwealth membership is found to increase goods exports by 18.5-33.2% and services exports by 42.8% in our results, ceteris paribus and on average. Our analyses on the determinants of intra-commonwealth trade suggest the positive role of common language (only for goods trade) and colonial relationships as well as the negative impact of geography, thereby confirming that commonwealth member states are not natural trading partners for each other. Finally, being one of Australia, Canada or the UK is associated with 98.2% greater merchandise trade than the commonwealth average; however, a similar effect is not observed for services trade.

The contribution of supply and demand shifts to earnings inequality in urban China

This paper examines the degree to which supply and demand shift across skill groups contributed to the earnings inequality increase in urban China from 1988 to 2002. Product demand shift contributed to an equalizing of earnings distribution in urban China from 1988 to 1995 by increasing the relative product for the low educated. However, it contributed to enlarging inequality from 1995 to 2002 by increasing the relative demand for the highly educated. Relative demand was continuously higher for workers in the coastal region and contributed to a raising of interregional inequality. Supply shift contributed essentially nothing or contributed only slightly to a reduction in inequality. Remaining factors, the largest disequalizer, may contain skill-biased technological and institutional changes, and unobserved supply shift effects due to increasing numbers of migrant workers.

Why do oil importers diversify their import sources politically? : evidence from U.S. firm-level data

International politics affects oil trade. But why? We construct a firm-level dataset for all U.S. oil-importing companies over 1986-2008 to examine what kinds of firms are more responsive to change in "political distance" between the U.S. and her trading partners, measured by divergence in their UN General Assembly voting patterns. Consistent with previous macro evidence, we first show that individual firms diversify their oil imports politically, even after controlling for unobserved firm heterogeneity. We conjecture that the political pattern of oil imports from these individual firms is driven by hold-up risks, because oil trade is often associated with backward vertical FDI. To test this hold-up risk hypothesis, we investigate heterogeneity in responses by matching transaction-level import data with firm-level worldwide reserves. Our results show that long-run oil import decisions are indeed more elastic for firms with oil reserves overseas than those without, although the reverse is true in the short run. We interpret this empirical regularity as that while firms trade in the spot market can adjust their imports immediately, vertically-integrated firms with investment overseas tend to commit to term contracts in the short run even though they are more responsive to changes in international politics in the long run.

The effects of international politics on oil-exporting developing countries

International politics affects oil trade. But does it affect the oil-exporting developing countries more? We construct a firm-level dataset for all U.S. oil-importing companies over 1986-2008 to examine how these firms respond to changes in "political distance" between the U.S. and her trading partners, measured by divergence in their UN General Assembly voting patterns. Consistent with previous macro evidence, we first show that individual firms diversify their oil imports politically, even after controlling for unobserved firm heterogeneity. We conjecture that the political pattern of oil imports from these individual firms is driven by hold-up risks, because oil trade is often associated with backward vertical FDI. To the extent that developing countries have higher hold-up risks because of their weaker institutions, the political effect on oil trade should be more significant in the developing world. We find that oil import decisions are indeed more elastic when firms import from developing countries, although the reverse is true in the short run. Our results suggest that international politics can affect oil revenue and hence long-term development in the developing world.

Political influence in commercial and financial oil trading : the evidence from US firms

International politics affects oil trade. But do financial and commercial traders who participate in spot oil trading also respond to changes in international politics? We construct a firm-level dataset for all U.S. oil-importing companies over 1986-2008 to examine how these firms respond to increases in "political distance" between the U.S. and her trading partners, measured by divergence in their UN General Assembly voting patterns. Consistent with previous macro evidence, we first show that individual firms diversify their oil imports politically, even after controlling for unobserved firm heterogeneity. However, the political pattern of oil imports is not entirely driven by the concerns of hold-up risks, which exist when oil transactions via term contracts are associated with backward vertical FDI that is subject to expropriation. In particular, our results indicate that even financial and commercial traders significantly reduce their oil imports from U.S. political enemies. Interestingly, while these traders diversify their oil imports politically immediately after changes in international politics, other oil companies reduce their oil imports with a significant time lag. Our findings suggest that in designing regulations to avoid harmful repercussions on commodity and financial assets, policymakers need to understand the nature of political risk.

Modelling the Impact of Built Environment, Geographical Scales and Latent Constructs On Individual Travel Behaviour

La relación entre la estructura urbana y la movilidad ha sido estudiada desde hace más de 70 años. El entorno urbano incluye múltiples dimensiones como por ejemplo: la estructura urbana, los usos de suelo, la distribución de instalaciones diversas (comercios, escuelas y zonas de restauración, parking, etc.). Al realizar una revisión de la literatura existente en este contexto, se encuentran distintos análisis, metodologías, escalas geográficas y dimensiones, tanto de la movilidad como de la estructura urbana. En este sentido, se trata de una relación muy estudiada pero muy compleja, sobre la que no existe hasta el momento un consenso sobre qué dimensión del entorno urbano influye sobre qué dimensión de la movilidad, y cuál es la manera apropiada de representar esta relación. Con el propósito de contestar estas preguntas investigación, la presente tesis tiene los siguientes objetivos generales: (1) Contribuir al mejor entendimiento de la compleja relación estructura urbana y movilidad. y (2) Entender el rol de los atributos latentes en la relación entorno urbano y movilidad. El objetivo específico de la tesis es analizar la influencia del entorno urbano sobre dos dimensiones de la movilidad: número de viajes y tipo de tour. Vista la complejidad de la relación entorno urbano y movilidad, se pretende contribuir al mejor entendimiento de la relación a través de la utilización de 3 escalas geográficas de las variables y del análisis de la influencia de efectos inobservados en la movilidad. Para el análisis se utiliza una base de datos conformada por tres tipos de datos: (1) Una encuesta de movilidad realizada durante los años 2006 y 2007. Se obtuvo un total de 943 encuestas, en 3 barrios de Madrid: Chamberí, Pozuelo y Algete. (2) Información municipal del Instituto Nacional de Estadística: dicha información se encuentra enlazada con los orígenes y destinos de los viajes recogidos en la encuesta. Y (3) Información georeferenciada en Arc-GIS de los hogares participantes en la encuesta: la base de datos contiene información respecto a la estructura de las calles, localización de escuelas, parking, centros médicos y lugares de restauración. Se analizó la correlación entre e intra-grupos y se modelizaron 4 casos de atributos bajo la estructura ordinal logit. Posteriormente se evalúa la auto-selección a través de la estimación conjunta de las elecciones de tipo de barrio y número de viajes. La elección del tipo de barrio consta de 3 alternativas: CBD, Urban y Suburban, según la zona de residencia recogida en las encuestas. Mientras que la elección del número de viajes consta de 4 categorías ordinales: 0 viajes, 1-2 viajes, 3-4 viajes y 5 o más viajes. A partir de la mejor especificación del modelo ordinal logit. Se desarrolló un modelo joint mixed-ordinal conjunto. Los resultados indican que las variables exógenas requieren un análisis exhaustivo de correlaciones con el fin de evitar resultados sesgados. ha determinado que es importante medir los atributos del BE donde se realiza el viaje, pero también la información municipal es muy explicativa de la movilidad individual. Por tanto, la percepción de las zonas de destino a nivel municipal es considerada importante. En el contexto de la Auto-selección (self-selection) es importante modelizar conjuntamente las decisiones. La Auto-selección existe, puesto que los parámetros estimados conjuntamente son significativos. Sin embargo, sólo ciertos atributos del entorno urbano son igualmente importantes sobre la elección de la zona de residencia y frecuencia de viajes. Para analizar la Propensión al Viaje, se desarrolló un modelo híbrido, formado por: una variable latente, un indicador y un modelo de elección discreta. La variable latente se denomina “Propensión al Viaje”, cuyo indicador en ecuación de medida es el número de viajes; la elección discreta es el tipo de tour. El modelo de elección consiste en 5 alternativas, según la jerarquía de actividades establecida en la tesis: HOME, no realiza viajes durante el día de estudio, HWH tour cuya actividad principal es el trabajo o estudios, y no se realizan paradas intermedias; HWHs tour si el individuo reaiza paradas intermedias; HOH tour cuya actividad principal es distinta a trabajo y estudios, y no se realizan paradas intermedias; HOHs donde se realizan paradas intermedias. Para llegar a la mejor especificación del modelo, se realizó un trabajo importante considerando diferentes estructuras de modelos y tres tipos de estimaciones. De tal manera, se obtuvieron parámetros consistentes y eficientes. Los resultados muestran que la modelización de los tours, representa una ventaja sobre la modelización de los viajes, puesto que supera las limitaciones de espacio y tiempo, enlazando los viajes realizados por la misma persona en el día de estudio. La propensión al viaje (PT) existe y es específica para cada tipo de tour. Los parámetros estimados en el modelo híbrido resultaron significativos y distintos para cada alternativa de tipo de tour. Por último, en la tesis se verifica que los modelos híbridos representan una mejora sobre los modelos tradicionales de elección discreta, dando como resultado parámetros consistentes y más robustos. En cuanto a políticas de transporte, se ha demostrado que los atributos del entorno urbano son más importantes que los LOS (Level of Service) en la generación de tours multi-etapas. la presente tesis representa el primer análisis empírico de la relación entre los tipos de tours y la propensión al viaje. El concepto Propensity to Travel ha sido desarrollado exclusivamente para la tesis. Igualmente, el desarrollo de un modelo conjunto RC-Number of trips basado en tres escalas de medida representa innovación en cuanto a la comparación de las escalas geográficas, que no había sido hecha en la modelización de la self-selection. The relationship between built environment (BE) and travel behaviour (TB) has been studied in a number of cases, using several methods - aggregate and disaggregate approaches - and different focuses – trip frequency, automobile use, and vehicle miles travelled and so on. Definitely, travel is generated by the need to undertake activities and obtain services, and there is a general consensus that urban components affect TB. However researches are still needed to better understand which components of the travel behaviour are affected most and by which of the urban components. In order to fill the gap in the research, the present dissertation faced two main objectives: (1) To contribute to the better understanding of the relationship between travel demand and urban environment. And (2) To develop an econometric model for estimating travel demand with urban environment attributes. With this purpose, the present thesis faced an exhaustive research and computation of land-use variables in order to find the best representation of BE for modelling trip frequency. In particular two empirical analyses are carried out: 1. Estimation of three dimensions of travel demand using dimensions of urban environment. We compare different travel dimensions and geographical scales, and we measure self-selection contribution following the joint models. 2. Develop a hybrid model, integrated latent variable and discrete choice model. The implementation of hybrid models is new in the analysis of land-use and travel behaviour. BE and TB explicitly interact and allow richness information about a specific individual decision process For all empirical analysis is used a data-base from a survey conducted in 2006 and 2007 in Madrid. Spatial attributes describing neighbourhood environment are derived from different data sources: National Institute of Statistics-INE (Administrative: municipality and district) and GIS (circular units). INE provides raw data for such spatial units as: municipality and district. The construction of census units is trivial as the census bureau provides tables that readily define districts and municipalities. The construction of circular units requires us to determine the radius and associate the spatial information to our households. The first empirical part analyzes trip frequency by applying an ordered logit model. In this part is studied the effect of socio-economic, transport and land use characteristics on two travel dimensions: trip frequency and type of tour. In particular the land use is defined in terms of type of neighbourhoods and types of dwellers. Three neighbourhood representations are explored, and described three for constructing neighbourhood attributes. In particular administrative units are examined to represent neighbourhood and circular – unit representation. Ordered logit models are applied, while ordinal logit models are well-known, an intensive work for constructing a spatial attributes was carried out. On the other hand, the second empirical analysis consists of the development of an innovative econometric model that considers a latent variable called “propensity to travel”, and choice model is the choice of type of tour. The first two specifications of ordinal models help to estimate this latent variable. The latent variable is unobserved but the manifestation is called “indicators”, then the probability of choosing an alternative of tour is conditional to the probability of latent variable and type of tour. Since latent variable is unknown we fit the integral over its distribution. Four “sets of best variables” are specified, following the specification obtained from the correlation analysis. The results evidence that the relative importance of SE variables versus BE variables depends on how BE variables are measured. We found that each of these three spatial scales has its intangible qualities and drawbacks. Spatial scales play an important role on predicting travel demand due to the variability in measures at trip origin/destinations within the same administrative unit (municipality, district and so on). Larger units will produce less variation in data; but it does not affect certain variables, such as public transport supply, that are more significant at municipality level. By contrast, land-use measures are more efficient at district level. Self-selection in this context, is weak. Thus, the influence of BE attributes is true. The results of the hybrid model show that unobserved factors affect the choice of tour complexity. The latent variable used in this model is propensity to travel that is explained by socioeconomic aspects and neighbourhood attributes. The results show that neighbourhood attributes have indeed a significant impact on the choice of the type of tours either directly and through the propensity to travel. The propensity to travel has a different impact depending on the structure of each tour and increases the probability of choosing more complex tours, such as tours with many intermediate stops. The integration of choice and latent variable model shows that omitting important perception and attitudes leads to inconsistent estimates. The results also indicate that goodness of fit improves by adding the latent variable in both sequential and simultaneous estimation. There are significant differences in the sensitivity to the latent variable across alternatives. In general, as expected, the hybrid models show a major improvement into the goodness of fit of the model, compared to a classical discrete choice model that does not incorporate latent effects. The integrated model leads to a more detailed analysis of the behavioural process. Summarizing, the effect that built environment characteristics on trip frequency studied is deeply analyzed. In particular we tried to better understand how land use characteristics can be defined and measured and which of these measures do have really an impact on trip frequency. We also tried to test the superiority of HCM on this field. We can concluded that HCM shows a major improvement into the goodness of fit of the model, compared to classical discrete choice model that does not incorporate latent effects. And consequently, the application of HCM shows the importance of LV on the decision of tour complexity. People are more elastic to built environment attributes than level of services. Thus, policy implications must take place to develop more mixed areas, work-places in combination with commercial retails.

La dimensión de la subjetividad en la formación del valor inmobiliario : aplicación del método de análisis de ecuaciones estructurales al mercado residencial de Lisboa

The discretionality and the appraisers’ subjectivity that characterize traditional real estate valuation are still allowed to take part in the formation of the asset price even when respecting international standards (EVS, IVS) or Appraisal Institution´s regulations (TEGOVA, RICS, etc.). The application of econometric and statistical methods to real estate valuation aims at the elimination of subjectivity on the appraisal process. But the unanswered question underneath this subject is the following: How important is the subjective component on real estate appraisal value formation? On this study Structural Equation Models (SEM) are used to determine the importance of the objective and subjective components on real estate valuation value formation as well as the weight of economic factors and the current economic context on real estate appraisal for mortgage purposes price formation. There were used two latent variables, Objective Component and Subjective Component, witch aggregate objective observed variables and subjective observed and unobserved variables, respectively. Factorial Exploratory Analysis is the statistical technique used in order to link the observed variables extracted from the valuation appraisal reports to the latent constructs derived from the theoretical model. SEM models were used to refine the model, eliminate non‐significant variables and to determine the weight of Objective and Subjective latent variables. These techniques were applied to a sample of over 11.000 real estate assets appraisal reports throughout the time period between November of 2006 and April of 2012. The real assets used on this study are located on Lisbon’s Metropolitan Area – “Grande Lisboa” –, Portugal. From this study, we conclude that Subjective Component has a considerable weight on real estate appraisal value formation and that the external factor Economic Situation has a very small impact on real estate appraisal value formation.

Hybrid choice model for propensity to travel and tour complexity

During the last years cities around the world have invested important quantities of money in measures for reducing congestion and car-trips. Investments which are nothing but potential solutions for the well-known urban sprawl phenomenon, also called the “development trap” that leads to further congestion and a higher proportion of our time spent in slow moving cars. Over the path of this searching for solutions, the complex relationship between urban environment and travel behaviour has been studied in a number of cases. The main question on discussion is, how to encourage multi-stop tours? Thus, the objective of this paper is to verify whether unobserved factors influence tour complexity. For this purpose, we use a data-base from a survey conducted in 2006-2007 in Madrid, a suitable case study for analyzing urban sprawl due to new urban developments and substantial changes in mobility patterns in the last years. A total of 943 individuals were interviewed from 3 selected neighbourhoods (CBD, urban and suburban). We study the effect of unobserved factors on trip frequency. This paper present the estimation of an hybrid model where the latent variable is called propensity to travel and the discrete choice model is composed by 5 alternatives of tour type. The results show that characteristics of the neighbourhoods in Madrid are important to explain trip frequency. The influence of land use variables on trip generation is clear and in particular the presence of commercial retails. Through estimation of elasticities and forecasting we determine to what extent land-use policy measures modify travel demand. Comparing aggregate elasticities with percentage variations, it can be seen that percentage variations could lead to inconsistent results. The result shows that hybrid models better explain travel behavior than traditional discrete choice models.

Maximum likelihood estimation of new state space models for operational modal analysis

The modal analysis of a structural system consists on computing its vibrational modes. The experimental way to estimate these modes requires to excite the system with a measured or known input and then to measure the system output at different points using sensors. Finally, system inputs and outputs are used to compute the modes of vibration. When the system refers to large structures like buildings or bridges, the tests have to be performed in situ, so it is not possible to measure system inputs such as wind, traffic, . . .Even if a known input is applied, the procedure is usually difficult and expensive, and there are still uncontrolled disturbances acting at the time of the test. These facts led to the idea of computing the modes of vibration using only the measured vibrations and regardless of the inputs that originated them, whether they are ambient vibrations (wind, earthquakes, . . . ) or operational loads (traffic, human loading, . . . ). This procedure is usually called Operational Modal Analysis (OMA), and in general consists on to fit a mathematical model to the measured data assuming the unobserved excitations are realizations of a stationary stochastic process (usually white noise processes). Then, the modes of vibration are computed from the estimated model. The first issue investigated in this thesis is the performance of the Expectation- Maximization (EM) algorithm for the maximum likelihood estimation of the state space model in the field of OMA. The algorithm is described in detail and it is analysed how to apply it to vibration data. After that, it is compared to another well known method, the Stochastic Subspace Identification algorithm. The maximum likelihood estimate enjoys some optimal properties from a statistical point of view what makes it very attractive in practice, but the most remarkable property of the EM algorithm is that it can be used to address a wide range of situations in OMA. In this work, three additional state space models are proposed and estimated using the EM algorithm: • The first model is proposed to estimate the modes of vibration when several tests are performed in the same structural system. Instead of analyse record by record and then compute averages, the EM algorithm is extended for the joint estimation of the proposed state space model using all the available data. • The second state space model is used to estimate the modes of vibration when the number of available sensors is lower than the number of points to be tested. In these cases it is usual to perform several tests changing the position of the sensors from one test to the following (multiple setups of sensors). Here, the proposed state space model and the EM algorithm are used to estimate the modal parameters taking into account the data of all setups. • And last, a state space model is proposed to estimate the modes of vibration in the presence of unmeasured inputs that cannot be modelled as white noise processes. In these cases, the frequency components of the inputs cannot be separated from the eigenfrequencies of the system, and spurious modes are obtained in the identification process. The idea is to measure the response of the structure corresponding to different inputs; then, it is assumed that the parameters common to all the data correspond to the structure (modes of vibration), and the parameters found in a specific test correspond to the input in that test. The problem is solved using the proposed state space model and the EM algorithm. Resumen El análisis modal de un sistema estructural consiste en calcular sus modos de vibración. Para estimar estos modos experimentalmente es preciso excitar el sistema con entradas conocidas y registrar las salidas del sistema en diferentes puntos por medio de sensores. Finalmente, los modos de vibración se calculan utilizando las entradas y salidas registradas. Cuando el sistema es una gran estructura como un puente o un edificio, los experimentos tienen que realizarse in situ, por lo que no es posible registrar entradas al sistema tales como viento, tráfico, . . . Incluso si se aplica una entrada conocida, el procedimiento suele ser complicado y caro, y todavía están presentes perturbaciones no controladas que excitan el sistema durante el test. Estos hechos han llevado a la idea de calcular los modos de vibración utilizando sólo las vibraciones registradas en la estructura y sin tener en cuenta las cargas que las originan, ya sean cargas ambientales (viento, terremotos, . . . ) o cargas de explotación (tráfico, cargas humanas, . . . ). Este procedimiento se conoce en la literatura especializada como Análisis Modal Operacional, y en general consiste en ajustar un modelo matemático a los datos registrados adoptando la hipótesis de que las excitaciones no conocidas son realizaciones de un proceso estocástico estacionario (generalmente ruido blanco). Posteriormente, los modos de vibración se calculan a partir del modelo estimado. El primer problema que se ha investigado en esta tesis es la utilización de máxima verosimilitud y el algoritmo EM (Expectation-Maximization) para la estimación del modelo espacio de los estados en el ámbito del Análisis Modal Operacional. El algoritmo se describe en detalle y también se analiza como aplicarlo cuando se dispone de datos de vibraciones de una estructura. A continuación se compara con otro método muy conocido, el método de los Subespacios. Los estimadores máximo verosímiles presentan una serie de propiedades que los hacen óptimos desde un punto de vista estadístico, pero la propiedad más destacable del algoritmo EM es que puede utilizarse para resolver un amplio abanico de situaciones que se presentan en el Análisis Modal Operacional. En este trabajo se proponen y estiman tres modelos en el espacio de los estados: • El primer modelo se utiliza para estimar los modos de vibración cuando se dispone de datos correspondientes a varios experimentos realizados en la misma estructura. En lugar de analizar registro a registro y calcular promedios, se utiliza algoritmo EM para la estimación conjunta del modelo propuesto utilizando todos los datos disponibles. • El segundo modelo en el espacio de los estados propuesto se utiliza para estimar los modos de vibración cuando el número de sensores disponibles es menor que vi Resumen el número de puntos que se quieren analizar en la estructura. En estos casos es usual realizar varios ensayos cambiando la posición de los sensores de un ensayo a otro (múltiples configuraciones de sensores). En este trabajo se utiliza el algoritmo EM para estimar los parámetros modales teniendo en cuenta los datos de todas las configuraciones. • Por último, se propone otro modelo en el espacio de los estados para estimar los modos de vibración en la presencia de entradas al sistema que no pueden modelarse como procesos estocásticos de ruido blanco. En estos casos, las frecuencias de las entradas no se pueden separar de las frecuencias del sistema y se obtienen modos espurios en la fase de identificación. La idea es registrar la respuesta de la estructura correspondiente a diferentes entradas; entonces se adopta la hipótesis de que los parámetros comunes a todos los registros corresponden a la estructura (modos de vibración), y los parámetros encontrados en un registro específico corresponden a la entrada en dicho ensayo. El problema se resuelve utilizando el modelo propuesto y el algoritmo EM.

Residential self-selection and geographical scales in trip frequency.

Neighbourhood representation and scale used to measure the built environment have been treated in many ways. However, it is anything but clear what representation of neighbourhood is the most feasible in the existing literature. This paper presents an exhaustive analysis of built environment attributes through three spatial scales. For this purpose multiple data sources are integrated, and a set of 943 observations is analysed. This paper simultaneously analyses the influence of two methodological issues in the study of the relationship between built environment and travel behaviour: (1) detailed representation of neighbourhood by testing different spatial scales; (2) the influence of unobserved individual sensitivity to built environment attributes. The results show that different spatial scales of built environment attributes produce different results. Hence, it is important to produce local and regional transport measures, according to geographical scale. Additionally, the results show significant sensitivity to built environment attributes depending on place of residence. This effect, called residential sorting, acquires different magnitudes depending on the geographical scale used to measure the built environment attributes. Spatial scales risk to the stability of model results. Hence, transportation modellers and planners must take into account both effects of self-selection and spatial scales.