An analysis of the relationship between wages in the public and private sector in colombia : a panel data approach

This document examines the time-series properties of the wage differentials that arise between the public and private sector in Colombia during the sample period 1984 to 2005. We Find conflicting results in unit-root and stationary tests when looking at wage differentials at an aggregate level (such as for men, women or both). However, when we analyse wage differentials at higher levels of disaggregation, treat them jointly as a panel of data, and allow for the presence of potential cross section dependence, there is more supportive evidence for the view that wage differentials are stationary. This implies that although wage differentials do exist, they have not been consistently increasing (or decreasing) over time.

Extending Zelterman’s approach for robust estimation of population size to zero-truncated clustered data

Estimation of population size with missing zero-class is an important problem that is encountered in epidemiological assessment studies. Fitting a Poisson model to the observed data by the method of maximum likelihood and estimation of the population size based on this fit is an approach that has been widely used for this purpose. In practice, however, the Poisson assumption is seldom satisfied. Zelterman (1988) has proposed a robust estimator for unclustered data that works well in a wide class of distributions applicable for count data. In the work presented here, we extend this estimator to clustered data. The estimator requires fitting a zero-truncated homogeneous Poisson model by maximum likelihood and thereby using a Horvitz-Thompson estimator of population size. This was found to work well, when the data follow the hypothesized homogeneous Poisson model. However, when the true distribution deviates from the hypothesized model, the population size was found to be underestimated. In the search of a more robust estimator, we focused on three models that use all clusters with exactly one case, those clusters with exactly two cases and those with exactly three cases to estimate the probability of the zero-class and thereby use data collected on all the clusters in the Horvitz-Thompson estimator of population size. Loss in efficiency associated with gain in robustness was examined based on a simulation study. As a trade-off between gain in robustness and loss in efficiency, the model that uses data collected on clusters with at most three cases to estimate the probability of the zero-class was found to be preferred in general. In applications, we recommend obtaining estimates from all three models and making a choice considering the estimates from the three models, robustness and the loss in efficiency. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Efficient and cost-effective experimental determination of kinetic constants and data: the success of a Bayesian systematic approach to drug transport, receptor binding, continuous culture and cell transport kinetics

Details about the parameters of kinetic systems are crucial for progress in both medical and industrial research, including drug development, clinical diagnosis and biotechnology applications. Such details must be collected by a series of kinetic experiments and investigations. The correct design of the experiment is essential to collecting data suitable for analysis, modelling and deriving the correct information. We have developed a systematic and iterative Bayesian method and sets of rules for the design of enzyme kinetic experiments. Our method selects the optimum design to collect data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. The rules select features of the design such as the substrate range and the number of measurements. We show here that this method can be directly applied to the study of other important kinetic systems, including drug transport, receptor binding, microbial culture and cell transport kinetics. It is possible to reduce the errors in the estimated parameters and, most importantly, increase the efficiency and cost-effectiveness by reducing the necessary amount of experiments and data points measured. (C) 2003 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

A novel approach to processing high-performance polymers that exploits entropically driven ring-opening polymerization

Blends of the poly(ether sulfone) derived from 4,4'-biphenol and 4,4'-dichlorodiphenylsulfone (Radel-R(TM)) with its homologous macrocyclic oligomers show greatly lowered melt viscosities relative to that of the parent polymer, potentially enabling more facile production and fabrication of fiber-reinforced composite materials. The macrocycles can then undergo entropically driven ring-opening polymerization in situ. The required blends can be obtained easily in one step, by carrying out polycondensations at concentrations lower than those usually used for polymer synthesis.

A knowledge-driven management approach to environmental-conscious construction

This paper aims to introduce a knowledge-based managemental prototype entitled Eþ for environmental-conscious construction relied on an integration of current environmental management tools in construction area. The overall objective of developing the Eþ prototype is to facilitate selectively reusing the retrievable knowledge in construction engineering and management areas assembled from previous projects for the best practice in environmental-conscious construction. The methodologies adopted in previous and ongoing research related to the development of the Eþ belong to the operations research area and the information technology area, including literature review, questionnaire survey and interview, statistical analysis, system analysis and development, experimental research and simulation, and so on. The content presented in this paper includes an advanced Eþ prototype, a comprehensive review of environmental management tools integrated to the Eþ prototype, and an experimental case study of the implementation of the Eþ prototype. It is expected that the adoption and implementation of the Eþ prototype can effectively facilitate contractors to improve their environmental performance in the lifecycle of projectbased construction and to reduce adverse environmental impacts due to the deployment of various engineering and management processes at each construction stage.

A differential thermal expansion approach to crystal structure determination from powder diffraction data

Differential thermal expansion over the range 90-210 K has been applied successfully to determine the crystal structure of chlorothiazide from synchrotron powder diffraction data using direct methods. Key to the success of the approach is the use of a multi-data-set Pawley refinement to extract a set of reflection intensities that is more 'single-crystal-like' than those extracted from a single data set. The improvement in reflection intensity estimates is quantified by comparison with reference single-crystal intensities. (C) 2008 International Union of Crystallography Printed in Singapore - all rights reserved

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing and complementary fashion. The collateral content and context-based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix of the visual keywords. A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Sustainable building certification and the rent premium: a panel data approach

This paper investigates whether obtaining sustainable building certification entails a rental premium for commercial office buildings and tracks its development over time. To this aim, both a difference-in-differences and a fixed-effects model approach are applied to a large panel dataset of office buildings in the United States in the 2000–2010 period. The results indicate a significant rental premium for both ENERGY STAR and LEED certified buildings. Controlling for confounding factors, this premium is shown to have increased steadily from 2006 to 2008, followed by a moderate decline in the subsequent periods. The results also show a significant positive relationship between ENERGY STAR labeling and building occupancy rates.