Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

An Evaluation of Respiratory Exacerbation in New York City Given Current Trends in Climate Variability and Predictive Sociodemographic Factors

Background: Within the last century, the massive increase in emissions due to economic growth has made climate variability a major problem for many of the industrialized countries and an emerging problem for the rest of the world. With an increasing number of extreme weather events, such as heat waves, cold fronts, floods, heavy rain, and storms, individuals with Chronic Obstructive Pulmonary Disease (COPD) and asthma remain a vulnerable population, placing them at highest risk of respiratory exacerbation. This dissertation aims to contribute to our understanding of how climate variability, which can influence patterns of rainfall, temperature and other variables on a wide range of timescales, and sociodemographic factors impact respiratory disease exacerbation among New York City (NYC) residents. Methods: Outcome variables of asthma and COPD exacerbation occurring in the inpatient and outpatient medical settings are from the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS). Secondary outcome variables of hospital length of stay (LOS) and total charges for the third study are also from SPARCS. Additional datasets include products for climate (temperature, precipitation, wind, relative humidity, PM 2.5) and sociodemographic potential predictor covariates (sex, age, race, income, poverty, educational attainment, and type of house heating fuel). The first study uses a space-time permutation model to determine spatial and temporal clusters of increased risk of respiratory exacerbation episodes across NYC neighborhoods. The second study uses Poisson regression using a series of three models (general linear model (GLM), general linear mixed model (GLMM) and a Bayesian spatio-temporal model) to further quantify the complexity of factors such as overall climate and socioeconomic status (SES). The third study applies Bayesian spatio-temporal modelling to predict the expected cases into the year 2039, total hospital charges and length of stay (inpatient setting only) based on Intergovernmental Panel on Climate Change (IPCC) predictive climatic factors and other predictive socio-demographic and SES factors. Results: The first study determined temporal and spatial variation of respiratory exacerbation episodes across NYC neighborhoods throughout the study period, with disproportionate increases in ZCTAs demonstrating geographic and hypothesized socioeconomic disparity. Results from the second study indicate the associations between respiratory exacerbation and predictive climate and sociodemographic factors vary according to location and are both positive and negative (increase and decrease risk). The third study found respiratory exacerbation, hospital length of stay and total hospital charges had a combination of positive (increase risk, increase LOS and increase total hospital costs), negative (decrease risk, LOS, cost) and zero associations into the year 2039 with increased temperature and precipitation projections by the IPCC well as predictive sociodemographic and SES factors. Conclusion: The studies in this dissertation provide additional evidence for the relationships between climate variability, sociodemographic factors and respiratory exacerbation in New York City. Spatio-temporal methods identify both time periods and spatial locations of increased risk and the role of predictive factors in certain neighborhoods. For example, the first and second studies determined neighborhoods in the south Bronx, upper Manhattan, eastern Brooklyn (as well as parts of north-western Brooklyn), north-central Queens (near LaGuardia airport) and southern Queens (near John F. Kennedy airport) all presented with elevated risk. These neighborhoods are in proximity to major highways and/or airports and are known to have the highest rates of populations that are either in poverty or black regardless of ethnicity. Race and poverty were consistent risk factors throughout the second and third studies, whereas increased temperatures were protective suggesting cooler temperatures may potentially increase risk instead. The third study determined long-term implications of IPCC projected climatic trends and predictive factors: factors determining SES (black, poverty status) would be predictive of respiratory exacerbation into 2039, and so would age for COPD outcomes; hospital LOS and total charges would be impacted by SES, age and IPCC projected precipitation increase, but for COPD outcomes only. The findings from this research support on-going respiratory management goals, which aim to improve disease management through underlying clinical components including proper medication reconciliation, follow-up on medication adherence, education on diagnosis/prognosis and access to resources to better track environmental conditions (e.g., weather, pollutant levels). The results also support adaptation measures, which are aimed at factors that are difficult to change in themselves, such as living in disadvantaged neighborhoods that are more vulnerable to the impacts of climate variability. The findings can further translate into pragmatic and practical measures to alleviate the variation observed in risk factors of respiratory exacerbation, including the promotion of increased annual primary care physician visits, increased access to specialty care within the outpatient setting, population health informatics tools to easily connect patients to medical resources, and coalitions with local environmental justice groups to help carry out these measures. Limitations, including spatial, temporal, and methodological issues with the data sources are discussed, as are suggestions for future research

Diagnostic checking and intra-daily effects in time series models

A variety of topics on the statistical analysis of time series are addressed in this thesis. The main emphasis is on the state space methodology and, in particular, on structural time series (STS) models. There are now many applications of STS models in the literature and they have proved to be very successful. The keywords of this thesis vary from - Kalman filter, smoothing and diagnostic checking - to - time-varying cubic splines and intra-daily effects -. Five separate studies are carried out for this research project and they are reflected in the chapters 2 to 6. All studies concern time series models which are placed in the state space form (SSF) so that the Kalman filter (KF) can be applied for estimation. The SSF and the KF play a central role in time series analysis that can be compared with the important role of the regression model and the method of least squares estimation in econometrics. Chapter 2 gives an overview of the latest developments in the state space methodology including diffuse likelihood evaluation, stable calculations, etc. Smoothing algorithms evaluate the full sample estimates of unobserved components in time series models. New smoothing algorithms are developed for the state and the disturbance vector of the SSF which are computationally efficient and outperform existing methods. Chapter 3 discusses the existing and the new smoothing algorithms with an emphasis on theory, algorithms and practical implications. The new smoothing results pave the way to use auxiliary residuals, that is full sample estimates of the disturbances, for diagnostic checking of unobserved components time series models. Chapter 4 develops test statistics for auxiliary residuals and it presents applications showing how they can be used to detect and distinguish between outliers and structural change. A cubic spline is a polynomial function of order three which is regularly used for interpolation and curve-fitting. It has also been applied to piecewise regressions, density approximations, etc. Chapter 5 develops the cubic spline further by allowing it to vary over time and by introducing it into time series models. These timevarying cubic splines are an efficient way of handling slowly changing periodic movements in time series. This method for modelling a changing periodic pattern is applied in a structural time series model used to forecast hourly electricity load demand, with the periodic movements being intradaily or intra-weekly. The full model contains other components, including a temperature response which is also modelled using cubic splines. A statistical computer package (SHELF) is developed to produce, at any time, hourly load forecasts three days ahead

Multivariate assessment of linear and non-linear causal coupling pathways within the central-autonomic-network in patients suffering from schizophrenia

Im Bereich der Zeitreihenanalyse richtet sich das Interesse zunehmend darauf, wie Einblicke in die Interaktions- und Regulationsprozesse von pathophysiologischen- und physiologischen Zuständen erlangt werden können. Neuste Fortschritte in der nichtlinearen Dynamik, der Informationstheorie und der Netzwerktheorie liefern dabei fundiertes Wissen über Kopplungswege innerhalb (patho)physiologischer (Sub)Systeme. Kopplungsanalysen zielen darauf ab, ein besseres Verständnis dafür zu erlangen, wie die verschiedenen integrierten regulatorischen (Sub)Systeme mit ihren komplexen Strukturen und Regulationsmechanismen das globale Verhalten und die unterschiedlichen physiologischen Funktionen auf der Ebene des Organismus beschreiben. Insbesondere die Erfassung und Quantifizierung der Kopplungsstärke und -richtung sind wesentliche Aspekte für ein detaillierteres Verständnis physiologischer Regulationsprozesse. Ziel dieser Arbeit war die Charakterisierung kurzfristiger unmittelbarer zentral-autonomer Kopplungspfade (top-to-bottom und bottom to top) durch die Kopplungsanalysen der Herzfrequenz, des systolischen Blutdrucks, der Atmung und zentraler Aktivität (EEG) bei schizophrenen Patienten und Gesunden. Dafür wurden in dieser Arbeit neue multivariate kausale und nicht-kausale, lineare und nicht-lineare Kopplungsanalyseverfahren (HRJSD, mHRJSD, NSTPDC) entwickelt, die in der Lage sind, die Kopplungsstärke und -richtung, sowie deterministische regulatorische Kopplungsmuster innerhalb des zentralen-autonomen Netzwerks zu quantifizieren und zu klassifizieren. Diese Kopplungsanalyseverfahren haben ihre eigenen Besonderheiten, die sie einzigartig machen, auch im Vergleich zu etablierten Kopplungsverfahren. Sie erweitern das Spektrum neuartiger Kopplungsansätze für die Biosignalanalyse und tragen auf ihre Weise zur Gewinnung detaillierter Informationen und damit zu einer verbesserten Diagnostik/Therapie bei. Die Hauptergebnisse dieser Arbeit zeigen signifikant schwächere nichtlineare zentral-kardiovaskuläre und zentral-kardiorespiratorische Kopplungswege und einen signifikant stärkeren linearen zentralen Informationsfluss in Richtung des Herzkreislaufsystems auf, sowie einen signifikant stärkeren linearen respiratorischen Informationsfluss in Richtung des zentralen Nervensystems in der Schizophrenie im Vergleich zu Gesunden. Die detaillierten Erkenntnisse darüber, wie die verschiedenen zentral-autonomen Netzwerke mit paranoider Schizophrenie assoziiert sind, können zu einem besseren Verständnis darüber führen, wie zentrale Aktivierung und autonome Reaktionen und/oder Aktivierung in physiologischen Netzwerken unter pathophysiologischen Bedingungen zusammenhängen.In the field of time series analysis, increasing interest focuses on insights gained how the coupling pathways of regulatory mechanisms work in healthy and ill states. Recent advances in non-linear dynamics, information theory and network theory lead to a new sophisticated body of knowledge about coupling pathways within (patho)physiological (sub)systems. Coupling analyses aim to provide a better understanding of how the different integrated physiological (sub)systems, with their complex structures and regulatory mechanisms, describe the global behaviour and distinct physiological functions at the organism level. In particular, the detection and quantification of the coupling strength and direction are important aspects for a more detailed understanding of physiological regulatory processes. This thesis aimed to characterize short-term instantaneous central-autonomic-network coupling pathways (top-to-bottom and bottom to top) by analysing the coupling of heart rate, systolic blood pressure, respiration and central activity (EEG) in schizophrenic patients and healthy participants. Therefore, new multivariate causal and non-causal linear and non-linear coupling approaches (HRJSD, mHRJSD, NSTPDC) that are able to determine the coupling strength and direction were developed. Whereby, the HRJSD and mHRJSD approaches allow the quantification and classification of deterministic regulatory coupling patterns within and between the cardiovascular- the cardiorespiratory system and the central-autonomic-network were developed. These coupling approaches have their own unique features, even as compared to well-established coupling approaches. They expand the spectrum of novel coupling approaches for biosignal analysis and thus contribute in their own way to detailed information obtained, and thereby contribute to improved diagnostics/therapy. The main findings of this thesis revealed significantly weaker non-linear central-cardiovascular and central-cardiorespiratory coupling pathways, and significantly stronger linear central information flow in the direction of the cardiac- and vascular system, and a significantly stronger linear respiratory information transfer towards the central nervous system in schizophrenia in comparison to healthy participants. This thesis provides an enhanced understanding of the interrelationship of central and autonomic regulatory mechanisms in schizophrenia. The detailed findings on how variously-pronounced, central-autonomic-network pathways are associated with paranoid schizophrenia may enable a better understanding on how central activation and autonomic responses and/or activation are connected in physiology networks under pathophysiological conditions

Enhanced Query Processing on Complex Spatial and Temporal Data

Innovative technologies in the area of multimedia and mechanical engineering as well as novel methods for data acquisition in different scientific subareas, including geo-science, environmental science, medicine, biology and astronomy, enable a more exact representation of the data, and thus, a more precise data analysis. The resulting quantitative and qualitative growth of specifically spatial and temporal data leads to new challenges for the management and processing of complex structured objects and requires the employment of efficient and effective methods for data analysis. Spatial data denote the description of objects in space by a well-defined extension, a specific location and by their relationships to the other objects. Classical representatives of complex structured spatial objects are three-dimensional CAD data from the sector "mechanical engineering" and two-dimensional bounded regions from the area "geography". For industrial applications, efficient collision and intersection queries are of great importance. Temporal data denote data describing time dependent processes, as for instance the duration of specific events or the description of time varying attributes of objects. Time series belong to one of the most popular and complex type of temporal data and are the most important form of description for time varying processes. An elementary type of query in time series databases is the similarity query which serves as basic query for data mining applications. The main target of this thesis is to develop an effective and efficient algorithm supporting collision queries on spatial data as well as similarity queries on temporal data, in particular, time series. The presented concepts are based on the efficient management of interval sequences which are suitable for spatial and temporal data. The effective analysis of the underlying objects will be efficiently supported by adequate access methods. First, this thesis deals with collision queries on complex spatial objects which can be reduced to intersection queries on interval sequences. We introduce statistical methods for the grouping of subsequences. Involving the concept of multi-step query processing, these methods enable the user to accelerate the query process drastically. Furthermore, in this thesis we will develop a cost model for the multi-step query process of interval sequences in distributed systems. The proposed approach successfully supports a cost based query strategy. Second, we introduce a novel similarity measure for time series. It allows the user to focus specific time series amplitudes for the similarity measurement. The new similarity model defines two time series to be similar iff they show similar temporal behavior w.r.t. being below or above a specific threshold. This type of query is primarily required in natural science applications. The main goal of this new query method is the detection of anomalies and the adaptation to new claims in the area of data mining in time series databases. In addition, a semi-supervised cluster analysis method will be presented which is based on the introduced similarity model for time series. The efficiency and effectiveness of the proposed techniques will be extensively discussed and the advantages against existing methods experimentally proofed by means of datasets derived from real-world applications