States that owing to foresight and planning by Portuguese business executives, most firms in Portugal survived the difficult 1970s and 1980s and, as a consequence, are stronger in today's competitive internal and external challenges. Sets out the methodology used and gives data analysis and results in a descriptive way, with the use of explanatory tables. Closes by stating that time-based differentiation may offer new ways for firms competing in highly differentiated markets.

The objective of this study is to investigate the approaches utilised by external decision-makers in their evaluation of the different facets of performance of manufacturing organisations, operating as open systems. In process, important linkages which exist among relevant performance parameters are explored. The types and frequencies of performance measures used by the sampled Portuguese financial analysts are compared using factor analysis and multiple regression analysis. Results tend to underscore the significance of the collective performance of all subsystems of the manufacturing system. Based on the findings of this research, implications focusing on the management of organisational performance systems are identified.

Emergency department (ED) overcrowding is a global concern. To help mitigate this issue, this thesis studies impediments to efficient patient flow in the ED caused by suboptimal worker behaviors and patient routing policies. I focus on three issues: (i) admission batching, (ii) hallway placement and (iii) under-triage behavior, and empirically demonstrate their impact on patient flow and quality of care. These studies are summarized as follows. Admissions batching: We study the behavior of admitting patients back-to-back (i.e., batching) by ED physicians. Using data from a large hospital, we show that the probability of batching admissions is increasing in the hour of an ED physician’s shift, and that batched patients experience a longer delay from hospital admission to receiving an inpatient bed. We further show that this effect is partially due to the increase in the coefficient of variation of inpatient bed-requests caused by batching. However, we also find that batching admissions is associated with a higher shift-level productivity. An important implication of our work is that workers may induce delays in downstream stages, caused by practices that increase their productivity. Hallway utilization: A common practice in busy EDs is to admit patients from the waiting area to hallway beds as the regular beds fill up. Using data from a large ED, we first perform a causal analysis to quantify the impact of hallway placement on wait times and quality of care – as defined by disposition time, room-to-departure (R2D) time and likelihood of adverse outcomes. We find that patients admitted to the hallway experience a significantly lower door-to-doctor time at the cost of longer disposition and R2D times. Hallway patients are also substantially more likely to experience an adverse outcome. Next, using a counterfactual analysis we show that a pooling policy, where hallway beds are used only if all regular beds are full, significantly reduces wait times, albeit at the cost of a slightly higher hallway utilization. Also, too little or too much wait tolerance for rooming patients may result in under- or over-utilization of the hallway space, both of which are detrimental to overall ED length of stay (LOS) and wait times. Under-triage behavior: Triaging ED patients upon arrival to the ED and assessing their urgency for treatment is crucial for timely service to all patients. Despite the standard patient classification algorithm by which all nurses are trained, we hypothesize, and show, that the ED’s workload impacts the perceived patient urgency, and subsequently, patient severity scores. We first use a predictive model to predict a patient’s true triage level using information collected at triage and define under-triage, accordingly. We find that under-triage is decreasing up to a certain point of workload but increasing after (U-shape). We also quantify the impact of under-triage on disposition time, room-to-departure time and risk of readmission. Collectively, this thesis demonstrates how patient-flow may be improved without the need to increase explicit physical capacity in the ED (e.g., beds). It offers practical solutions to managers and contributes to the operations management literature.

The motivation for the thesis was to discover whether Finnish Red Cross Youth Shelters can be evaluated and developed from legal and moral standpoints, such as the Human Rights. A special focus is put on how the international law gets interpreted and practiced in a local (Youth Shelter) context. Findings from the collected data are based on the experiences of the Youth Shleter’s employees and volunteers through workshop discussions, surveys and interviews. This observational work done within the thesis is an initial mapping of how “things are at the moment”. Data is analysed with The New Legal Realism (NLR) theory, which main focus is to study law’s context-based interpretations and mobility ie. how international law creates meanings in a local setting. (Dagan & Kreitner, 2018; 534.) The value of the findings will increase through new ideas, support measures and a better identification of the needs of employees and volunteers arising from the findings. The observational work is inspired by the idea that if someone has an obligation to ensure rights, they are also entitled to get support with knowledge of how to implement them properly.

Cette thèse présente trois parties liées à la théorie des sondages. La première partie présente deux résultats originaux de sondages qui ont eu des applications pratiques dans des enquêtes par sondage de l'Insee. Le premier article présente un théorème autorisant un plan de sondage stratifié constituant un compromis entre la dispersion des poids et l'allocation de précision optimale pour une variable d'intérêt spécifique. Les données d’enquête sont souvent utilisées pour estimer nombre de totaux ou modèles issus de variables exclues du design. La précision attendue pour ces variables est donc faible, mais une faible dispersion des poids permet de limiter les risques qu'une estimation dépendant d'une de ces variables ait une très mauvaise précision. Le second article concerne le facteur de repondération dans les estimateurs par calage. On propose un algorithme efficace capable de calculer les facteurs de poids les plus rapprochés autour de 1 tels qu'une solution au problème de calage existe. Cela permet de limiter les risques d'apparition d'unités influentes, particulièrement pour l'estimation sur des domaines. On étudie par simulations sur données réelles les propriétés statistiques des estimateurs obtenus. La seconde partie concerne l'étude des propriétés asymptotique des estimateurs sur données issues de sondage. Celles-ci sont difficiles à étudier en général. On présente une méthode originale qui établit la convergence faible vers un processus gaussien pour le processus empirique d'Horvitz-Thompson indexé par des classes de fonction, pour de nombreux algorithmes de sondage différents utilisés en pratique. Dans la dernière partie, on s'intéresse à des méthodes de sondage pour des données issues de graphes, qui ont des applications pratiques lorsque les graphes sont de taille telles que leur exploitation informatique est coûteuse. On détaille des algorithmes de sondage permettant d'estimer des statistiques d'intérêt pour le réseaux. Deux applications, à des données de Twitter puis à des données simulées, concluent cette partie. / This thesis presents three different parts with ties to survey sampling theory. In the first part, we present two original results that led to practical applications in surveys conducted at Insee (French official statistics Institute). The first chapter deals with allocations in stratified sampling. We present a theorem that proves the existence of an optimal compromise between the dispersion of the sampling weights and the allocation yielding optimal precision for a specific variable of interest. Survey data are commonly used to compute estimates for variables that were not included in the survey design. Expected precision is poor, but a low dispersion of the weights limits risks of very high variance for one or several estimates. The second chapter deals with reweighting factors in calibration estimates. We study an algorithm that computes the minimal bounds so that the calibration estimators exist, and propose an efficient way of resolution. We also study the statistical properties of estimates using these minimal bounds. The second part studies asymptotic properties of sampling estimates. Obtaining asymptotic guarantees is often hard in practice. We present an original method that establishes weak convergence for the Horvitz-Thompson empirical process indexed by a class of functions for a lot of sampling algorithms used in practice. In the third and last part, we focus on sampling methods for populations that can be described as networks. They have many applications when the graphs are so big that storing and computing algorithms on them are very costly. Two applications are presented, one using Twitter data, and the other using simulated data to establish guidelines to design efficient sampling designs for graphs.

With the shift to a system of floating exchange rates among major currencies in 1973, there was a shift of emphasis from the external balance to the exchange rate determination. Attempts have been made to explain the behavior of the exchange rate both theoretically and empirically over the last 20 years. Most models could not explain what happened, as in the 1980s, when the exchange rate moved a lot. Alternative models based on different approaches give different explanations and suggest different policies. This study examines the implications of the models to see what light the empirical results shed on the issues. Results of this study indicate that both monetary and real factors are important in explaining the behavior of the exchange rate, but the results generally support the view of the monetary approach.

Functional data analysis (FDA) has grown into a substantial field of statistical research, with new methodology, numerous useful applications and interesting novel theoretical developments. My dissertation focuses on the empirical properties of functional regression models and their application to financial data. We start from testing the empirical properties of forecasts with the functional autoregressive models based on simulated and real data. We define intraday returns and consider their prediction from such returns on a market index. This is an extension to intraday data of the Capital Asset Pricing model. Finally we investigate multifactor functional models and assess their suitability for the prediction of intraday returns for various financial assets, including stock and commodity futures.

This dissertation examined the theoretical foundations of an individual's labor force participation decision. Further, this dissertation provided empirical analysis of the impact of state tax rates, the duration of unemployment, and household size on male, female, and combined labor force participation rates of the fifty states from 1985 to 1990. Empirical tests showed that: 1) no significant relationship existed between tax variables and participation rates; 2) the duration of unemployment was positively related with participation rates while unemployment was negatively related; 3) service sector growth was positively correlated with longer durations of unemployment; and 4) household size was negatively related with female participation, although no significant relationship existed between household size and male participation rates.

This research presents three case studies of low temperature anomalies that occurred during the winter–spring seasons and their influence on extreme events and crop production. We investigate causes and effects of each climate event and developed prediction methods for crops based on the climate diagnostic information. The first study diagnosed the driven environmental-factors, including climate pattern, climate change, soils moisture, and sea level height, associated with the 2011 great flood in Thailand and resulting total crop loss. The second study investigated climate circulation and indices that contributed to wet-and-cold (WC) events leading to significant crop damage in Taiwan. We developed empirical–dynamical models based on prominent climate indices to confidently predict WC events as much as 6 months before they occur. The final study extends from the second study and predict chronic damage to rice crops from climate change by using a crop simulation model. The long-term prediction of rice growth and yield effectively illustrated both decreases and increases in yield depending on climate scenarios. The three studies are different in location and circumstances but the methodologies can be applied across Thailand, Taiwan, and other areas with similar agroclimatology.

Indiana University-Purdue University Indianapolis (IUPUI) / Adverse drug events (ADEs) are injuries resulting from drug-related medical interventions. ADEs can be either induced by a single drug or a drug-drug interaction (DDI). In order to prevent unnecessary ADEs, many regulatory agencies in public health maintain pharmacovigilance databases for detecting novel drug-ADE associations. However, pharmacovigilance databases usually contain a significant portion of false associations due to their nature structure (i.e. false drug-ADE associations caused by co-medications). Besides pharmacovigilance studies, the risks of ADEs can be minimized by understating their mechanisms, which include abnormal pharmacokinetics/pharmacodynamics due to genetic factors and synergistic effects between drugs. During the past decade, pharmacogenomics studies have successfully identified several predictive markers to reduce ADE risks. While, pharmacogenomics studies are usually limited by the sample size and budget. In this dissertation, we develop statistical methods for pharmacovigilance and pharmacogenomics studies. Firstly, we propose an empirical Bayes mixture model to identify significant drug-ADE associations. The proposed approach can be used for both signal generation and ranking. Following this approach, the portion of false associations from the detected signals can be well controlled. Secondly, we propose a mixture dose response model to investigate the functional relationship between increased dimensionality of drug combinations and the ADE risks. Moreover, this approach can be used to identify high-dimensional drug combinations that are associated with escalated ADE risks at a significantly low local false discovery rates. Finally, we proposed a cost-efficient design for pharmacogenomics studies. In order to pursue a further cost-efficiency, the proposed design involves both DNA pooling and two-stage design approach. Compared to traditional design, the cost under the proposed design will be reduced dramatically with an acceptable compromise on statistical power. The proposed methods are examined by extensive simulation studies. Furthermore, the proposed methods to analyze pharmacovigilance databases are applied to the FDA’s Adverse Reporting System database and a local electronic medical record (EMR) database. For different scenarios of pharmacogenomics study, optimized designs to detect a functioning rare allele are given as well.