Modelos para dados binários são bastante utilizados em várias situações práticas. Transformações em Análise de Regressão podem ser aplicadas para linearizar ou simplificar o modelo e também para corrigir desvios de suposições. Neste trabalho, descrevemos o uso de transformações nos modelos de regressão logística para dados binários e apresentamos modelos envolvendo parâmetros adicionais de modo a obter um ajuste mais adequado. Posteriormente, analisamos o custo da estimação quando são adicionados parâmetros aos modelos e apresentamos os testes de hipóteses relativos aos parâmetros do modelo de regressão logística de Box-Cox. Finalizando, apresentamos alguns métodos de diagnóstico para avaliar a influência das observações nas estimativas dos parâmetros de transformação da covariável, com aplicação a um conjunto de dados reais. / Binary data models have a lot of utilities in many practical situations. In Regrssion Analisys, transformations can be applied to linearize or simplify the model and correct deviations of the suppositions. In this dissertation, we show the use of the transformations in logistic models to binary data models and models involving additional parameters to obtain more appropriate fits. We also present the cost of the estimation when parameters are added to models, hypothesis tests of the parameters in the Box-Cox logistic regression model and finally, diagnostics methods to evaluate the influence of the observations in the estimation of the transformation covariate parameters with their applications to a real data set.

Dans cette thèse nous étudions l'estimation de modèles probabilistes discriminants, surtout des aspects d'apprentissage semi-supervisé et de sélection de caractéristiques. Le but de l'apprentissage semi-supervisé est d'améliorer l'efficacité de l'apprentissage supervisé en utilisant des données non-étiquetées. Cet objectif est difficile à atteindre dans les cas des modèles discriminants. Les modèles probabilistes discriminants permettent de manipuler des représentations linguistiques riches, sous la forme de vecteurs de caractéristiques de très grande taille. Travailler en grande dimension pose des problèmes, en particulier computationnels, qui sont exacerbés dans le cadre de modèles de séquences tels que les champs aléatoires conditionnels (CRF). Notre contribution est double. Nous introduisons une méthode originale et simple pour intégrer des données non étiquetées dans une fonction objectif semi-supervisée. Nous démontrons alors que l'estimateur semi-supervisé correspondant est asymptotiquement optimal. Le cas de la régression logistique est illustré par des résultats d'expèriences. Dans cette étude, nous proposons un algorithme d'estimation pour les CRF qui réalise une sélection de modèle, par le truchement d'une pénalisation $L_1$. Nous présentons également les résultats d'expériences menées sur des tâches de traitement des langues (le chunking et la détection des entités nommées), en analysant les performances en généralisation et les caractéristiques sélectionnées. Nous proposons finalement diverses pistes pour améliorer l'efficacité computationelle de cette technique.

<p>There has been a quick development in the wireless network area during the last decade. Mostly </p><p>these days the focus in the wireless area is on very high speed and long range applications. This </p><p>thesis describes how ZigBee is suitable for wireless sensor networks in logistic telemetry </p><p>applications for global managing and monitoring of goods. ZigBee has been developed by the </p><p>organization named as ‘ZigBee Alliance’ as a new wireless standard for the wireless solutions </p><p>based upon the IEEE 802.15.4 Standard [2]. ZigBee is a new technology as compared to the other </p><p>wireless technologies such as Bluetooth, but it has certain characteristics such as low cost, low </p><p>power, support for mesh networking e.t.c which makes its chances to be more successful than </p><p>others. </p><p> </p><p>The other aim of this thesis is to examine different issues related to ZigBee to see its fitness for </p><p>logistic telemetry applications like multi-hop routing issues, routing strategies and design </p><p>requirements. ZigBee is relatively new wireless technology, so there are great deals of promises </p><p>associated with it. In this thesis, a comparison between ZigBee and Bluetooth technologies will </p><p>also be made.</p>

Every organization has to deal with planning of the appropriate level of human resources over time. The workforce is not always aligned with the requirements of the organization and it increases an organization’s budget. A literature review reveals that there is no model that can systematically predict accurate human resource required within a complex organization. To address this gap, a human resource predictive model was developed based on material requirements planning (MRP). This approach accounts for complexity in workforce planning and generalized it with a logistic regression model. The model estimates the employee turnover number and forecasts the expected remaining headcount for the next time period based on employee information such as; age, working year, salary, etc. Moreover, external variables and economic data can be utilized to adjust the estimated turnover probability. This model also suggests the possible internal workforce movement in case of in-house manpower imbalance.

Surface mining and subsequent reclamation often results in the establishment of large areas of grassland that can benefit wildlife. Grasslands have declined substantially over the last 150 years, resulting in declines of many grassland birds. The dickcissel (Spiza americana), a neotropical migrant, is one such bird whose numbers have declined in the last 30 years due to habitat loss, increased nest predation and parasitism, and over harvest (lethally controlled as an agricultural pest on its wintering range in Central and South America). Reclaimed surface-mined lands have been documented to provide important breeding habitat for dickcissels in the United States, emphasizing the importance of reclamation efforts. Objectives were to understand specific aspects of dickcissel nesting ecology (i.e., nest-site selection, nest success, and nest parasitism, and identification of nest predators) on 2 spatial scales on TXU Energy’s Big Brown Mine, near Fairfield, Texas, and to subsequently provide TXU Energy with recommendations to improve reclaimed areas as breeding habitat for dickcissels. I examined the influence of nest-site vegetation characteristics and the effects of field-level spatial factors on dickcissel nesting ecology on 2 sites reclaimed as wildlife habitat. Additionally, I developed a novel technique to identify predators at active nests during the 2003 field season. During 2002–2003, 119 nests were monitored. On smaller spatial scales, dickcissels were likely to select nest-sites with low vegetation, high densities of bunchgrasses and tall forbs, and areas with higher clover content. Probability of nest success increased with nest heights and vegetation heights above the nest, characteristics associated with woody nesting substrates. Woody nesting substrates were selected and bunchgrasses were avoided. Oak (Quercus spp.) saplings remained an important nesting substrate throughout the breeding season. On a larger scale, nest-site selection was likely to occur farther from wooded riparian areas and closer to recently-reclaimed areas. Nest parasitism was likely to occur near roads and wooded riparian areas. Results suggest reclaimed areas could be improved by planting more bunchgrasses, tall forbs (e.g., curly-cup gumweed [Grindelia squarrosa] and sunflower [Helianthus spp.]), clover (Trifolium spp.), and oaks (a preferred nesting substrate associated with higher survival rates). Larger-scale analysis suggests that larger tracts of wildlife areas should be created with wooded riparian areas comprising a minimal portion of a field’s edge.

This thesis presents a methodology for conducting logistic regression modeling of trip and household information obtained from household travel surveys and vehicle trip information obtained from global positioning systems (GPS) to better understand the trip underreporting that occurs. The methodology presented here builds on previous research by adding additional variables to the logistic regression model that might be significant in contributing to underreporting, specifically, trip purpose. Understanding the trip purpose is crucial in transportation planning because many of the transportation models used today are based on the number of trips in a given area by the purpose of a trip. The methodology used here was applied to two study areas in Texas, Laredo and Tyler-Longview. In these two study areas, household travel survey data and GPS-based vehicle tracking data was collected over a 24-hour period for 254 households and 388 vehicles. From these 254 households, a total of 2,795 trips were made, averaging 11.0 trips per household. By comparing the trips reported in the household travel survey with those recorded by the GPS unit, trips not reported in the household travel survey were identified. Logistic regression was shown to be effective in determining which household- and trip-related variables significantly contributed to the likelihood of a trip being reported. Although different variables were identified as significant in each of the models tested, one variable was found to be significant in all of them - trip purpose. It was also found that the household residence type and the use of household vehicles for commercial purposes did not significantly affect reporting rates in any of the models tested. The results shown here support the need for modeling trips by trip purpose, but also indicate that, from urban area to urban area, there are different factors contributing to the level of underreporting that occurs. An analysis of additional significant variables in each urban area found combinations that yielded trip reporting rates of 0%. Similar to the results of Zmud and Wolf (2003), trip duration and the number of vehicles available were also found to be significant in a full model encompassing both study areas.

The future has always caught the attention of the human being. The thirst of exploring the future and to know the unknown has driven the human being toward innovativeness. Companies are expanding their operations worldwide since the past few decades. Profit growth coupled with an effective strategy has become the primary need of global companies. Research in this area has given rise to optimization of the supply chain for higher profitability. Considering the overall strategy the company needs to plan production well in advance. The operational planning comes in picture at this moment. In order to reduce excessive inventory at each stage of the production; one should know the demand of the next stage and preferably the end customer demand. The process of sales forecasting is undertaken to predict demand at different stages. It is a complex managerial function and hence needed to be undertaken by a scientific way. The sales forecasting the function includes process of forecasting, administration, hardware, software, users and developers of forecast. Historically sales forecasting has been considered as a side activity by most of the companies. Sales forecasting has not been considered as an important function of marketing and finance. Very few companies have seen sales forecasting by a scientific management point of view. Less research has been reported in sales forecasting in comparison to other managerial functions. Planning based on sales forecasting; may be part of a selected strategy for growth and profitability. These facts have attracted us to study sales forecasting as a managerial function. The purpose of this study is to describe and analyze the sales forecasting process, sales forecasting system, sales forecasting methods and techniques. Further proposing possibilities of improvements in existing forecasting process is also purpose of this study. We have selected three manufacturing companies for this study based on purposive sampling. Considering research interest in phenomenon study; we have selected a qualitative research strategy for this study. We have selected a case study method for our research as it is the most appropriate tool to study the relation between theory and phenomenon. For this research, we have collected the data by semistructured interviews based on a pre formed questionnaire. The questionnaire has been prepared with respect to our research purpose and open ended questions were used to gather extensive data. The data gathered during interviews, have been analyzed by the use of ‘Flow model’ suggested by Miles and Huberman (1994). Results from this study shows that there is a need to see ‘sales forecasting’ as a management function rather than a computer activity. To achieve the best information integration throughout the supply chain, increased information visibility is needed. To achieve accuracy in both forecasting and planning; collaborative forecasting may be used. Forecasting software needs to have a suite of methods towards product specific forecasting. The need of customized softwares has also been indicated by this study. The need to measure performance of forecasting by means of accuracy, cost and customer relationship has been concluded.

Record matching is a fundamental and ubiquitous part of today?s society. Anything from typing in a password in order to access your email to connecting existing health records in California with new health records in New York requires matching records together. In general, there are two types of record matching algorithms: deterministic, a more rules-based approach, and probabilistic, a model-based approach. Both types have their advantages and disadvantages. If the amount of data is relatively small, deterministic algorithms yield very high success rates. However, the number of common mistakes, and subsequent rules, becomes astronomically large as the sizes of the datasets increase. This leads to a highly labor-intensive process updating and maintaining the matching algorithm. On the other hand, probabilistic record matching implements a mathematical model that can take into account keying mistakes, does not require as much maintenance and over- head, and provides a probability that two particular entities should be linked. At the same time, as a model, assumptions need to be met, fitness has to be assessed, and predictions can be incorrect. Regardless of the type of algorithm, nearly all utilize a 0/1 field-matching structure, including the Fellegi-Sunter algorithm from 1969. That is to say that either the fields match entirely, or they do not match at all. As a result, typographical errors can get lost and false negatives can result. My research has yielded that using Jaro-Winkler string comparator scores as predictors to a Bayesian logistic regression model in lieu of a restrictive binary structure yields marginal improvement over current methodologies.

This master’s thesis is performed at Department of Management and Engineering Linköping University, for Scania Omni at Scania Production Slupsk (S.P.S). Omni is responsible for development, manufacturing and marketing of city, suburban and intercity buses. After acquisition of the production unit in Slupsk in 2002 lower production cost per bus is possible. But without control over the organisation costs are rising due to late delivery fees and high stock levels. At the outset, the thesis included three clearly defined objectives: - Map the present situation at Scania Production Slupsk regarding material flow from supplier to assembly line including a part and storage analysis. - Benchmark the current routines at Scania Production Slupsk with other successful companies. Furthermore, conduct literature research in order to find theories and philosophies that support problem analysis and thesis solution. - Develop standard routines for material control methods (MCM) and material supply methods (MSM). A complimentary objective is to work as a catalyst during the time of the thesis. The mapping of the present situation showed that MCM and MSM are very tight connected to each other. It was questioned whether this structure was the best way to manage the material flow. After a parts and storage analysis, material was divided into different segments depending of price, consumption and movement. The benchmarking studies showed different ways to manage the material flow. Implementation of unit load, kanban and clear defined interface between departments showed potential to improve the material handling and increase effectiveness. New routines and part segment definitions described in a logistics manual (Appendix I) were made align with a comparison between previous and recommended definitions. The result showed that some parts needs to be controlled differently. Primary recommendations are that logistics manual shall be used when new parts are introduced into the Scala system. Responsible personnel are suppose to give suggestion concerning decision making of MCM and MSM and with help of the logistics manual the work can be more efficient, resulting in a material flow that is flexible and have potential for improvements. Secondary, to avoid material handling to some extent implementation of two-bin system is recommended. Additional recommendations regarding two-bin system is to handle material according to unit load, which enable FIFO, traceability and higher turn over rate

There has been a quick development in the wireless network area during the last decade. Mostly these days the focus in the wireless area is on very high speed and long range applications. This thesis describes how ZigBee is suitable for wireless sensor networks in logistic telemetry applications for global managing and monitoring of goods. ZigBee has been developed by the organization named as ‘ZigBee Alliance’ as a new wireless standard for the wireless solutions based upon the IEEE 802.15.4 Standard [2]. ZigBee is a new technology as compared to the other wireless technologies such as Bluetooth, but it has certain characteristics such as low cost, low power, support for mesh networking e.t.c which makes its chances to be more successful than others. The other aim of this thesis is to examine different issues related to ZigBee to see its fitness for logistic telemetry applications like multi-hop routing issues, routing strategies and design requirements. ZigBee is relatively new wireless technology, so there are great deals of promises associated with it. In this thesis, a comparison between ZigBee and Bluetooth technologies will also be made.