[pt] Esta dissertação tem como objetivo principal a proposição de modelos para previsão, em um curto prazo, do número de processos que são ajuizados em desfavor de uma empresa do setor elétrico. A metodologia utilizada consiste em, a partir de uma análise exploratória dos dados, construir modelos usando uma estratégia bottom-up, ou seja, parte-se de um modelo simples e processa-se seu refinamento até encontrar um modelo apropriado que mais se adeque à realidade. Partiu-se então de um modelo auto projetivo indo até uma formulação de um modelo de regressão dinâmica. Os modelos são então comparados segundo alguns critérios, basicamente no que tange à sua eficiência preditiva. Conclui-se ao final sobre a eficiência de se utilizar modelos de regressão dinâmica para este tipo de previsão tendo em vista a presença de correlação serial dos resíduos, comumente presentes nas séries econômicas. Propõe-se, ao final, uma ferramenta para, a partir dos valores estimados, analisar a viabilidade econômica de estimular ou desestimular as medidas responsáveis pela geração de processos contra a empresa. / [en] The aim of this dissertation is to develop short term models to forecast the number of judicial process in electric sector companies. From the methodology point of view, data is analyzed and models using bottom-up strategy is developed. In other words, a simple model is improved step by step until a proper model that fits well the reality is found. From a univariate model it ends up in a dynamic regression model. The models obtained in this study are compared according to some criterion, mainly forecast accuracy. In the end the conclusion is about the efficiency of dynamic regression models for this kind of forecast, which one presents data with serial correlation of residues, commonly present in economic series. In the end, from the estimated values, it´s proposed a mechanism to analyze the economic viability, to encourage or not, actions which are responsible for instigating judicial processes against the company.

The purpose of this study was to compare the relative performance of various smoothing methods under the common item nonequivalent groups (CINEG) design. In light of the previous literature on smoothing under the CINEG design, this study aimed to provide general guidelines and practical insights on the selection of smoothing procedures under specific testing conditions. To investigate the smoothing procedures, 100 replications were simulated under various testing conditions by using an item response theory (IRT) framework. A total of 192 conditions (3 sample size × 4 group ability difference × 2 common-item proportion × 2 form difficulty difference × 1 test length × 2 common-item type × 2 common-item difficulty spread) were investigated. Two smoothing methods including log-linear presmoothing and cubic spline postsmoothing were considered with four equating methods including frequency estimation (FE), modified frequency estimation (MFE), chained equipercentile equating (CE), and kernel equating (KE). Bias, standard error, and root mean square error were computed to evaluate the performance of the smoothing methods. Results showed that 1) there were always one or more smoothing methods that produced smaller total error than unsmoothed methods; 2) polynomial log-linear presmoothing tended to perform better than cubic spline postsmoothing in terms of systematic and total errors when FE or MFE were used; 3) cubic spline postsmoothing showed a strong tendency to produce the least amount of random error regardless of the equating method used; 4) KE produced more accurate equating relationships under a majority of testing conditions when paired with CE; and 5) log-linear presmoothing produced smaller total error under a majority testing conditions than did cubic spline postsmoothing. Tables are provided to show the best-performing method for all combinations of testing conditions considered.

Dans ce travail de thèse, nous proposons plusieurs algorithmespermettant d'estimer des paramètres évoluant sur des groupes de Lie. Cesalgorithmes s’inscrivent de manière générale dans un cadre bayésien, ce qui nouspermet d'établir une notion d'incertitude sur les paramètres estimés. Pour ce faire,nous utilisons une généralisation de la distribution normale multivariée aux groupesde Lie, appelée distribution normale concentrée sur groupe de Lie.Dans une première partie, nous nous intéressons au problème du filtrage de Kalmanà temps discret et continu-discret où l’état et les d’observations appartiennent à desgroupes de Lie. Cette étude nous conduit à la proposition de deux filtres ; le CD-LGEKFqui permet de résoudre un problème à temps continu-discret et le D-LG-EKF quipermet de résoudre un problème à temps discret.Dans une deuxième partie, nous nous inspirons du lien entre optimisation et filtragede Kalman, qui a conduit au développement du filtrage de Kalman étendu itéré surespace euclidien, en le transposant aux groupes de Lie. Nous montrons ainsicomment obtenir une généralisation du filtre de Kalman étendu itéré aux groupes deLie, appelée LG-IEKF, ainsi qu’une généralisation du lisseur de Rauch-Tung-Striebelaux groupes de Lie, appelée LG-RTS.Finalement, dans une dernière partie, les concepts et algorithmes d’estimation surgroupes de Lie proposés dans la thèse sont utilisés dans le but de fournir dessolutions au problème de la cartographie d'un environnement à partir d'une caméramonoculaire d'une part, et au problème de la localisation d'une caméra monoculairese déplaçant dans un environnement préalablement cartographié d'autre part. / In this thesis, we derive novel parameter estimation algorithmsdedicated to parameters evolving on Lie groups. These algorithms are casted in aBayesian formalism, which allows us to establish a notion of uncertainty for theestimated parameters. To do so, a generalization of the multivariate normaldistribution to Lie groups, called concentrated normal distribution on Lie groups, isemployed.In a first part, we generalize the Continuous-Discrete Extended Kalman Filter (CDEKF),as well as the Discrete Extended Kalman Filter (D-EKF), to the case where thestate and the observations evolve on Lie groups. We obtain two novel algorithmscalled Continuous-Discrete Extended Kalman Filter on Lie Groups (CD-LG-EKF) andDiscrete Extended Kalman Filter on Lie Groups (D-LG-EKF).In a second part, we focus on bridging the gap between the formulation of intrinsicnon linear least squares criteria and Kalman filtering/smoothing on Lie groups. Wepropose a generalization of the Euclidean Iterated Extended Kalman Filter (IEKF) toLie groups, called LG-IEKF. We also derive a generalization of the Rauch-Tung-Striebel smoother (RTS), also known as Extended Kalman Smoother, to Lie groups,called LG-RTS.Finally, the concepts and algorithms presented in the thesis are employed in a seriesof applications. Firstly, we propose a novel simultaneous localization and mappingapproach. Secondly we develop an indoor camera localization framework. For thislatter purpose, we derived a novel Rao-Blackwellized particle smoother on Liegroups, which builds upon the LG-IEKF and the LG-RTS.

<p>Sammanfattning</p><p>Den internationella redovisningen har genomgått stora förändringar under de senaste åren. Allt fler länder i världen använder sig numera av International Financial Reporting Standards (IFRS). Alla noterade företag inom EU måste tillämpa IFRS från och med 2005. Ett av de områden som förändrats under de senaste åren är redovisningsreglerna kring goodwill. Före år 2005 fick företag använda sig av olika metoder vid redovisning av företagsförvärv. Möjligheterna för företag att använda sig av olika redovisningsmetoder skapade enligt vissa forskare incitament att anordna transaktioner endast för att dra fördelar av skillnaderna som uppstod i de olika redovisningsmetoderna.</p><p>Vid redovisning av goodwill ska företagen testa om ett nedskrivningsbehov föreligger. För att testa detta måste företagen använda sig av flera subjektiva uppskattningar och antaganden. Dessa antaganden kan öppna dörrarna för kreativ bokföring, vilket innebär att ledningen missleder intressenter om det ekonomiska läget. Nedskrivningar av tillgångar kan skapa incitament för kreativ bokföring i form av stålbad eller resultatutjämning, eftersom ledningen kan välja att göra nedskrivningar då deras resultat är ovanligt högt, resultatutjämning, eller ovanligt lågt, stålbad. Tidigare forskning har också visat att det kan förekomma skillnader i hur stor utsträckning små och stora företag använder sig av stålbad.</p><p>Vårt syfte var att se om kreativ bokföring i form av stålbad påverkar ett företags beslut om nedskrivning av goodwill. Vårt delsyfte var att studera om en eventuell förekomst av stålbad förekommer i både små och stora svenska företag. Studien avgränsar sig till att studera förekomsten av stålbad i svenska koncerner för år 2008. Uppsatsen baseras på 203 publika koncerner som har haft goodwill som ingående balans under året 2008.</p><p>Vi har gjort regressionstester för att testa om nedskrivningar av goodwill påverkas av stålbad och om det förekommer i små respektive stora företag. Studiens empiri visar att det finns ett statistiskt signifikant samband att stålbad påverkar ett företags beslut om en nedskrivning. Vi fann även statistiska signifikanta samband för att stålbad både förekommer i små och stora företag. Slutsatserna vi fann är därmed att kreativ bokföring i form av stålbad förekommer i företag som skriver ned goodwill och att det förekommer i både små och stora företag.</p>

A frequent problem that appears in practical survival data analysis is censoring. A censored observation occurs when the observation of the event time (duration or survival time) may be prevented by the occurrence of an earlier competing event (censoring time). Censoring may be due to different causes. For example, the loss of some subjects under study, the end of the follow-up period, drop out or the termination of the study and the limitation in the sensitivity of a measurement instrument. The literature about censored data focuses on the i.i.d. case. However in many real applications the data are collected sequentially in time or space and so the assumption of independence in such case does not hold. Here we only give some typical examples from the literature involving correlated data which are subject to censoring. In the clinical trials domain it frequently happens that the patients from the same hospital have correlated survival times due to unmeasured variables like the quality of the hospital equipment. Censored correlated data are also a common problem in the domain of environmental and spatial (geographical or ecological) statistics. In fact, due to the process being used in the data sampling procedure, e.g. the analytical equipment, only the measurements which exceed some thresholds, for example the method detection limits or the instrumental detection limits, can be included in the data analysis. Many other examples can also be found in other fields like econometrics and financial statistics. Observations on duration of unemployment e.g., may be right censored and are typically correlated. When the data are not independent and are subject to censoring, estimation and inference become more challenging mathematical problems with a wide area of applications. In this context, we propose here some new and flexible tools based on a nonparametric approach. More precisely, allowing dependence between individuals, our main contribution to this domain concerns the following aspects. First, we are interested in developing more suitable confidence intervals for a general class of functionals of a survival distribution via the empirical likelihood method. Secondly, we study the problem of conditional mean estimation using the local linear technique. Thirdly, we develop and study a new estimator of the conditional quantile function also based on the local linear method. In this dissertation, for each proposed method, asymptotic results like consistency and asymptotic normality are derived and the finite sample performance is evaluated in a simulation study.

A frequent problem that appears in practical survival data analysis is censoring. A censored observation occurs when the observation of the event time (duration or survival time) may be prevented by the occurrence of an earlier competing event (censoring time). Censoring may be due to different causes. For example, the loss of some subjects under study, the end of the follow-up period, drop out or the termination of the study and the limitation in the sensitivity of a measurement instrument. The literature about censored data focuses on the i.i.d. case. However in many real applications the data are collected sequentially in time or space and so the assumption of independence in such case does not hold. Here we only give some typical examples from the literature involving correlated data which are subject to censoring. In the clinical trials domain it frequently happens that the patients from the same hospital have correlated survival times due to unmeasured variables like the quality of the hospital equipment. Censored correlated data are also a common problem in the domain of environmental and spatial (geographical or ecological) statistics. In fact, due to the process being used in the data sampling procedure, e.g. the analytical equipment, only the measurements which exceed some thresholds, for example the method detection limits or the instrumental detection limits, can be included in the data analysis. Many other examples can also be found in other fields like econometrics and financial statistics. Observations on duration of unemployment e.g., may be right censored and are typically correlated. When the data are not independent and are subject to censoring, estimation and inference become more challenging mathematical problems with a wide area of applications. In this context, we propose here some new and flexible tools based on a nonparametric approach. More precisely, allowing dependence between individuals, our main contribution to this domain concerns the following aspects. First, we are interested in developing more suitable confidence intervals for a general class of functionals of a survival distribution via the empirical likelihood method. Secondly, we study the problem of conditional mean estimation using the local linear technique. Thirdly, we develop and study a new estimator of the conditional quantile function also based on the local linear method. In this dissertation, for each proposed method, asymptotic results like consistency and asymptotic normality are derived and the finite sample performance is evaluated in a simulation study.

Sammanfattning Den internationella redovisningen har genomgått stora förändringar under de senaste åren. Allt fler länder i världen använder sig numera av International Financial Reporting Standards (IFRS). Alla noterade företag inom EU måste tillämpa IFRS från och med 2005. Ett av de områden som förändrats under de senaste åren är redovisningsreglerna kring goodwill. Före år 2005 fick företag använda sig av olika metoder vid redovisning av företagsförvärv. Möjligheterna för företag att använda sig av olika redovisningsmetoder skapade enligt vissa forskare incitament att anordna transaktioner endast för att dra fördelar av skillnaderna som uppstod i de olika redovisningsmetoderna. Vid redovisning av goodwill ska företagen testa om ett nedskrivningsbehov föreligger. För att testa detta måste företagen använda sig av flera subjektiva uppskattningar och antaganden. Dessa antaganden kan öppna dörrarna för kreativ bokföring, vilket innebär att ledningen missleder intressenter om det ekonomiska läget. Nedskrivningar av tillgångar kan skapa incitament för kreativ bokföring i form av stålbad eller resultatutjämning, eftersom ledningen kan välja att göra nedskrivningar då deras resultat är ovanligt högt, resultatutjämning, eller ovanligt lågt, stålbad. Tidigare forskning har också visat att det kan förekomma skillnader i hur stor utsträckning små och stora företag använder sig av stålbad. Vårt syfte var att se om kreativ bokföring i form av stålbad påverkar ett företags beslut om nedskrivning av goodwill. Vårt delsyfte var att studera om en eventuell förekomst av stålbad förekommer i både små och stora svenska företag. Studien avgränsar sig till att studera förekomsten av stålbad i svenska koncerner för år 2008. Uppsatsen baseras på 203 publika koncerner som har haft goodwill som ingående balans under året 2008. Vi har gjort regressionstester för att testa om nedskrivningar av goodwill påverkas av stålbad och om det förekommer i små respektive stora företag. Studiens empiri visar att det finns ett statistiskt signifikant samband att stålbad påverkar ett företags beslut om en nedskrivning. Vi fann även statistiska signifikanta samband för att stålbad både förekommer i små och stora företag. Slutsatserna vi fann är därmed att kreativ bokföring i form av stålbad förekommer i företag som skriver ned goodwill och att det förekommer i både små och stora företag.

Due to the recursive and integrative nature of zero-velocity-update-aided (ZUPT-aided) inertial navigation systems (INSs), the error covariance increases throughout each ZUPT-less period followed by a drastic decrease and large state estimate corrections as soon as ZUPTs are applied. For dead-reckoning with foot-mounted inertial sensors, this gives undesirable discontinuities in the estimated trajectory at the end of each step. However, for many applications, some degree of lag can be tolerated and the information provided by the ZUPTs at the end of a step can be made available throughout the step, eliminating the discontinuities. For this purpose, we propose a smoothing algorithm for ZUPT-aided INSs. For near real-time applications, smoothing is applied to the data in a step-wise manner requiring a suggested varying-lag segmentation rule. For complete off-line processing, full data set smoothing is examined. Finally, the consequences and impact of smoothing are analyzed and quantified based on real-data. / <p>QC 20130114</p>

Analyzing ranking data is an essential component in a wide range of important applications including web-search and recommendation systems. Rankings are difficult to visualize or model due to the computational difficulties associated with the large number of items. On the other hand, partial or incomplete rankings induce more difficulties since approaches that adapt well to typical types of rankings cannot apply generally to all types. While analyzing ranking data has a long history in statistics, construction of an efficient framework to analyze incomplete ranking data (with or without ties) is currently an open problem. This thesis addresses the problem of scalability for visualizing and modeling partial incomplete rankings. In particular, we propose a distance measure for top-k rankings with the following three properties: (1) metric, (2) emphasis on top ranks, and (3) computational efficiency. Given the distance measure, the data can be projected into a low dimensional continuous vector space via multi-dimensional scaling (MDS) for easy visualization. We further propose a non-parametric model for estimating distributions of partial incomplete rankings. For the non-parametric estimator, we use a triangular kernel that is a direct analogue of the Euclidean triangular kernel. The computational difficulties for large n are simplified using combinatorial properties and generating functions associated with symmetric groups. We show that our estimator is computational efficient for rankings of arbitrary incompleteness and tie structure. Moreover, we propose an efficient learning algorithm to construct a preference elicitation system from partial incomplete rankings, which can be used to solve the cold-start problems in ranking recommendations. The proposed approaches are examined in experiments with real search engine and movie recommendation data.

Modern aircraft are getting more and better sensors. As a result of this, the pilots are getting moreinformation than they can handle. To solve this problem one can automate the information processingand instead provide the pilots with conclusions drawn from the sensor information. An aircraft’smovement can be used to determine which class (e.g. commercial aircraft, large military aircraftor fighter) it belongs to. This thesis focuses on comparing three classification schemes; a Bayesianclassification scheme with uniform priors, Transferable Belief Model and a Bayesian classificationscheme with entropic priors.The target is modeled by a jump Markov linear system that switches between different modes (flystraight, turn left, etc.) over time. A marginalized particle filter that spreads its particles over thepossible mode sequences is used for state estimation. Simulations show that the results from Bayesianclassification scheme with uniform priors and the Bayesian classification scheme with entropic priorsare almost identical. The results also show that the Transferable Belief Model is less decisive thanthe Bayesian classification schemes. This effect is argued to come from the least committed principlewithin the Transferable Belief Model. A fixed-lag smoothing algorithm is introduced to the filter andit is shown that the classification results are improved. The advantage of having a filter that remembersthe full mode sequence (such as the marginalized particle filter) and not just determines the currentmode (such as an interacting multiple model filter) is also discussed.