The thesis contains four essays covering topics in the field of macroeconomic forecasting.<p><p>The first two chapters consider factor models in the context of real-time forecasting with many indicators. Using a large number of predictors offers an opportunity to exploit a rich information set and is also considered to be a more robust approach in the presence of instabilities. On the other hand, it poses a challenge of how to extract the relevant information in a parsimonious way. Recent research shows that factor models provide an answer to this problem. The fundamental assumption underlying those models is that most of the co-movement of the variables in a given dataset can be summarized by only few latent variables, the factors. This assumption seems to be warranted in the case of macroeconomic and financial data. Important theoretical foundations for large factor models were laid by Forni, Hallin, Lippi and Reichlin (2000) and Stock and Watson (2002). Since then, different versions of factor models have been applied for forecasting, structural analysis or construction of economic activity indicators. Recently, Giannone, Reichlin and Small (2008) have used a factor model to produce projections of the U.S GDP in the presence of a real-time data flow. They propose a framework that can cope with large datasets characterised by staggered and nonsynchronous data releases (sometimes referred to as “ragged edge”). This is relevant as, in practice, important indicators like GDP are released with a substantial delay and, in the meantime, more timely variables can be used to assess the current state of the economy.<p><p>The first chapter of the thesis entitled “A look into the factor model black box: publication lags and the role of hard and soft data in forecasting GDP” is based on joint work with Gerhard Rünstler and applies the framework of Giannone, Reichlin and Small (2008) to the case of euro area. In particular, we are interested in the role of “soft” and “hard” data in the GDP forecast and how it is related to their timeliness.<p>The soft data include surveys and financial indicators and reflect market expectations. They are usually promptly available. In contrast, the hard indicators on real activity measure directly certain components of GDP (e.g. industrial production) and are published with a significant delay. We propose several measures in order to assess the role of individual or groups of series in the forecast while taking into account their respective publication lags. We find that surveys and financial data contain important information beyond the monthly real activity measures for the GDP forecasts, once their timeliness is properly accounted for.<p><p>The second chapter entitled “Maximum likelihood estimation of large factor model on datasets with arbitrary pattern of missing data” is based on joint work with Michele Modugno. It proposes a methodology for the estimation of factor models on large cross-sections with a general pattern of missing data. In contrast to Giannone, Reichlin and Small (2008), we can handle datasets that are not only characterised by a “ragged edge”, but can include e.g. mixed frequency or short history indicators. The latter is particularly relevant for the euro area or other young economies, for which many series have been compiled only since recently. We adopt the maximum likelihood approach which, apart from the flexibility with regard to the pattern of missing data, is also more efficient and allows imposing restrictions on the parameters. Applied for small factor models by e.g. Geweke (1977), Sargent and Sims (1977) or Watson and Engle (1983), it has been shown by Doz, Giannone and Reichlin (2006) to be consistent, robust and computationally feasible also in the case of large cross-sections. To circumvent the computational complexity of a direct likelihood maximisation in the case of large cross-section, Doz, Giannone and Reichlin (2006) propose to use the iterative Expectation-Maximisation (EM) algorithm (used for the small model by Watson and Engle, 1983). Our contribution is to modify the EM steps to the case of missing data and to show how to augment the model, in order to account for the serial correlation of the idiosyncratic component. In addition, we derive the link between the unexpected part of a data release and the forecast revision and illustrate how this can be used to understand the sources of the<p>latter in the case of simultaneous releases. We use this methodology for short-term forecasting and backdating of the euro area GDP on the basis of a large panel of monthly and quarterly data. In particular, we are able to examine the effect of quarterly variables and short history monthly series like the Purchasing Managers' surveys on the forecast.<p><p>The third chapter is entitled “Large Bayesian VARs” and is based on joint work with Domenico Giannone and Lucrezia Reichlin. It proposes an alternative approach to factor models for dealing with the curse of dimensionality, namely Bayesian shrinkage. We study Vector Autoregressions (VARs) which have the advantage over factor models in that they allow structural analysis in a natural way. We consider systems including more than 100 variables. This is the first application in the literature to estimate a VAR of this size. Apart from the forecast considerations, as argued above, the size of the information set can be also relevant for the structural analysis, see e.g. Bernanke, Boivin and Eliasz (2005), Giannone and Reichlin (2006) or Christiano, Eichenbaum and Evans (1999) for a discussion. In addition, many problems may require the study of the dynamics of many variables: many countries, sectors or regions. While we use standard priors as proposed by Litterman (1986), an<p>important novelty of the work is that we set the overall tightness of the prior in relation to the model size. In this we follow the recommendation by De Mol, Giannone and Reichlin (2008) who study the case of Bayesian regressions. They show that with increasing size of the model one should shrink more to avoid overfitting, but when data are collinear one is still able to extract the relevant sample information. We apply this principle in the case of VARs. We compare the large model with smaller systems in terms of forecasting performance and structural analysis of the effect of monetary policy shock. The results show that a standard Bayesian VAR model is an appropriate tool for large panels of data once the degree of shrinkage is set in relation to the model size. <p><p>The fourth chapter entitled “Forecasting euro area inflation with wavelets: extracting information from real activity and money at different scales” proposes a framework for exploiting relationships between variables at different frequency bands in the context of forecasting. This work is motivated by the on-going debate whether money provides a reliable signal for the future price developments. The empirical evidence on the leading role of money for inflation in an out-of-sample forecast framework is not very strong, see e.g. Lenza (2006) or Fisher, Lenza, Pill and Reichlin (2008). At the same time, e.g. Gerlach (2003) or Assenmacher-Wesche and Gerlach (2007, 2008) argue that money and output could affect prices at different frequencies, however their analysis is performed in-sample. In this Chapter, it is investigated empirically which frequency bands and for which variables are the most relevant for the out-of-sample forecast of inflation when the information from prices, money and real activity is considered. To extract different frequency components from a series a wavelet transform is applied. It provides a simple and intuitive framework for band-pass filtering and allows a decomposition of series into different frequency bands. Its application in the multivariate out-of-sample forecast is novel in the literature. The results indicate that, indeed, different scales of money, prices and GDP can be relevant for the inflation forecast.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

The aim of this thesis is to provide a comprehensive overview of the main approaches to modeling data loaded with redundant zeros. There are three main subclasses of zero modified models (ZMM) described here - zero inflated models (the main focus lies on models of this subclass), zero truncated models and hurdle models. Models of each subclass are defined and then a construction of maximum likelihood estimates of regression coefficients is described. ZMM models are mostly based on Poisson or negative binomial type 2 distribution (NB2). In this work, author has extended the theory to ZIM models generally based on any discrete distributions of exponential type. There is described a construction of MLE of regression coefficients of theese models, too. Just few of present works are interested in ZIM models based on negative binomial type 1 distribution (NB1). This distribution is not of exponential type therefore a common method of MLE construction in ZIM models cannot be used here. In this work provides modification of this method using quasi-likelihood method. There are two simulation studies concluding the work. 1

Ce travail de thèse se penche sur le problème de la localisation de sources acoustiques à partir de signaux déterministes et aléatoires mesurés par un réseau de microphones. Le problème est résolu dans un cadre statistique, par estimation via la méthode du maximum de vraisemblance. La pression mesurée par un microphone est interprétée comme étant un mélange de signaux latents émis par les sources. Les positions et les amplitudes des sources acoustiques sont estimées en utilisant l’algorithme espérance-maximisation (EM). Dans cette thèse, deux types d’incertitude sont également pris en compte : les positions des microphones et le nombre d’onde sont supposés mal connus. Ces incertitudes sont transposées aux données dans le cadre théorique des fonctions de croyance. Ensuite, les positions et les amplitudes des sources acoustiques peuvent être estimées en utilisant l’algorithme E2M, qui est une variante de l’algorithme EM pour les données incertaines.La première partie des travaux considère le modèle de signal déterministe sans prise en compte de l’incertitude. L’algorithme EM est utilisé pour estimer les positions et les amplitudes des sources. En outre, les résultats expérimentaux sont présentés et comparés avec le beamforming et la holographie optimisée statistiquement en champ proche (SONAH), ce qui démontre l’avantage de l’algorithme EM. La deuxième partie considère le problème de l’incertitude du modèle et montre comment les incertitudes sur les positions des microphones et le nombre d’onde peuvent être quantifiées sur les données. Dans ce cas, la fonction de vraisemblance est étendue aux données incertaines. Ensuite, l’algorithme E2M est utilisé pour estimer les sources acoustiques. Finalement, les expériences réalisées sur les données réelles et simulées montrent que les algorithmes EM et E2M donnent des résultats similaires lorsque les données sont certaines, mais que ce dernier est plus robuste en présence d’incertitudes sur les paramètres du modèle. La troisième partie des travaux présente le cas de signaux aléatoires, dont l’amplitude est considérée comme une variable aléatoire gaussienne. Dans le modèle sans incertitude, l’algorithme EM est utilisé pour estimer les sources acoustiques. Dans le modèle incertain, les incertitudes sur les positions des microphones et le nombre d’onde sont transposées aux données comme dans la deuxième partie. Enfin, les positions et les variances des amplitudes aléatoires des sources acoustiques sont estimées en utilisant l’algorithme E2M. Les résultats montrent ici encore l’avantage d’utiliser un modèle statistique pour estimer les sources en présence, et l’intérêt de prendre en compte l’incertitude sur les paramètres du modèle. / This work addresses the problem of multiple sound source localization for both deterministic and random signals measured by an array of microphones. The problem is solved in a statistical framework via maximum likelihood. The pressure measured by a microphone is interpreted as a mixture of latent signals emitted by the sources; then, both the sound source locations and strengths can be estimated using an expectation-maximization (EM) algorithm. In this thesis, two kinds of uncertainties are also considered: on the microphone locations and on the wave number. These uncertainties are transposed to the data in the belief functions framework. Then, the source locations and strengths can be estimated using a variant of the EM algorithm, known as Evidential EM (E2M) algorithm. The first part of this work begins with the deterministic signal model without consideration of uncertainty. The EM algorithm is then used to estimate the source locations and strengths : the update equations for the model parameters are provided. Furthermore, experimental results are presented and compared with the beamforming and the statistically optimized near-field holography (SONAH), which demonstrates the advantage of the EM algorithm. The second part raises the issue of model uncertainty and shows how the uncertainties on microphone locations and wave number can be taken into account at the data level. In this case, the notion of the likelihood is extended to the uncertain data. Then, the E2M algorithm is used to solve the sound source estimation problem. In both the simulation and real experiment, the E2M algorithm proves to be more robust in the presence of model and data uncertainty. The third part of this work considers the case of random signals, in which the amplitude is modeled by a Gaussian random variable. Both the certain and uncertain cases are investigated. In the former case, the EM algorithm is employed to estimate the sound sources. In the latter case, microphone location and wave number uncertainties are quantified similarly to the second part of the thesis. Finally, the source locations and the variance of the random amplitudes are estimated using the E2M algorithm.

Le changement structurel en agriculture suscite beaucoup d’intérêt de la part des économistes agricoles ainsi que des décideurs politiques. Pour prendre en compte l’hétérogénéité du comportement des agriculteurs, une approche par les modèles de mélange de chaînes de Markov est appliquée pour la première fois en économie agricole pour analyser ce processus. La performance de cette approche est d’abord testée en utilisant une forme simplifiée du modèle, puis sa forme générale est appliquée pour étudier l’impact de certaines mesures de politique agricole. Pour identifier les principaux canaux d’interdépendance entre exploitations voisines dans les processus du changement structurel, une approche de mélange non-Markovienne a été appliquée pour modéliser la survie et l’agrandissement des exploitations agricolesTrois principales conclusions découlent de cette thèse. Tout d’abord, la prise en compte de l’hétérogénéité dans les processus de transition des exploitations agricoles permet de mieux représenter le changement structurel et conduit à des prédictions plus précises de la distribution des exploitations, comparé aux modèles généralement utilisés jusqu’ici. Deuxièmement, l’impact des principaux facteurs du changement structurel dépend lui aussi des types non-observables d’exploitations mis en évidence. Enfin, le cadre du modèle de mélange permet également de révéler différents types de relations inobservées entre exploitations voisines qui contribuent au changement structurel observé à un niveau global ou régional. / Structural change in farming has long been the subject of considerable interest among agricultural economists and policy makers. To account for heterogeneity in farmers’ behaviours, a mixture Markov modelling framework is applied to analyse this process for the first time in agricultural economics. The performance of this approach is first investigated using a restrictive form of the model, and its general form is then applied to study the impact of some drivers of structural change, including agricultural policy measures. To identify channels through which interdependency between neighbouring farms arises in this process, the mixture modelling approach is applied to analyse both farm survival and farm growth. The main conclusions of this thesis are threefoldFirstly, accounting for the generally unobserved heterogeneity in the transition process of farms allows better representing structural change in farming and leads to more accurate predictions of farm-size distributions than the models usually used so far. Secondly, the impacts of the main drivers of structural change themselves depend on the specific unobservable farm types which are revealed by the model. Lastly, the mixture modelling approach enables identifying different unobserved relationships between neighbouring farms that contributes to the structural change observed at an aggregate or regional level.

No description available.

Speech is by far the most natural form of communication between human beings. It is intuitive, expressive and contains information at several cognitive levels. We as humans, are perceptive to several of these cognitive levels of information, as we can gather the information pertaining to the identity of the speaker, the speaker's gender, emotion, location, the language, and so on, in addition to the content of what is being spoken. This makes speech based human machine interaction (HMI), both desirable and challenging for the same set of reasons. For HMI to be natural for humans, it is imperative that a machine understands information present in speech, at least at the level of speaker identity, language, location in space, and the summary of what is being spoken. Although one can draw parallels between the human-human interaction and HMI, the two differ in their purpose. We, as humans, interact with a machine, mostly in the context of getting a task done more efficiently, than is possible without the machine. Thus, typically in HMI, controlling the machine in a specific manner is the primary goal. In this context, it can be argued that, HMI, with a limited vocabulary containing specific commands, would suffice for a more efficient use of the machine. In this thesis, we address the problem of ``Who spoke what and where", in the context of a machine understanding the information pertaining to identities of the speakers, their locations in space and the keywords they spoke, thus considering three levels of information - speaker identity (who), location (where) and keywords (what). This can be addressed with the help of multiple sensors like microphones, video camera, proximity sensors, motion detectors, etc., and combining all these modalities. However, we explore the use of only microphones to address this issue. In practical scenarios, often there are times, wherein, multiple people are talking at the same time. Thus, the goal of this thesis is to detect all the speakers, their keywords, and their locations in mixture signals containing speech from simultaneous speakers. Addressing this problem of ``Who spoke what and where" using only microphone signals, forms a part of acoustic scene analysis (ASA) of speech based acoustic events. We divide the problem of ``who spoke what and where" into two sub-problems: ``Who spoke what?" and ``Who spoke where". Each of these problems is cast in a generic latent variable (LV) framework to capture information in speech at different levels. We associate a LV to represent each of these levels and model the relationship between the levels using conditional dependency. The sub-problem of ``who spoke what" is addressed using single channel microphone signal, by modeling the mixture signal in terms of LV mass functions of speaker identity, the conditional mass function of the keyword spoken given the speaker identity, and a speaker-specific-keyword model. The LV mass functions are estimated in a Maximum likelihood (ML) framework using the Expectation Maximization (EM) algorithm using Student's-t Mixture Model (tMM) as speaker-specific-keyword models. Motivated by HMI in a home environment, we have created our own database. In mixture signals, containing two speakers uttering the keywords simultaneously, the proposed framework achieves an accuracy of 82 % for detecting both the speakers and their respective keywords. The other sub-problem of ``who spoke where?" is addressed in two stages. In the first stage, the enclosure is discretized into sectors. The speakers and the sectors in which they are located are detected in an approach similar to the one employed for ``who spoke what" using signals collected from a Uniform Circular Array (UCA). However, in place of speaker-specific-keyword models, we use tMM based speaker models trained on clean speech, along with a simple Delay and Sum Beamformer (DSB). In the second stage, the speakers are localized within the active sectors using a novel region constrained localization technique based on time difference of arrival (TDOA). Since the problem being addressed is a multi-label classification task, we use the average Hamming score (accuracy) as the performance metric. Although the proposed approach yields an accuracy of 100 % in an anechoic setting for detecting both the speakers and their corresponding sectors in two-speaker mixture signals, the performance degrades to an accuracy of 67 % in a reverberant setting, with a $60$ dB reverberation time (RT60) of 300 ms. To improve the performance under reverberation, prior knowledge of the location of multiple sources is derived using a novel technique derived from geometrical insights into TDOA estimation. With this prior knowledge, the accuracy of the proposed approach improves to 91 %. It is worthwhile to note that, the accuracies are computed for mixture signals containing more than 90 % overlap of competing speakers. The proposed LV framework offers a convenient methodology to represent information at broad levels. In this thesis, we have shown its use with three different levels. This can be extended to several such levels to be applicable for a generic analysis of the acoustic scene consisting of broad levels of events. It will turn out that not all levels are dependent on each other and hence the LV dependencies can be minimized by independence assumption, which will lead to solving several smaller sub-problems, as we have shown above. The LV framework is also attractive to incorporate prior knowledge about the acoustic setting, which is combined with the evidence from the data to derive the information about the presence of an acoustic event. The performance of the framework, is dependent on the choice of stochastic models, which model the likelihood function of the data given the presence of acoustic events. However, it provides an access to compare and contrast the use of different stochastic models for representing the likelihood function.

Ce travail de thèse poursuit une perspective double dans l'usage conjoint des méthodes de Monte Carlo séquentielles (MMS) et de l'algorithme Espérance-Maximisation (EM) dans le cadre des modèles de Markov cachés présentant une structure de dépendance markovienne d'ordre supérieur à 1 au niveau de la composante inobservée. Tout d'abord, nous commençons par un exposé succinct de l'assise théorique des deux concepts statistiques à Travers les chapitres 1 et 2 qui leurs sont consacrés. Dans un second temps, nous nous intéressons à la mise en pratique simultanée des deux concepts au chapitre 3 et ce dans le cadre usuel ou la structure de dépendance est d'ordre 1, l'apport des méthodes MMS dans ce travail réside dans leur capacité à approximer efficacement des fonctionnelles conditionnelles bornées, notamment des quantités de filtrage et de lissage dans un cadre non linéaire et non gaussien. Quant à l'algorithme EM, il est motivé par la présence à la fois de variables observables, et inobservables (ou partiellement observées) dans les modèles de Markov Cachés et singulièrement les modèles de volatilité stochastique étudié. Après avoir présenté aussi bien l'algorithme EM que les méthodes MCS ainsi que quelques une de leurs propriétés dans les chapitres 1 et 2 respectivement, nous illustrons ces deux outils statistiques au travers de la calibration d'un modèle de volatilité stochastique. Cette application est effectuée pour des taux change ainsi que pour quelques indices boursiers au chapitre 3. Nous concluons ce chapitre sur un léger écart du modèle de volatilité stochastique canonique utilisé ainsi que des simulations de Monte Carlo portant sur le modèle résultant. Enfin, nous nous efforçons dans les chapitres 4 et 5 à fournir les assises théoriques et pratiques de l'extension des méthodes Monte Carlo séquentielles notamment le filtrage et le lissage particulaire lorsque la structure markovienne est plus prononcée. En guise d’illustration, nous donnons l'exemple d'un modèle de volatilité stochastique dégénéré dont une approximation présente une telle propriété de dépendance. / This thesis pursues a double perspective in the joint use of sequential Monte Carlo methods (SMC) and the Expectation-Maximization algorithm (EM) under hidden Mar­kov models having a Markov dependence structure of order grater than one in the unobserved component signal. Firstly, we begin with a brief description of the theo­retical basis of both statistical concepts through Chapters 1 and 2 that are devoted. In a second hand, we focus on the simultaneous implementation of both concepts in Chapter 3 in the usual setting where the dependence structure is of order 1. The contribution of SMC methods in this work lies in their ability to effectively approximate any bounded conditional functional in particular, those of filtering and smoothing quantities in a non-linear and non-Gaussian settings. The EM algorithm is itself motivated by the presence of both observable and unobservable ( or partially observed) variables in Hidden Markov Models and particularly the stochastic volatility models in study. Having presented the EM algorithm as well as the SMC methods and some of their properties in Chapters 1 and 2 respectively, we illustrate these two statistical tools through the calibration of a stochastic volatility model. This application is clone for exchange rates and for some stock indexes in Chapter 3. We conclude this chapter on a slight departure from canonical stochastic volatility model as well Monte Carlo simulations on the resulting model. Finally, we strive in Chapters 4 and 5 to provide the theoretical and practical foundation of sequential Monte Carlo methods extension including particle filtering and smoothing when the Markov structure is more pronounced. As an illustration, we give the example of a degenerate stochastic volatility model whose approximation has such a dependence property.

Les batteries électriques sont omniprésentes dans notre vie quotidienne : ordinateur, téléphone, etc. Elles jouent un rôle important dans le défi de la transition énergétique : anticiper la raréfaction des énergies fossiles et réduire la pollution, en développant le stockage des énergies renouvelables et les transports électriques. Cependant, l'estimation de l'état de charge (State of Charge – SoC) d'une batterie est difficile et les modèles de prédiction actuels sont peu robustes. En effet, une batterie est un système électrochimique complexe, dont la dynamique est influencée non seulement par ses caractéristiques internes, mais aussi par les conditions d'usages souvent non contrôlables : température, profil d’utilisation, etc. Or, une estimation précise du SoC permet de garantir une utilisation sûre de la batterie en évitant une surcharge ou surdécharge ; mais aussi d’estimer son autonomie. Dans cette étude, nous utilisons un modèle à espaces d'états gouverné par une chaîne de Markov cachée. Ce modèle est fondé sur des équations physiques et la chaîne de Markov cachée permet d’appréhender les différents «régimes de fonctionnement» de la batterie. Pour garantir l’unicité des paramètres du modèle, nous démontrons son identifiabilité à partir de contraintes simples et naturelles sur ses paramètres «physiques ». L’estimation du SoC dans un véhicule électrique doit être faîte en ligne et avec une puissance de calcul limitée. Nous estimons donc le SoC en utilisant une technique d’échantillonnage préférentiel séquentiel. D’autre part l’estimation des paramètres est faîte à partir d’une base d’apprentissage pour laquelle les états de la chaîne de Markov et le SoC ne sont pas observés. Nous développons et testons trois algorithmes adaptés à notre modèle à structure latente : un échantillonneur particulaire de Gibbs, un algorithme de Monte-Carlo EM pénalisé par des contraintes d’identifiabilité et un algorithme de Monte-Carlo EM pénalisé par une loi a priori. Par ailleurs les états cachés de la chaîne de Markov visent à modéliser les différents régimes du fonctionnement de la batterie. Nous identifions leur nombre par divers critères de sélection de modèles. Enfin, à partir de données issues de trois types de batteries (cellule, module et pack d’un véhicule électrique), notre modèle a permis d’appréhender les différentes sollicitations de la batterie et donne des estimations robustes et précises du SoC. / Electric batteries are omnipresent in our daily lives: computers, smartphones, etc. Batteries are important for anticipating the scarcity of fossil fuels and tackling their environmental impact. Therefore, estimating the State of Charge (SoC) of a battery is nowadays a challenging issue, as existing physical and statistical models are not yet robust. Indeed a battery is a complex electrochemical system. Its dynamic depends not only on its internal characteristics but also on uncontrolled usage conditions: temperature, usage profile, etc. However the SoC estimation helps to prevent overcharge and deep discharge, and to estimate the battery autonomy. In this study, the battery dynamics are described by a set of physical linear equations, switching randomly according to a Markov chain. This model is referred to as switching Markov state space model. To ensure the unicity of the model parameters, we prove its identifiability by applying straightforward and natural constraints on its “physical” parameters. Embedded applications, like electric vehicles, impose online estimated with hardware and time constraints. Therefore we estimate the SoC using a sequential importance sampling technique. Furthermore the model includes two latent variables: the SoC and the Markov chain state. Thus, to estimate the parameters, we develop and test three algorithms adapted to latent structure models: particle Gibbs sampler, Monte Carlo EM penalized with identifiability constraints, and Monte Carlo EM penalized with a prior distribution. The hidden Markov states aim to model the different “regimes” of the battery dynamics. We identify their number using different model selection criteria. Finally, when applied to various data from three battery types (cell, module and pack of an electric vehicle) our model allows us to analyze the battery dynamics and to obtain a robust and accurate SoC estimation under uncontrolled usage conditions.

This master's thesis deals with with FIT-Miner, the system for knowledge discovery in databases. The first part of this paper describes the data-mining process, mixture model's issues and FIT-Miner system. Second part deals with design, implementation and testing of created module, which is used for cluster analysis with Expectation-Maximalization algorithm. The end of the paper is focused to design of modules using Java Store Procedures Technology.

Zapisovač letových údajů je zařízení určené pro zaznamenávání letových dat z různých senzorů v letadlech. Analýza letových údajů hraje důležitou roli ve vývoji a testování avioniky. Testování a hodnocení charakteristik letadla se často provádí pomocí testovacích manévrů. Naměřená data z jednoho letu jsou uložena v jednom letovém záznamu, který může obsahovat několik testovacích manévrů. Cílem této práce je identi kovat základní testovací manévry s pomocí naměřených letových dat. Teoretická část popisuje letové manévry a formát měřených letových dat. Analytická část popisuje výzkum v oblasti klasi kace založené na statistice a teorii pravděpodobnosti potřebnou pro pochopení složitých Gaussovských směšovacích modelů. Práce uvádí implementaci, kde jsou Gaussovy směšovací modely použité pro klasifi kaci testovacích manévrů. Navržené řešení bylo testováno pro data získána z letového simulátoru a ze skutečného letadla. Ukázalo se, že Gaussovy směšovací modely poskytují vhodné řešení pro tento úkol. Další možný vývoj práce je popsán v závěrečné kapitole.