Dans cette thèse, nous nous intéressons à l'estimation de modèles conditionnellement hétéroscédastiques (CH) sous différentes hypothèses. Dans une première partie, en modifiant l'hypothèse d'identification usuelle du modèle, nous définissions un estimateur de quasi-maximum de vraisemblance (QMV) non gaussien et nous montrons que, sous certaines conditions, cet estimateur est plus efficace que l'estimateur du quasi maximum de vraisemblance gaussien. Nous étudions dans une deuxième partie l'inférence d'un modèle CH dans le cas où le processus des innovations est distribué selon une loi alpha stable. Nous établissons la consistance et la normalité asymptotique de l'estimateur du maximum de vraisemblance. La loi alpha stable n'apparaissant que comme loi limite, nous étudions ensuite le comportement de ce même estimateur dans le cas où la loi du processus des innovations n'est plus une loi alpha stable mais est dans le domaine d'attraction d'une telle loi. Dans la dernière partie, nous étudions l'estimation d'un modèle GARCH lorsque le processus générateur de données est un modèle CH dont les coefficients sont sujets à des changements de régimes markoviens. Nous montrons que cet estimateur, dans un cadre mal spécifié, converge vers une pseudo vraie valeur et nous établissons sa loi asymptotique. Nous étudions cet estimateur lorsque le processus observé est stationnaire mais nous détaillons également ses propriétés asymptotiques lorsque ce processus est non stationnaire et explosif. Par des simulations, nous étudions les capacités prédictives du modèle GARCH mal spécifié. Nous déterminons ainsi la robustesse de ce modèle et de l'estimateur du QMV à une erreur de spécification de la volatilité. / In this thesis, we focus on the inference of conditionally heteroskedastic models under different assumptions. This thesis consists of three parts and an introductory chapter. In the first part, we use an alternate identification assumption of the model and we define a non Gaussian quasi maximum likelihood estimator. We show that, under certain conditions, this estimator is more efficient than the Gaussian quasi maximum likelihood estimator. In a second part, we study the inference of a conditionally heteroskedastic model when the process of the innovations is distributed as an alpha stable law. We establish the consistency and the asymptotic normality of the maximum likelihood estimator. Since the alpha stable laws appear in general as a limit, we then focus of the behavior of this same estimator when the law of the innovation process is not stable but in the domain of attraction of a stable law. In the last part of this thesis, we study the estimation of a GARCH model when the data generating process is a conditionally heteroskedastic model whose coefficients are subject to Markov switching regimes. We show that, in a missspecified framework, this estimator converges toward a pseudo true value and we establish its asymptotic properties when this process is non stationary and explosive. Through simulations, we investigate the predictive ability of the missspecified GARCH model. Thus we determinate the robustness of the model and of the estimator of the quasi maximum likelihood to the missspecification of the volatility

One single primary study is only a little piece of a bigger puzzle. Meta-analysis is the statistical combination of results from primary studies that address a similar question. The most general case is the random-effects model, in where it is assumed that for each study the vector of outcomes T_i~N(θ_i,Σ_i ) and that the vector of true-effects for each study is θ_i~N(θ,Ψ). Since each θ_i is a nuisance parameter, inferences are based on the marginal model T_i~N(θ,Σ_i+Ψ). The main goal of a meta-analysis is to obtain estimates of θ, the sampling error of this estimate and Ψ. Standard meta-analysis techniques assume that Σ_i is known and fixed, allowing the explicit modeling of its elements and the use of Generalized Least Squares as the method of estimation. Furthermore, one can construct the variance-covariance matrix of standard errors and build confidence intervals or ellipses for the vector of pooled estimates. In practice, each Σ_i is estimated from the data using a matrix function that depends on the unknown vector θ_i. Some alternative methods have been proposed in where explicit modeling of the elements of Σ_i is not needed. However, estimation of between-studies variability Ψ depends on the within-study variance Σ_i, as well as other factors, thus not modeling explicitly the elements of Σ_i and departure of a hierarchical structure has implications on the estimation of Ψ. In this dissertation, I develop an alternative model for random-effects meta-analysis based on the theory of hierarchical models. Motivated, primarily, by Hoaglin's article "We know less than we should about methods of meta-analysis", I take into consideration that each Σ_i is unknown and estimated by using a matrix function of the corresponding unknown vector θ_i. I propose an estimation method based on the Minimum Covariance Estimator and derive formulas for the expected marginal variance for two effect sizes, namely, Pearson's moment correlation and standardized mean difference. I show through simulation studies that the proposed model and estimation method give accurate results for both univariate and bivariate meta-analyses of these effect-sizes, and compare this new approach to the standard meta-analysis method.

In this thesis, we consider a robust state estimation problem for discrete-time, homogeneous, first-order, finite-state finite-alphabet hidden Markov models (HMMs). Based on Kolmogorov's Theorem on the existence of a process, we first present the Kolmogorov model for the HMMs under consideration. A new change of measure is introduced. The statistical properties of the Kolmogorov representation of an HMM are discussed on the canonical probability space. A special Kolmogorov measure is constructed. Meanwhile, the ergodicity of two expanded Markov chains is investigated. In order to describe the uncertainty of HMMs, we study probability distance problems based on the Kolmogorov model of HMMs. Using a change of measure technique, the relative entropy and the relative entropy rate as probability distances between HMMs, are given in terms of the HMM parameters. Also, we obtain a new expression for a probability distance considered in the existing literature such that we can use an information state method to calculate it. Furthermore, we introduce regular conditional relative entropy as an a posteriori probability distance to measure the discrepancy between HMMs when a realized observation sequence is given. A representation of the regular conditional relative entropy is derived based on the Radon-Nikodym derivative. Then a recursion for the regular conditional relative entropy is obtained using an information state method. Meanwhile, the well-known duality relationship between free energy and relative entropy is extended to the case of regular conditional relative entropy given a sub-[special character]-algebra. Finally, regular conditional relative entropy constraints are defined based on the study of the probability distance problem. Using a Lagrange multiplier technique and the duality relationship for regular conditional relative entropy, a finite horizon robust state estimator for HMMs with regular conditional relative entropy constraints is derived. A complete characterization of the solution to the robust state estimation problem is also presented.

In this thesis, three control methodologies are proposed for suppressing multi-mode vibration in flexible structures. Controllers developed using these methods are designed to (i) be able to cope with large and sudden changes in the system's parameters, (ii) be robust to unmodelled dynamics, and (iii) have a fast transient response. In addition, the controllers are designed to employ a minimum number of sensor-actuator pairs, and yet pose a minimum computational demand so as to allow real-time implementation. A cantilever beam with magnetically clamped loads is designed and constructed as the research vehicle for evaluation of the proposed controllers. Using this set-up, sudden and large dynamic variations of the beam loading can be tested, and the corresponding changes in the plant's parameters can be observed. Modal testing reveals that the first three modes of the plant are the most significant and need to be suppressed. It is also identified that the first and third modes are spaced more than a decade apart in frequency. The latter characteristic increases the difficulty of effectively controlling all three modes simultaneously using one controller. To overcome this problem, the resonant control method is chosen as the basis for the control methodologies discussed in this thesis. The key advantage of resonant control is that it can be tuned to provide specific attenuation only at and immediately close to the resonant frequency of concern. Consequently, it does not cause control spillover to other modes owing to unmodeled dynamics. Because of these properties, a resonant controller can be configured to form a parallel structure with the objective of targeting and cancelling multiple modes individually. This is possible regardless of the mode spacing. In addition, resonant control requires only a minimum number of collocated sensor-actuator pairs for multi-mode vibration cancellation. All these characteristics make resonant control a suitable candidate for multi-mode vibration cancellation of flexible structures. Since a resonant controller provides negligible attenuation away from the natural frequencies that it has been specifically designed for, it is very sensitive to changes of a system's natural frequencies and becomes ineffective when these mode frequencies change. Hence, for the case of a dynamically loaded structure with consequent variations in mode frequencies, the resonant control method must be modified to allow tracking of system parameter changes. This consideration forms the theme of this thesis, which is to allow adaptive multi-mode vibration control of dynamically-loaded flexible structures. Three controller design methodologies based on the resonant control principle are consequently proposed and evaluated. In the first approach, all possible loading conditions are assumed to be a priori known. Based on this assumption, a multi-model multi-mode resonant control (M4RC) method is proposed. The basis of the M4RC approach is that it comprises a bank of known loading models that are designed such that each model gives optimum attenuation for a particular loading condition. Conceptually, each model is implemented as a set of fixed-parameter controllers, one for each mode of concern. In reality, each mode controller is implemented as an adjustable resonant controller that is loaded with the fixed-model parameters of the corresponding mode. The M4RC method takes advantage of the highly frequency-sensitive nature of resonant control to allow simple and rapid selection of the optimum controller. Identification of the set of resonant frequencies is implemented using a bank of band-pass filters that correspond to the mode frequencies of the known models. At each time interval a supervisor scheme determines for each mode which model has the closest frequency to the observed vibration frequency and switches the corresponding model controller output to attenuate the mode. Selection is handled on a mode-by-mode basis, such that for each mode the closest model is selected. The proposed M4RC is relatively simple and less computationally complex compared to other multi-model methods reported in the literature. In particular, the M4RC uses a simple supervisor scheme and requires only a single controller per mode. Other multi-model methods use more complex supervision schemes and require one controller per model. The M4RC method is evaluated through both simulation and experimental studies. The results reveal that the proposed M4RC is very effective for controlling multi-mode vibration of a flexible structure with known loading conditions, but is ineffective for unmodeled loading conditions. In the second approach, the assumption that all loading conditions are a priori known is relaxed. An adaptive multi-mode resonant control (ARC) method is proposed to control the flexible structure for all possible (including unknown) loading conditions. On-line estimation of the structure's natural frequencies is used to update the adaptive resonant controller's parameters. The estimation of the natural frequencies is achieved using a parallel set of second-order recursive least-squares estimators, each of which is designed for a specific mode of concern. To optimise the estimation accuracy for each mode frequency, a different sampling rate suitable for that mode is used for the corresponding estimator. Simulation and experiment results show that the proposed adaptive method can achieve better performance, as measured by attenuation level, over its fixed-parameter counterpart for a range of unmodeled dynamics. The results also reveal that, for the same sequences of known loading changes, the transient responses of the ARC are slower than those of the M4RC. In the third approach, a hybrid multi-model and adaptive resonant control is utilized to improve the transient response of the ARC. The proposed multi-model multi-mode adaptive resonant control (M4ARC) method is designed as a combination of the M4RC and ARC methods. The basis of the proposed method is to use the M4RC fixed-parameter model scheme to deal with transient conditions while the ARC adaptive parameter estimator is still in a state of fluctuation. Then, once the estimator has reached the vicinity of its steady-state, the adaptive model is switched in place of the fixed model to achieve optimum control of the unforeseen loading condition. Whenever a loading change is experienced, the simple M4RC supervisor scheme is used to identify the closest model and to load the adjustable resonant controllers with the fixed parameters for that model. Meanwhile, the mode estimators developed for the ARC method are used to identify the exact plant parameters for the modes of concern. As soon as these parameters stop rapidly evolving and reach their steady-state, they are loaded into the respective adjustable controllers. The same process is repeated whenever a loading change occurs. Given the simplicity of the M4ARC method and its minimal computation demand, it is easily applicable for real-time implementation. Simulation and experiment results show that the proposed M4ARC outperforms both the ARC with respect to transient performance, and the M4RC with respect to unmodeled loading conditions. The outcomes of this thesis provide a basis for further development of the theory and application of active control for flexible structures with unforeseen configuration variations. Moreover, the basis for the proposed multi-model adaptive control can be used in other areas of control (not limited to vibration cancellation) where fast dynamic reconfiguration of the controller is necessary to accommodate structural changes and fluctuating external disturbances.

當我們在應用迴歸分析法時，往往會遇到兩個或多個自變數間存在著線性 關係的問題，即所謂多重共線性(multicollinearity)； 多重共線性的存 在會使得一般被廣泛運用的最小平方估計式 (least square estimator) 出現不穩定的情形。此估計式之總變異(total variance)會因共線性之程 度愈高而發散，呈現出不穩定的現象，進而影響其預測模式的能力。因此 相繼有學者提出改良共線性模式的方法，以期達到較精確且穩健的預測結 果。脊迴歸分析法(Ridge regression analysis) 便是其中之一；對於有 共線性存在之模式，若使用傳統脊估式，其總變異會較最小平方估計式穩 定。但傳統脊估式為一個偏量估計式(biased estimator)，故本文考慮採 用Jackknife 取一法以求降低脊迴歸估計式之偏量(bias)，此二法併用所 產生之一個新的估計式即本文所謂改良式脊迴歸估計式。本文將應用線性 模式Jackknife 估計式，配合脊迴歸分析法導出改良式脊迴歸估計式。並 另外利用電腦模擬出不同程度之共線性資料以比較分析傳統脊迴歸係數與 改良式脊迴歸係數，此二者於預測模式上之表現。結果顯示：改良式脊迴 歸估計係數對於降低估計偏差方面有顯著之改善，其預測能力亦優於傳統 脊迴歸係數，因此改良式脊迴歸估計式較傳統脊迴歸估計式更加穩定、精 確。迴歸分析是目前應用最廣泛之統計工具，不論是經濟模型、商業方面 以及醫學上之應用等均以求精求準之預測為主要目的，本文提出之改良式 脊迴歸係數，於共線性存在之迴歸模式下兼備了傳統脊迴歸係數穩定估計 式變異以求精，降低估計偏量以求準之優點，因此改良式脊迴歸係數於預 測模式上之貢獻是值得肯定的。

Net pay (NP) and net-to-gross ratio (NGR) are often crucial quantities to characterize a reservoir and assess the amount of hydrocarbons in place. Numerous methods in the industry have been developed to evaluate NP and NGR, depending on the intended purposes. These methods usually involve the use of cut-off values of one or more surrogate variables to discriminate non-reservoir from reservoir rocks. This study investigates statistical issues related to the selection of such cut-off values by considering the specific case of using porosity () as the surrogate. Four methods are applied to permeability-porosity datasets to estimate porosity cut-off values. All the methods assume that a permeability cut-off value has been previously determined and each method is based on minimizing the prediction error when particular assumptions are satisfied. The results show that delineating NP and evaluating NGR require different porosity cut-off values. In the case where porosity and the logarithm of permeability are joint normally distributed, NP delineation requires the use of the Y-on-X regression line to estimate the optimal porosity cut-off while the reduced major axis (RMA) line provides the optimal porosity cut-off value to evaluate NGR. Alternatives to RMA and regression lines are also investigated, such as discriminant analysis and a data-oriented method using a probabilistic analysis of the porosity-permeability crossplots. Joint normal datasets are generated to test the ability of the methods to predict accurately the optimal porosity cut-off value for sampled sub datasets. These different methods have been compared to one another on the basis of the bias, standard error and robustness of the estimates. A set of field data has been used from the Travis Peak formation to test the performance of the methods. The conclusions of the study have been confirmed when applied to field data: as long as the initial assumptions concerning the distribution of data are verified, it is recommended to use the Y-on-X regression line to delineate NP while either the RMA line or discriminant analysis should be used for evaluating NGR. In the case where the assumptions on data distribution are not verified, the quadrant method should be used.

In diesem Beitrag wird eine Regressionsanalyse vorgestellt, die die Einflüsse auf die Entscheidung verheirateter deutscher Frauen untersucht, eine Erwerbstätigkeit aufzunehmen. Um Differenzen im Verhalten von ost- und westdeutschen Frauen zu ermitteln, erfolgte die Untersuchung getrennt in zwei Datensätzen. Zur Vermeidung von Annahmen über die Art des Zusammenhanges wurde das Generalisierte Additive Modell (GAM) gewählt, ein semiparametrisches Regressionsmodell. Diese Modellform, die nichtparametrische und parametrische Regressionsmethoden in sich vereint, hat bisher wenig Verbreitung in der Praxis gefunden. Dies lag vor allem am Schätz verfahren, dem Backfitting. Seit etwa einem Jahr gibt es neue Ansätze, in dieser Modellform zu schätzen. Die analytischen Eigenschaften des neuen Schätzers lassen sich leichter bestimmen. Mit dieser Schätzung konnten Unterschiede zwischen Ost und West genau herausgearbeitet werden und die funktionalen Zusammenhänge zwischen Einflußvariablen und Antwortvariable untersucht werden. Die Analyse brachte deutliche Unterschiede im Erwerbsverhalten zwischen der Frauen beider Landesteile zum Vorschein. / This paper will focus on the regression analysis of labor supply decisions of married German women. In order to determine differences East and West German women were compared seperately. To avoid assumptions about the functional type of correlation the Generalized Additive Model, a semiparametric regression model, was chosen. So far, this pattern consisting of nonparametric and parametric methods has not found acceptance in practical application. Reason for that is the backfitting-estimator. One year ago new ideas for the estimation by GAM were found. The analytical features of the new estimator are easier to determine. Using this method differences between East and West were discovered in detail and functional correlations between endogenous and exogenous variables were investigated. This analysis unveiled significant differences of labor supply behavior among East and West Germany.

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This study analyzes the relationship between financial development and income inequality by using panel data of 60 developing and developed countries for the period 2000-2010. We find evidence for the linear negative relationship between financial development and income inequality which asserts that financial development reduces income inequality. We also find evidence supporting Kuznets inverted u-shaped hypothesis on development-income inequality link, except that for the developed countries where we find evidence for u-shaped hypothesis. It is also concluded that the panel is stationary without unit root, indicating that shocks on income inequality is not persistent.

Cooperative communications have received much attention recently, due to its ability to attain cooperation diversity. But when two nodes communicate via relays, it is difficult to get the perfect channel information, so relays must estimate their forward channel and backward channel in order to amplify the data to the destination. We investigate the effect of channel estimation error, and design the LMMSE estimator to estimate the channels, and also we consider the multi-relays to assist the whole system for training and data transmission. We propose the SNR gap ratio, outage probability, and the BER simulations for the analysis. Simulation shows that when using multi-relays, it can mitigate the effect of channel estimation errors in all of the amplify-and-forward (AF) scenarios.