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Competing Neural Networks as Models for Non Stationary Financial Time Series -Changepoint Analysis-
(2005)
The problem of structural changes (variations) play a central role in many scientific fields. One of the most current debates is about climatic changes. Further, politicians, environmentalists, scientists, etc. are involved in this debate and almost everyone is concerned with the consequences of climatic changes. However, in this thesis we will not move into the latter direction, i.e. the study of climatic changes. Instead, we consider models for analyzing changes in the dynamics of observed time series assuming these changes are driven by a non-observable stochastic process. To this end, we consider a first order stationary Markov Chain as hidden process and define the Generalized Mixture of AR-ARCH model(GMAR-ARCH) which is an extension of the classical ARCH model to suit to model with dynamical changes. For this model we provide sufficient conditions that ensure its geometric ergodic property. Further, we define a conditional likelihood given the hidden process and a pseudo conditional likelihood in turn. For the pseudo conditional likelihood we assume that at each time instant the autoregressive and volatility functions can be suitably approximated by given Feedfoward Networks. Under this setting the consistency of the parameter estimates is derived and versions of the well-known Expectation Maximization algorithm and Viterbi Algorithm are designed to solve the problem numerically. Moreover, considering the volatility functions to be constants, we establish the consistency of the autoregressive functions estimates given some parametric classes of functions in general and some classes of single layer Feedfoward Networks in particular. Beside this hidden Markov Driven model, we define as alternative a Weighted Least Squares for estimating the time of change and the autoregressive functions. For the latter formulation, we consider a mixture of independent nonlinear autoregressive processes and assume once more that the autoregressive functions can be approximated by given single layer Feedfoward Networks. We derive the consistency and asymptotic normality of the parameter estimates. Further, we prove the convergence of Backpropagation for this setting under some regularity assumptions. Last but not least, we consider a Mixture of Nonlinear autoregressive processes with only one abrupt unknown changepoint and design a statistical test that can validate such changes.
In conventional radio communication systems, the system design generally starts from the transmitter (Tx), i.e. the signal processing algorithm in the transmitter is a priori selected, and then the signal processing algorithm in the receiver is a posteriori determined to obtain the corresponding data estimate. Therefore, in these conventional communication systems, the transmitter can be considered the master and the receiver can be considered the slave. Consequently, such systems can be termed transmitter (Tx) oriented. In the case of Tx orientation, the a priori selected transmitter algorithm can be chosen with a view to arrive at particularly simple transmitter implementations. This advantage has to be countervailed by a higher implementation complexity of the a posteriori determined receiver algorithm. Opposed to the conventional scheme of Tx orientation, the design of communication systems can alternatively start from the receiver (Rx). Then, the signal processing algorithm in the receiver is a priori determined, and the transmitter algorithm results a posteriori. Such an unconventional approach to system design can be termed receiver (Rx) oriented. In the case of Rx orientation, the receiver algorithm can be a priori selected in such a way that the receiver complexity is minimum, and the a posteriori determined transmitter has to tolerate more implementation complexity. In practical communication systems the implementation complexity corresponds to the weight, volume, cost etc of the equipment. Therefore, the complexity is an important aspect which should be taken into account, when building practical communication systems. In mobile radio communication systems, the complexity of the mobile terminals (MTs) should be as low as possible, whereas more complicated implementations can be tolerated in the base station (BS). Having in mind the above mentioned complexity features of the rationales Tx orientation and Rx orientation, this means that in the uplink (UL), i.e. in the radio link from the MT to the BS, the quasi natural choice would be Tx orientation, which leads to low cost transmitters at the MTs, whereas in the downlink (DL), i.e. in the radio link from the BS to the MTs, the rationale Rx orientation would be the favorite alternative, because this results in simple receivers at the MTs. Mobile radio downlinks with the rationale Rx orientation are considered in the thesis. Modern mobile radio communication systems are cellular systems, in which both the intracell and intercell interferences exist. These interferences are the limiting factors for the performance of mobile radio systems. The intracell interference can be eliminated or at least reduced by joint signal processing with consideration of all the signals in the considered cell. However such joint signal processing is not feasible for the elimination of intercell interference in practical systems. Knowing that the detrimental effect of intercell interference grows with its average energy, the transmit energy radiated from the transmitter should be as low as possible to keep the intercell interference low. Low transmit energy is required also with respect to the growing electro-phobia of the public. The transmit energy reduction for multi-user mobile radio downlinks by the rationale Rx orientation is dealt with in the thesis. Among the questions still open in this research area, two questions of major importance are considered here. MIMO is an important feature with respect to the transmit power reduction of mobile radio systems. Therefore, first questionconcerns the linear Rx oriented transmission schemes combined with MIMO antenna structures. The investigations of the MIMO benefit on the linear Rx oriented transmission schemes are studied in the thesis. Utilization of unconventional multiply connected quantization schemes at the receiver has also great potential to reduce the transmit energy. Therefore, the second question considers the designing of non-linear Rx oriented transmission schemes combined with multiply connected quantization schemes.
The thesis is focused on modelling and simulation of a Joint Transmission and Detection Integrated Network (JOINT), a novel air interface concept for B3G mobile radio systems. Besides the utilization of the OFDM transmission technique, which is a promising candidate for future mobile radio systems, and of the duplexing scheme time division duplexing (TDD), the subdivision of the geographical domain to be supported by mobile radio communications into service areas (SAs) is a highlighted concept of JOINT. A SA consists of neighboring sub-areas, which correspond to the cells of conventional cellular systems. The signals in a SA are jointly processed in a Central Unit (CU) in each SA. The CU performs joint channel estimation (JCE) and joint detection (JD) in the form of the receive-zero-forcing (RxZF) Filter for the uplink (UL) transmission and joint transmission (JT) in the form of the transmit-zero-forcing (TxZF) Filter for the downlink (DL) transmission. By these algorithms intra-SA multiple access interference (MAI) can be eliminated within the limits of the used model so that unbiased data estimates are obtained, and most of the computational effort is moved from mobile terminals (MTs) to the CU so that the MTs can do with low complexity. A simulation chain of JOINT has been established in the software MLDesigner by the author based on time discrete equivalent lowpass modelling. In this simulation chain, all key functionalities of JOINT are implemented. The simulation chain is designed for link level investigations. A number of channel models are implemented both for the single-SA scenario and the multiple-SA scenario so that the system performance of JOINT can be comprehensively studied. It is shown that in JOINT a duality or a symmetry of the MAI elimination in the UL and in the DL exists. Therefore, the typical noise enhancement going along with the MAI elimination by JD and JT, respectively, is the same in both links. In the simulations also the impact of channel estimation errors on the system performance is studied. In the multiple-SA scenario, due to the existence of the inter-SA MAI, which cannot be suppressed by the algorithms of JD and JT, the system performance in terms of the average bit error rate (BER) and the BER statistics degrades. A collection of simulation results show the potential of JOINT with respect to the improvement of the system performance and the enhancement of the spectrum e±ciency as compared to conventional cellular systems.
In the thesis the task of channel estimation in beyond 3G service area based mobile radio air interfaces is considered. A system concept named Joint Transmission and Detection Integrated Network (JOINT) forms the target platform for the investigations. A single service area of JOINT is considered, in which a number of mobile terminals is supported by a number of radio access points, which are connected to a central unit responsible for the signal processing. The modulation scheme of JOINT is OFDM. Pilot-aided channel estimation is considered, which has to be performed only in the uplink of JOINT, because the duplexing scheme TDD is applied. In this way, the complexity of the mobile terminals is reduced, because they do not need a channel estimator. Based on the signals received by the access points, the central unit estimates the channel transfer functions jointly for all mobile terminals. This is done by resorting to the a priori knowledge of the radiated pilot signals and by applying the technique of joint channel estimation, which is developed in the thesis. The quality of the gained estimates is judged by the degradation of their signal-to-noise ratio as compared to the signal-to-noise ratio of the respective estimates gained in the case of a single mobile terminal radiating its pilots. In the case of single-element receive antennas at the access points, said degradation depends solely on the structure of the applied pilots. In the thesis it is shown how by a proper design of the pilots the SNR degradation can be minimized. Besides using appropriate pilots, the performance of joint channel estimation can be further improved by the inclusion of additional a-priori information in the estimation process. An example of such additional information would be the knowledge of the directional properties of the radio channels. This knowledge can be gained if multi-element antennas are applied at the access points. Further, a-priori channel state information in the form of the power delay profiles of the radio channels can be included in the estimation process by the application of the minimum mean square error estimation principle for joint channel estimation. After having intensively studied the problem of joint channel estimation in JOINT, the thesis rounds itself by considering the impact of the unavoidable channel estimation errors on the performance of data estimation in JOINT. For the case of small channel estimation errors occurring due to the presence of noise at the access points, the performance of joint detection in the uplink and of joint transmission in the downlink of JOINT are investigated based on simulations. For the uplink, which utilizes joint detection, it is shown to which degree the bit error probability increases due to channel estimation errors. For the downlink, which utilizes joint transmission, channel estimation errors lead to an increase of the required transmit power, which can be quantified by the simulation results.
In many industrial applications fast and accurate solutions of linear elliptic partial differential equations are needed as one of the building blocks of more complex problems. The domains are often highly complex and meshing turns out to be expensive and difficult to obtain with a sufficient quality. In such cases methods with a regular, not boundary adapted grid offer an attractive alternative. The Explicit Jump Immersed Interface Method is one of these algorithms. The main interest of this work lies in solving the linear elasticity equations. For this purpose the existing EJIIM algorithm has been extended to three dimensions. The Poisson equation is always considered in parallel as the most typical representative of elliptic PDEs. During the work it became clear that EJIIM can have very high computational memory requirements. To overcome this problem an improvement, Reduced EJIIM is proposed. The main theoretical result in this work is the proof of the smoothing property of inverses of elliptic finite difference operators in two and three space dimensions. It is an often observed phenomena that the local truncation error is allowed to be of lower order along some lower dimensional manifold without influencing the global convergence order of the solution.
An autoregressive-ARCH model with possible exogeneous variables is treated. We estimate the conditional volatility of the model by applying feedforward networks to the residuals and prove consistency and asymptotic normality for the estimates under the rate of feedforward networks complexity. Recurrent neural networks estimates of GARCH and value-at-risk is studied. We prove consistency and asymptotic normality for the recurrent neural networks ARMA estimator under the rate of recurrent networks complexity. We also overcome the estimation problem in stochastic variance models in discrete time by feedforward networks and the introduction of a new distributions on the innovations. We use the method to calculate market risk such as expected shortfall and Value-at risk. We tested this distribution together with other new distributions on the GARCH family models against other common distributions on the financial market such as Normal Inverse Gaussian, normal and the Student's t- distributions. As an application of the models, some German stocks are studied and the different approaches are compared together with the most common method of GARCH(1,1) fit.
In this paper we introduce a derivative-free, iterative method for solving nonlinear ill-posed problems \(Fx=y\), where instead of \(y\) noisy data \(y_\delta\) with \(|| y-y_\delta ||\leq \delta\) are given and \(F:D(F)\subseteq X \rightarrow Y\) is a nonlinear operator between Hilbert spaces \(X\) and \(Y\). This method is defined by splitting the operator \(F\) into a linear part \(A\) and a nonlinear part \(G\), such that \(F=A+G\). Then iterations are organized as \(A u_{k+1}=y_\delta-Gu_k\). In the context of ill-posed problems we consider the situation when \(A\) does not have a bounded inverse, thus each iteration needs to be regularized. Under some conditions on the operators \(A\) and \(G\) we study the behavior of the iteration error. We obtain its stability with respect to the iteration number \(k\) as well as the optimal convergence rate with respect to the noise level \(\delta\), provided that the solution satisfies a generalized source condition. As an example, we consider an inverse problem of initial temperature reconstruction for a nonlinear heat equation, where the nonlinearity appears due to radiation effects. The obtained iteration error in the numerical results has the theoretically expected behavior. The theoretical assumptions are illustrated by a computational experiment.
The HMG-CoA reductase inhibitors SIM, LOV, ATV, PRA, FV and NKS were investigated for their effects on human SkMCs. We were able to demonstrate that statins can induce oxidative stress (ROS formation, GSH-depletion, TBARS), apoptosis (, caspase-3 activity, nuclear morphology) and necrosis (LDH-leakage) in hSkMCs. After incubation with statins, the sequence of cellular events starts by the increased formation of ROS (30 min) followed by caspase-3 activation (2-4 hours) and necrosis (LDH-leakage) and formation of condensed and fragmented nuclei after 24-72 hours. It was shown that, antioxidants (NAC, DTT, TPGS, M-2 and M-3) and the HMG-CoA reductase downstream metabolites (MVA, F, FPP, GG and GGPP) protected against statin-induced ROS formation, caspase-3 activation and partially from necrosis. The caspase-3 inhibitor Ac-DEVD-CHO rescues cells partially from necrosis. These results suggest that the statin-induced necrosis is HMG-CoA dependent and occurs secondary to apoptosis, which by decrease of ATP is driven into necrosis. The increase of ATP observed at low concentrations and early time points suggest an increased glycolytic activity. This was confirmed by increased PDK-4 gene expression and increased PFK2/F-2,6-BPase expression both activator of glycolysis. Glycolysis was also confirmed for some statins by increased cellular lactate concentations. The consequence of PDK-4 mediated pyruvate dehydrogenase inactivation is the metabolic switching from fatty acid to amino acid from proteins as energy source. The oxidative stress hypothesis was further supported by the induction of the FOXO3A transcription factor, which is involved in regulating MnSOD-2 expression in the mitochondrium. The mechanism by which statins produce ROS is still not resolved. There is an indirect evidence from our experiments as well as from the literature, that immediately after the statin treatment, intracellular Ca2+ is mobilized due to HMG-CoA reductase inhibition, which after mitochondrial uptake could lead to increased ROS formation.
In the first part of this work, called Simple node singularity, are computed matrix factorizations of all isomorphism classes, up to shiftings, of rank one and two, graded, indecomposable maximal Cohen--Macaulay (shortly MCM) modules over the affine cone of the simple node singularity. The subsection 2.2 contains a description of all rank two graded MCM R-modules with stable sheafification on the projective cone of R, by their matrix factorizations. It is given also a general description of such modules, of any rank, over a projective curve of arithmetic genus 1, using their matrix factorizations. The non-locally free rank two MCM modules are computed using an alghorithm presented in the Introduction of this work, that gives a matrix factorization of any extension of two MCM modules over a hypersurface. In the second part, called Fermat surface, are classified all graded, rank two, MCM modules over the affine cone of the Fermat surface. For the classification of the orientable rank two graded MCM R-modules, is used a description of the orientable modules (over normal rings) with the help of codimension two Gorenstein ideals, realized by Herzog and Kühl. It is proven (in section 4), that they have skew symmetric matrix factorizations (over any normal hypersurface ring). For the classification of the non-orientable rank two MCM R-modules, we use a similar idea as in the case of the orientable ones, only that the ideal is not any more Gorenstein.