Most macroeconomic activity series such as Swedish GDP growth are collected quarterly while an important proportion of time series are recorded at a higher frequency. Thus, policy and business decision makers are often confront with the problems of forecasting and assessing current business and economy state via incomplete statistical data due to publication lags. In this paper, we survey a few general methods and examine different models for mixed frequency issues. We mainly compare mixed data sampling regression (MIDAS) and state space dynamic factor model (SS-DFM) by the comparison experiments forecasting Swedish GDP growth with various economic indicators. We find that single-indicator MIDAS is a wise choice when the explanatory variable is coincident with the target series; that an AR term enables MIDAS more promising since it considers autoregressive behaviour of the target series and makes the dynamic construction more flexible; that SS-DFM and M-MIDAS are the most outstanding models and M-MIDAS dominates undoubtedly at short horizons up to 6 months, whereas SS-DFM is more reliable at long predictive horizons. And finally we conclude that there is no perfect winner because each model can dominate in a special situation.

By using AR/ARX-models on data generated by a nonlinear differential equation system representing a model for the cell-cycle control system in budding yeast, the interactions among proteins and thereby also to some extent the genes, are sought. A method consisting of graphical analysis of differences between estimates from two local linear models seems to make it possible to separate a set of linear equations from the nonlinear system. By comparing the properties of the estimations in the linear equations a set of approximate equations corresponding well to the real ones are found. A NARX model is tested on the same system to see whether it is possible to find the dependencies in one of the nonlinear differential equations. This approach did, for the choice of model, not work.

This report presents work done in the field of output error identification, with application to spark ignition (SI) engine identification for the purpose of air to fuel ratio control. The generic parts of the project consist mainly in setting out the basis for the design of output error identification software. Efficiency issues related to linear state space models have also been explored, and although the software design is not made explicit in this report, many of the important concepts have been implemented in order to provide powerful abstractions for the application to SI engine identification. The SI engine identification data was obtained under normal operating conditions. The goal is to re- estimate models without utilizing a virtual measurement which has been used successfully to estimate models in the past. This turns out to be a difficult problem much related to the lack of excitation in the system input, shortcomings of the fuel dynamics model and the unknown and hard to estimate exhaust sensor characteristics. Indeed, the larger of the previously estimated models are found not to be identifiable in the present situation. However, trivial restrictions of the models (not meaning restriction to trivial models) avoid that problem.

In the present paper we study switching state space models from a Bayesian point of view. For estimation, the model is reformulated as a hierarchical model. We discuss various MCMC methods for Bayesian estimation, among them unconstrained Gibbs sampling, constrained sampling and permutation sampling. We address in detail the problem of unidentifiability, and discuss potential information available from an unidentified model. Furthermore the paper discusses issues in model selection such as selecting the number of states or testing for the presence of Markov switching heterogeneity. The model likelihoods of all possible hypotheses are estimated by using the method of bridge sampling. We conclude the paper with applications to simulated data as well as to modelling the U.S./U.K. real exchange rate. (author's abstract) / Series: Forschungsberichte / Institut für Statistik

Target tracking is a broad subject area extensively studied in many engineering disciplines. In this thesis, target tracking implies the temporal estimation of target features such as the target's direction-of-arrival (DOA), the target's boundary pixels in a sequence of images, and/or the target's position in space. For multiple target tracking, we have introduced a new motion model that incorporates an acceleration component along the heading direction of the target. We have also shown that the target motion parameters can be considered part of a more general feature set for target tracking, e.g., target frequencies, which may be unrelated to the target motion, can be used to improve the tracking performance. We have introduced an acoustic multiple-target tracker using a flexible observation model based on an image tracking approach by assuming that the DOA observations might be spurious and that some of the DOAs might be missing in the observation set. We have also addressed the acoustic calibration problem from sources of opportunity such as beacons or a moving source. We have derived and compared several calibration methods for the case where the node can hear a moving source whose position can be reported back to the node. The particle filter, as a recursive algorithm, requires an initialization phase prior to tracking a state vector. The Metropolis-Hastings (MH) algorithm has been used for sampling from intractable multivariate target distributions and is well suited for the initialization problem. Since the particle filter only needs samples around the mode, we have modified the MH algorithm to generate samples distributed around the modes of the target posterior. By simulations, we show that this mode hungry algorithm converges an order of magnitude faster than the original MH scheme. Finally, we have developed a general framework for the joint state-space tracking problem. A proposal strategy for joint state-space tracking using the particle filters is defined by carefully placing the random support of the joint filter in the region where the final posterior is likely to lie. Computer simulations demonstrate improved performance and robustness of the joint state-space when using the new particle proposal strategy.

The study involves the following stages: First, coupled-inductor and integrated magnetic structure used in Cuk converter circuit topologies are analyzed and the necessary information about these elements in circuit design is gathered. Also, benefits of using these magnetic elements are presented. Secondly / steady-state model, dynamic model and transfer functions of coupled-inductor Cuk converter topology are obtained via state-space averaging method. Third stage deals with determining the design criteria to be fulfilled by the implemented circuit. The selection of the circuit components and the design of the coupled-inductor providing ripple-free input current waveform are performed at this stage. Fourth stage introduces the experimental results of the implemented circuit operating in open loop mode. Besides, the controller design is carried out and the closed loop performance of the implemented circuit is presented in this stage.

All physical systems are affected by some noise that limits the resolution that can be attained in partitioning their state space. For chaotic, locally hyperbolic flows, this resolution depends on the interplay of the local stretching/contraction and the smearing due to noise. My goal is to determine the `finest attainable' partition for a given hyperbolic dynamical system and a given weak additive white noise. That is achieved by computing the local eigenfunctions of the Fokker-Planck evolution operator in linearized neighborhoods of the periodic orbits of the corresponding deterministic system, and using overlaps of their widths as the criterion for an optimal partition. The Fokker-Planck evolution is then represented by a finite transition graph, whose spectral determinant yields time averages of dynamical observables. The method applies in principle to both continuous- and discrete-time dynamical systems. Numerical tests of such optimal partitions on unimodal maps support my hypothesis.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaf 104). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaf 104). Also available on the Internet.

With advances in micro-electronic complexity and fabrication, sophisticated algorithms for source localisation and tracking can now be deployed in cost sensitive appliances for both consumer and commercial markets. As a result, such algorithms are becoming ubiquitous elements of contemporary communication, robotics and surveillance systems. Two of the main requirements of acoustic localisation and tracking algorithms are robustness to acoustic disturbances (to maximise localisation accuracy), and low computational complexity (to minimise power-dissipation and cost of hardware components). The research presented in this thesis covers both advances in robustness and in computational complexity for acoustic source localisation and tracking algorithms. This thesis also presents advances in modelling of sound propagation in indoor environments; a key to the development and evaluation of acoustic localisation and tracking algorithms. As an advance in the field of tracking, this thesis also presents a new method for tracking human speakers in which the problem of the discontinuous nature of human speech is addressed using a new state-space filter based algorithm which incorporates a voice activity detector. The algorithm is shown to achieve superior tracking performance compared to traditional approaches. Furthermore, the algorithm is implemented in a real-time system using a method which yields a low computational complexity. Additionally, a new method is presented for optimising the parameters for the dynamics model used in a state-space filter. The method features an evolution strategy optimisation algorithm to identify the optimum dynamics’ model parameters. Results show that the algorithm is capable of real-time online identification of optimum parameters for different types of dynamics models without access to ground-truth data. Finally, two new localisation algorithms are developed and compared to older well established methods. In this context an analytic analysis of noise and room reverberation is conducted, considering its influence on the performance of localisation algorithms. The algorithms are implemented in a real-time system and are evaluated with respect to robustness and computational complexity. Results show that the new algorithms outperform their older counterparts, both with regards to computational complexity, and robustness to reverberation and background noise. The field of acoustic modelling is advanced in a new method for predicting the energy decay in impulse responses simulated using the image source method. The new method is applied to the problem of designing synthetic rooms with a defined reverberation time, and is compared to several well established methods for reverberation time prediction. This comparison reveals that the new method is the most accurate.