Current efforts to measure child mortality for municipalities in Brazil are hampered by the relative rarity of child deaths, which often results in unstable and unreliable estimates. As a result, it is not possible to accurately assess true levels of child mortality for many areas, hindering efforts towards constructing and implementing effective policy initiatives for the reduction of child mortality. However, with a spatial smoothing process based upon Bayesian Statistics it is possible to “borrow” information from neighboring areas in order to generate more stable and accurate estimates of mortality in smaller areas. The objective of this study is to use this spatial smoothing process to derive estimates of child mortality at the level of the municipality in Brazil. Using data from the 2000 Brazil Census, I derive both Bayesian and non-Bayesian estimates of mortality for each municipality. In comparing the smoothed and raw estimates of this parameter, I find that the Bayesian estimates yield a clearer spatial pattern of child mortality with smaller variances in less populated municipalities, thus, more accurately reflecting the true mortality situation of those municipalities. These estimates can then be used, ultimately, to lead to more effective policies and health initiatives in the fight for the reduction of child mortality in Brazil. / text

In this thesis work, Cyclic Root MUSIC method is integrated with spatial smoothing and interpolation techniques to estimate the direction of arrivals of coherent,cyclostationary signals received via a Uniform Circular Array (UCA). Cyclic Root MUSIC and Conventional Root MUSIC algorithms are compared for various signal scenarios by computer simulations. A cyclostationary process is a random process with probabilistic parameters, such as the autocorrelation function, that vary periodically with time. Most of the man-made communication signals exhibit cyclostationarity due to the periodicity arising from their carrier frequencies, chip rates, baud rates, etc. Cyclic Root MUSIC algorithm exploits the cyclostationarity properties of signals to achieve signal selective direction of arrival estimation. Spatial smoothing is presented to overcome the coherent signals problem in a multipath propagation environment. Forward spatial smoothing and forward backward spatial smoothing techniques are investigated. Interpolation method is presented to cope with the restrictions of spatial smoothing on array structure. Although the array structure that is considered in this thesis (Uniform Circular Array), is not suitable for applying spatial smoothing directly, using interpolation method makes it possible. Performance of Cyclic Root MUSIC and Conventional Root MUSIC algorithms are compared under variation of various factors by computer simulations. Effects of signal type on the performance of the algorithms are observed by using different signal scenarios.

In this thesis, DOA estimation using array interpolation in randomly distributed sensor arrays is considered. Array interpolation is a technique in which a virtual array is obtained from the real array and the outputs of the virtual array, computed from the real array using a linear transformation, is used for direction of arrival estimation. The idea of array interpolation techniques is to make simplified and computationally less demanding high resolution direction finding methods applicable to the general class of non-structured arrays.In this study,we apply an interpolation technique for arbitrary array geometries in an attempt to extend root-MUSIC algorithm to arbitrary array geometries.Another issue of array interpolation related to direction finding is spatial smoothing in the presence of multipath sources.It is shown that due to the Vandermonde structure of virtual array manifold vector obtained from the proposed interpolation method, it is possible to use spatial smoothing algorithms for the case of multipath sources.

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