Since the most efficient waveform coding methods use linear transforms before quantization and entropy coding, the methods designed to allow random access to compressed data normally have to deal with the result of these transforms. However, in some specialized technical applications the information needed is the result of non-linear operations. For instance, it is useful to have fast access to...

We present an algorithm for computing exact expressions for the distribution of the maximum or minimum of an arbitrary nite collection of linear combinations of spacings or exponential random variables with rational coeecients. These expressions can then be manipulated or evaluated using symbolic math packages such as MAPLE. As examples, we apply this algorithm to obtain the distributions of th...

Standardizing the empirical distribution function yields a statistic with norm square that matches the chi-square test statistic. To show this one may use the covariance matrix of the empirical distribution which, at any finite set of points, is shown to have an inverse which is tridiagonal. Moreover, a representation of the inverse is given which is a product of bidiagonal matrices correspondi...

In this paper, we propose a speech enhancement method based on spectral magnitude estimation. We modify the noise estimation from the minimum statistics method and combine with a maximum a posterior (MAP) decomposition, using the Rice-conditional probability and a non-Gaussian statistic model of the speech. We derive two versions of magnitude decomposition and magnitude-phase decomposition and ...

A new approach to model order selection is proposed. Based on the theory of sufficient statistics, the method does not require any prior knowledge of the model parameters. It is able to discriminate between models by basing the decision on the part of the data that is independent of the model parameters. This is accomplished conceptually by transforming the data into a sufficient statistic and ...

In the classical approach, the problem of distributed statistical inference and the problem of minimum cost routing of the measurements to the fusion center are treated separately. Such schemes cannot exploit the “inherent” saving in routing costs arising from data reduction in a sufficient statistic for inference. Our approach is to conduct in-network processing of the likelihood function whic...

Recent research by Bliemer and Rose (2005, 2009, in press) and Rose and Bliemer (2005) suggest as a measure for calculating sample size requirements for models estimated using stated choice data, the S-error statistic. Prior to this, existing sampling theories failed to adequately address the issue of sample size requirements specifically for this type of data and hence researchers have had to ...

Uncovering brain activity from magnetoencephalography (MEG) data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically th...