In this paper, we propose a new speech probability distribution, two-sided generalized gamma distribution (GΓD) for an efficient parametric characterization of speech spectra. GΓD forms a generalized class of parametric distributions including the Gaussian, Laplacian and Gamma probability density functions (pdf’s) as special cases. All the parameters associated with the GΓD are estimated by the...

The use of quantum bits (qubits) in cryptography holds the promise of secure cryptographic quantum key distribution schemes. It is based usually on single-photon polarization states. Unfortunately, the implemented “qubits” in the usual weak pulse experiments are not true two-level systems, and quantum key distribution based on these imperfect qubits is totally insecure in the presence of high (...

Online learning is discussed from the viewpoint of Bayesian statistical inference. By replacing the true posterior distribution with a simpler parametric distribution, one can define an online algorithm by a repetition of two steps: An update of the approximate posterior, when a new example arrives, and an optimal projection into the parametric family. Choosing this family to be Gaussian, we sh...

Non-parametric paired analyses are approached through the same logic as the parametric paired t-tests: a 2 sample paired analysis can be reduced to a 1 sample test by creating a single distribution of the differences between each pair. If the distribution of the differences are nonnormal, and cannot be normalized by transforming the data to some other ratio scale, a 1 sample non-parametric test...

Moments or cumulants have been traditionally used to characterize a probability distribution or an observed data set. Recently, L-moments and trimmed L-moments have been noticed as appealing alternatives to the conventional moments. This paper promotes the use of L-moments proposing new parametric families of distributions that can be estimated by the method of L-moments. The theoretical L-mome...

In this paper we propose a weighted integrated conditional moment (ICM) test of the validity of parametric specifications of conditional distribution models for stationary time series, by extending the weighted ICM test of Bierens (1984) for time series regression models to complete parametric conditional distribution specifications. ∗Support for research within the Center for the Study of Auct...

We establish a general framework to explore parametric statistics of individual energy levels in unitary random matrix ensembles. For a generic confinement potential W (H), we (i) find the joint distribution functions of the eigenvalues of H and H = H + V for an arbitrary fixed V both for finite matrix size N and in the “thermodynamic” N → ∞ limit; (ii) derive manypoint parametric correlation f...

Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These distributions are themselves stochastic, therefore we estimate their variance by epoch based leave-o...

In model-based clustering, the density of each cluster is usually assumed to be a certain basic parametric distribution, e.g., the normal distribution. In practice, it is often difficult to decide which parametric distribution is suitable to characterize a cluster, especially for multivariate data. Moreover, the densities of individual clusters may be multi-modal themselves, and therefore canno...