Discriminating Between Binomial Distributions

Discriminating between Weibull and generalized exponential distributions

Recently the two-parameter generalized exponential (GE) distribution was introduced by the authors. It is observed that a GE distribution can be considered for situations where a skewed distribution for a non-negative random variable is needed. The ratio of the maximized likelihoods (RML) is used in discriminating between Weibull and GE distributions. Asymptotic distributions of the logarithm o...

Testing Poisson Binomial Distributions

A Poisson Binomial distribution over n variables is the distribution of the sum of n independent Bernoullis. We provide a sample near-optimal algorithm for testing whether a distribution P supported on {0, . . . , n} to which we have sample access is a Poisson Binomial distribution, or far from all Poisson Binomial distributions. The sample complexity of our algorithm is O(n) to which we provid...

Binomial and multinomial distributions

Distance measure between Gaussian distributions for discriminating speaking styles

Discriminating speaking styles is an important issue in speech recognition, speaker recognition and speaker segmentation. This paper compares distance measures between Gaussian distributions for discriminating speaking styles. The Mahalanobis distance, the Bhattacharyya distance and the Kullback-Leibler divergence, which are in common use for a definition as a distance measure between Gaussian ...

Discriminating between Light- and Heavy-Tailed Distributions with Limit Theorem

In this paper we propose an algorithm to distinguish between light- and heavy-tailed probability laws underlying random datasets. The idea of the algorithm, which is visual and easy to implement, is to check whether the underlying law belongs to the domain of attraction of the Gaussian or non-Gaussian stable distribution by examining its rate of convergence. The method allows to discriminate be...