In this paper, we introduce a Goodness-of-Fit test for the Multivariate Exponential Power (MEP) distribution, a multivariate extension of the Generalized Gaussian, which has recently gained considerable interest as a model for wavelet coefficients in the context of color image retrieval and spread-spectrum watermarking. We present a size and power study of this test and show Goodness-of-Fit res...

Latent Dirichlet Allocation is a generative topic model for text. In this report, we implement collapsed Gibbs sampling to learn the topic model. We test our implementation on two data sets: classic400 and Psychological Abstract Review. We also discuss the different evaluation of goodness-of-fit of the models how parameter settings interact with the goodness-of-fit.

For goodness-of-fit tests for a model (distribution) admitting nuisance location or scale parameters, second-order asymptotic distributional representations are exploited for some robust estimators that are asymptotically first-order equivalent; their difference then provides a goodness-of-fit test criterion, whose asymptotic properties are studied under the null hypothesis as well as under loc...

The paper presents a new goodness-of-fit test on the basis of the interval estimate of the probability distribution function. The comparative analysis is carried out for the proposed test and for the Kolmogorov test. The results of the numerical modeling show that the proposed test has greater performance than the Kolmogorov test in specific cases. The analysis of correlation properties of the ...

We review two powerful methods to test the Gaussianity of the cosmic microwave background (CMB): one based on the distribution of spherical wavelet coefficients and the other on smooth tests of goodness-of-fit. The spherical wavelet families proposed to analyse the CMB are the Haar and the Mexican Hat ones. The latter is preferred for detecting non-Gaussian homogeneous and isotropic primordial ...

We develop a general theory for the goodness-of-fit test to non-linear models. In particular, we assume that observations are noisy samples of submanifold defined by sufficiently smooth map. The observation noise is additive Gaussian. Our main result shows “residual” model fit, solving least-square problem, follows (possibly noncentral) χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmln...