18,254 research outputs found
Inflated Beta Distributions
This paper considers the issue of modeling fractional data observed in the
interval [0,1), (0,1] or [0,1]. Mixed continuous-discrete distributions are
proposed. The beta distribution is used to describe the continuous component of
the model since its density can have quite diferent shapes depending on the
values of the two parameters that index the distribution. Properties of the
proposed distributions are examined. Also, maximum likelihood and method of
moments estimation is discussed. Finally, practical applications that employ
real data are presented.Comment: 15 pages, 4 figures. Submitted to Statistical Paper
Local power of the LR, Wald, score and gradient tests in dispersion models
We derive asymptotic expansions up to order for the nonnull
distribution functions of the likelihood ratio, Wald, score and gradient test
statistics in the class of dispersion models, under a sequence of Pitman
alternatives. The asymptotic distributions of these statistics are obtained for
testing a subset of regression parameters and for testing the precision
parameter. Based on these nonnull asymptotic expansions it is shown that there
is no uniform superiority of one test with respect to the others for testing a
subset of regression parameters. Furthermore, in order to compare the
finite-sample performance of these tests in this class of models, Monte Carlo
simulations are presented. An empirical application to a real data set is
considered for illustrative purposes.Comment: Submitted for publicatio
Video Acceleration Magnification
The ability to amplify or reduce subtle image changes over time is useful in
contexts such as video editing, medical video analysis, product quality control
and sports. In these contexts there is often large motion present which
severely distorts current video amplification methods that magnify change
linearly. In this work we propose a method to cope with large motions while
still magnifying small changes. We make the following two observations: i)
large motions are linear on the temporal scale of the small changes; ii) small
changes deviate from this linearity. We ignore linear motion and propose to
magnify acceleration. Our method is pure Eulerian and does not require any
optical flow, temporal alignment or region annotations. We link temporal
second-order derivative filtering to spatial acceleration magnification. We
apply our method to moving objects where we show motion magnification and color
magnification. We provide quantitative as well as qualitative evidence for our
method while comparing to the state-of-the-art.Comment: Accepted paper at CVPR 2017. Project webpage:
http://acceleration-magnification.github.io
- …