We present a new parametric model for the angular measure of a multivariate extreme value distribution. The flexible model can be applied to random vectors of any finite dimension. An advantage of this model is that its parameters correspond to the level of dependence between each pair of components of the random vector, and as such the parameters of the model are more interpretable that those ...

In this paper, we propose an image-driven 3D modeling technique for rapid panel design. Our semi-automatic approach is based on template technique and mesh volume deformation controlled by a special cage. We designed our modeling system to be interactive in 2D, automating the process of shape generation while relying on the user to provide image samples. Once a parametric model template is give...

The digital model of the laboratory bench parts of digging deep rootstalk crops were established through adopting the parametric model technology based on feature. The virtual assembly of the laboratory bench of digging deep rootstalk crops was done and the digital model of the laboratory bench parts of digging deep rootstalk crops was gained. The vibrospade, which is the key part of the labora...

This paper describes a randomization-based estimation and inference procedure for the distribution or quantiles of potential outcomes with a binary treatment and instrument. The method imposes no parametric model for the treatment effect, and remains valid for small n, a weak instrument, or inference on tail quantiles, when conventional large-sample methods break down. The method is illustrated...

Utilizing parametric and nonparametric techniques, we assess the role of a heretofore relatively unexplored ‘input’ in the educational process, homework, on academic achievement. Our results indicate that homework is an important determinant of student test scores. Relative to more standard spending related measures, extra homework has a larger and more significant impact on test scores. Howeve...

The internal estimates of Loss Given Default (LGD) must reflect economic downturn conditions, thus estimating the “downturn LGD”, as the new Basel Capital Accord Basel II establishes. We suggest a methodology to estimate the downturn LGD distribution to overcome the arbitrariness of the methods suggested by Basel II. We assume that LGD is a mixture of an expansion and recession distribution. In...

Wireless Video Transmission Leiming Qian, Douglas L Jones, Kannan Ramchandran and Swaroop Appadwedula January 18, 1999 Abstract With the rapid growth of multimedia content in wireless communication, there is an increasing demand for e cient image and video transmission schemes. In this paper we present a joint sourcechannel matching scheme for wireless video transmission. The scheme jointly opt...

We present a different perspective on the machine translation problem that relies upon continuous-space probabilistic models for words and phrases. Within this perspective we propose a method called Tied-Mixture Machine Translation (TMMT) that uses a trainable parametric model employing Gaussian mixture probability density functions to represent wordand phrase– pairs. In the new perspective, ma...

We propose a novel approach to online estimation of probability density functions, which is based on kernel density estimation (KDE). The method maintains and updates a non-parametric model of the observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach and a compression/revitalization scheme which maintains the KDE’s complexity low. We compare the...

How much information does a neural population convey about a stimulus? Answers to this question are known to strongly depend on the correlation of response variability in neural populations. These noise correlations, however, are essentially immeasurable as the number of parameters in a noise correlation matrix grows quadratically with population size. Here, we suggest to bypass this problem by...