The overlap representation of skewed parton distributions (SPDs) is discussed and applications to wide-angle Compton scattering and electroproduction of mesons are presented. The amplitudes for these processes factorise into parton-level subprocess amplitudes and form factors representing 1/x-moments of SPDs. Key-words: skewed parton distributions, wide-angle exclusive reactions Invited talk pr...

In this paper we propose a robust classification rule for skewed distributions. For low dimensional data, the classification is based on the adjusted outlyingness. In the case of high dimensional data, the robustified SIMCA method is adjusted for skewness. The robustness of the method is investigated through different simulations and by applying it to various real examples.

BACKGROUND There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. METHODS Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Mu...

In many applications, observed spatial variables have skewed distributions. It is often of interest to model the shape of the skewed marginal distribution as well as the spatial correlations. We propose a class of stationary processes that have skewed marginal distributions. The covariance function of the process can be given explicitly. We study maximum likelihood inference through a Monte Car...

Most research on panel data focuses mean or quantile regression, while there is not much about regression methods based the mode. In this paper, we propose a new model named fixed effects modal for in which how conditional mode of response variable depends covariates and employ kernel-based objective function to simplify computation. The proposed can complement regressions provide better centra...

Much work has been done in the area of cluster weighted model (CWM), which extends finite mixture regression to include modelling covariates. Although many types distributions have considered for both response(s) and covariates, our knowledge skewed not yet this paradigm. Herein, a family 24 novel CWMs is allows responses covariates be modelled using one four (the generalized hyberbolic three i...

This study develops practical methods for Bayesian nonparametric inference in regression models. The emphasis is on extending a nonparametric treatment of the regression function to the full conditional distribution. It applies these methods to the relationship of earnings of men in the United States to their age and education over the period 1967 through 1996. Principal findings include increa...

How does the structure of a series of payments influence its recipient's satisfaction? A common hypothesis is that each payment will be compared with a single "standard" or "reference" payment (e.g., the average payment). Cognitive models of judgment such as range frequency theory predict in contrast that the entire payment distribution will influence evaluation of each individual payment. Two ...

Accurate effort prediction of software projects is of concern to portfolio managers, customers, vendors as well as project managers. Ordinary least squares (OLS) regression is widely used to create software prediction models, and it seems to perform just as well or better than most other, non-regression, prediction models. Software data sets may however exhibit certain characteristics that do n...

Mobile Money Fraud is advancing in developing countries. We propose a solution to this problem based on machine learning. Labeled data from financial transactions which include mobile money are, however, skewed towards the negative class. Machine learning models built with such datasets are unreliable as prediction algorithms will be biased investigate performance of different sampling and weig...