We propose Bayesian nonparametric methodology for quantile regression modeling. In particular, we develop Dirichlet process mixture models for the error distribution in an additive quantile regression formulation. The proposed nonparametric prior probability models allow the data to drive the shape of the error density and thus provide more reliable predictive inference than models based on par...

A continuous case of new density function called harmonic probability density function (HPDF) is structured using mathematical method. The mean and variance of this probability function is presented using both the definition and moment generating function. A discussion has started on many other connections that may exist between this new model and its applications in geometry, probability, and ...

In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and select significant variables for parametric portion. Thus, it is much more challenging than that for parametric models such as linear models and generalized linear...

This is a brief tutorial to accompany a set of functions that we have written to facilitate fitting BRT (boosted regression tree) models in R . This tutorial is a modified version of the tutorial accompaniying Elith, Leathwick and Hastie’s article in Journal of Animal Ecology. It has been adjusted to match the implementation of these functions in the ’dismo’ package. The gbm* functions in the d...

When reviewing scientific literature, it would be useful to have automatic tools that identify the most influential scientific articles as well as how ideas propagate between articles. In this context, this paper introduces topical influence, a quantitative measure of the extent to which an article tends to spread its topics to the articles that cite it. Given the text of the articles and their...

Multilevel modeling is often treated as if it concerns only regression analysis and growth modeling. Multilevel modeling, however, is relevant for nested data not only with regression and growth analysis but with all types of statistical analyses. This chapter has two aims. First, it shows that already in the traditional multilevel analysis areas of regression and growth there are several new m...

     Data reduction is used to aggregate or amalgamate the large data sets into smaller and manageable information pieces in order to fast and accurate classification of different attributes. However, excessive spatial or spectral data reduction may result in losing or masking important radiometric information. Therefore, we conducted this research to evaluate the effectiveness of the different...