Distribution data on threatened and endangered species are often sparse and clustered making it difficult to model their suitable habitat distribution using commonly used modeling approaches. We used a novel method called maximum entropy distribution modeling or Maxent for predicting potential suitable habitat for Canacomyrica monticola, a threatened and endangered tree species in New Caledonia...

Stein’s method proves weak-convergence results through approximations to expectations without direct use of characteristic functions, allowing it to be used in complex problems with dependence. In this work we show how consistency can be proved and even some error terms provided for any bootstrap with exchangeable weights using Stein’s method. We say that the bootstrap works if the distance bet...

A new class of multivariate discrete distributions with binomial and multinomial marginals is studied. This class of distributions is obtained in a natural manner using probabilistic properties of the sampling model considered. Some possible applications in game theory, life testing and exceedance models for order statistics are discussed.

In this paper, we propose a multivariate time series model for sales count data. Based on the fact that setting an independent Poisson distribution to each brand’s sales produces the Poisson distribution for their total number, characterized as market sales, and then, conditional on market sales, the brand sales follow a multinomial distribution, we first extend this Poisson-multinomial modelin...

In the paper, authors introduce a matrix-parametrized generalization of multinomial probability mass function that involves ratio several multivariate gamma functions. They show logarithmic complete monotonicity this and derive new inequalities involving ratios

Machine Learning algorithms produce accurate classifiers when trained on large, balanced datasets. However, it is generally expensive to acquire labeled data, while unlabeled data is available in much larger amounts. A cost-effective alternative is to use Semi-Supervised Learning, which uses unlabeled data to improve supervised classifiers. Furthermore, for many practical problems, data often e...

The quantitative estimation of Sea Surface Temperatures from fossils assemblages is a fundamental issue in palaeoclimatic and paleooceanographic investigations. The Modern Analogue Technique, a widely adopted method based on direct comparison of fossil assemblages with modern coretop samples, was revised with the aim of conforming it to compositional data analysis. The new CODAMAT method was de...

Interpretive analysis of metagenomic data depends on an understanding of the underlying associations among microbes from metagenomic samples. Although several statistical tools have been developed for metagenomic association studies, they suffer from compositional bias or fail to take into account environmental factors that directly affect the composition of a given microbial community. In this...

MARKET RESPONSE MEASUREMENT USING THE MULTINOMIAL, MULTIATTRIBUTE LOGIT CHOICE MODEL by Peter Manning Guadagni Submitted to the Alfred P. Sloan School of Management on May 15, 1980, in partial fulfillment of the requirements for the degree of Master of Science. Universal Product Code (UPC) scanning is the key to a new marketing data source. Unfortunately, most existing marketing models do not h...