This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The us...

drop plate technique has a priority and preference compared with the spread plate procedure, because of less time, quantity of media, effort requirement, little incubator space, and less labor intensive. the objective of this research was to compare the accuracy and fidelity of drop plate method vs. spread plate method by parametric and nonparametric statistical tests. for bacterial enumeration...

Bayesian semi-parametric inference is considered for a log-linear model. This model consists of a parametric component for the regression coeecients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coeecients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov ch...

In this paper we discuss the problem on parametric and non parametric estimation of the distributions generated by the Marshall-Olkin copula. This copula comes from the Marshall-Olkin bivariate exponential distribution used in reliability analysis. Through this copula we can extend the Marshall-Olkin distribution in order to construct several bivariate survival functions. The cumulative distrib...

This paper investigates the performance of three types of random coefficients logistic regression models; that is, models using parametric, semi-parametric, and nonparametric specifications of the distribution of the random effects. Whereas earlier studies focussed on models with a single random effect, here we look at models with multidimensional random effects (intercepts and slopes). Moreove...

The relationship is studied between possibility and necessity measures defined on arbitrary spaces, the theory of imprecise probabilities, and elementary random set theory. It is shown how special random sets can be used to generate normal possibility and necessity measures, as well as their natural extensions. This leads to interesting alternative formulas for the calculation of these natural ...

We study the relationship between possibility and necessity measures defined on arbitrary spaces, the theory of imprecise probabilities, and elementary random set theory. We show how special random sets can be used to generate normal possibility and necessity measures, as well as their natural extensions. This leads to interesting alternative formulas for the calculation of these natural extens...

In this work a glass micromodel which its grains and pores are non-uniform in size, shape and distribution is considered as porous medium. A two-dimensional random network model of micromodel with non-uniform pores has been constructed. The non-uniformity of porous model is achieved by assigning parametric distribution functions to pores throat and pores length, which was measured using ima...

Much recent work has focused on the estimation of regression functions in samples which are truncated or censored. Much of this work has focused on the estimation of a parametric regression function with an error distribution of unknown form. While these method relax a strong parametric assumption about which we seldom have a priori information, they still impose a strong parametric assumption ...

Given an MOLP with fuzzy coefficients, this study proposed a solution procedure to find the exact solution-mix for any α -leveled satisfaction, which is in the form of an interval-valued problem. By incorporating the perturbation on weights and the degrees of possibility of the profits and resources, a multiparametric model can be solve by using intra-parametric analysis for the efficient contr...