A weighted linear regression model with impercise response and p-real explanatory variables is analyzed. The LR fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. A least square solution for estimating the parameters of the model is derived. The result are illustrated by the means of some case studies.

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...

species diversity is one of the important characteristics shows rangeland ecosystems changes, which isalready under effects of management practices. thus, this research was conducted to study speciesdiversity variation and assess suitable distributive abundance model in three rangeland sites in westazerbaijan. these sites were almost similar in environmental properties and differ in view point ...

The nonparametric estimation(NE) of kernel polynomial regression (KPR) model is a powerful tool to visually depict the effect of covariates on response variable, when there exist unstructured and heterogeneous data. In this paper we introduce KPR model that is the mixture of nonparametric regression models with bootstrap algorithm, which is considered in a heterogeneous and unstructured framewo...

Internet of Things (IoT) is a popular social network in which devices are virtually connected for communicating and sharing information. This applied greatly business enterprises government sectors delivering the services to their customers, clients citizens. But, interaction successful only based on trust that each device has another. Thus very much essential network. As have access over sensi...