the project was undertaken to study the evaluation of effectiveness of crop advisory services and suggested measures for filling the gap in aurangabad district of maharashtra in india. the survey was carried out in 2010. the data was collected with the help of a specifically designed and pre-tested questionnaire. the project carried out in catchment area of advisory services has given substanti...

purpose of this section is to show how many observations are actually analyzed in multivariate analyses, such as multiple linear regression analysis or multiple logistic regression analysis, when there are different numbers of missing values in each explanatory variable. Let’s perform a multiple linear regression using the following hypothetical data (Table 1). In this data, explanatory variabl...

This paper develops a methodology for approximating the posterior first two moments of the posterior distribution in Bayesian inference. Partially specified probability models, which are defined only by specifying means and variances, are constructed based upon second-order conditional independence, in order to facilitate posterior updating and prediction of required distributional quantities. ...

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and...

This paper concerns segmented multivariate regression models, models which have different linear forms in different subdomains of the domain of an independent variable. Without knowing that number and their boundaries, we first estimate the number of these subdomains using a modified Schwarz criterion. The estimated number of regions proves to be weakly consistent under fairly general condition...

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...