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...

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...

We consider in this paper the multivariate regression problem, when the target regression matrix A is close to a low rank matrix. Our primary interest is in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting...

A “multivariate interaction” in a regression model is a product of two independent variates (linear functions of the regressors) that is an additive component of the regression function E(Y |X). In many cases a substantial portion of the overall pairwise interaction structure in a regression function can be captured by a single multivariate interaction. Due to its parsimonious form, a multivari...

We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smo...