Generalised Wishart Processes

We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture a diverse class of covariance structures, it can easily handle missing data, the dependent variable can readily include covariates other than time, and it scales well with dimension; there is no need for free parameters, and optional parameters are easy to interpret. We describe how to construct the GWP, introduce general procedures for inference and predictions, and show that it outperforms its main competitor, multivariate GARCH, even on financial data that especially suits GARCH. We also show how to predict the mean of a multivariate process while accounting for dynamic correlations.

Socio-economic dimension of fish farming in Assam

Information on socio-economic framework of the fish farmer community forms a benchmark for policy formulation to develop this economically backward sector. Very few studies have been conducted on the socio-economic aspect of fish farming. Two districts of Assam, Darrang and Nagaon, were selected for this study where 120 respondents from each district were selected randomly. The characteristics representing the personnel and socio-economic attributes of the fish farmers are presented in this paper. The socio-economic status of fish farmers has to be improved by bringing the modern concepts of fish farming to the doorstep of farmers.