نتایج جستجو برای: general linear models
تعداد نتایج: 1913077 فیلتر نتایج به سال:
nowadays in trade and economic issues, prediction is proposed as the most important branch of science. existence of effective variables, caused various sectors of the economic and business executives to prefer having mechanisms which can be used in their decisions. in recent years, several advances have led to various challenges in the science of forecasting. economical managers in various fi...
Rural women play a significant role in domestic and socio-economic life of the society so that national development is not possible without developing this important and substantial segment of our society. Given the importance of women in agriculture, this study was conducted to examine the role of Iranian women in the farm management decision-making processed concerning animal husbandry. The s...
attempts have been made to study the thermodynamic behavior of 1,3 butadiene purification columns with the aim of retrofitting those columns to more energy efficient separation schemes. 1,3 butadiene is purified in two columns in series through being separated from methyl acetylene and 1,2 butadiene in the first and second column respectively. comparisons have been made among different therm...
uncertainty in the financial market will be driven by underlying brownian motions, while the assets are assumed to be general stochastic processes adapted to the filtration of the brownian motions. the goal of this study is to calculate the accumulated wealth in order to optimize the expected terminal value using a suitable utility function. this thesis introduced the lim-wong’s benchmark fun...
The multivariate linear model is Y (n×m) = X (n×p) B (p×m) + E (n×m) The multivariate linear model can be fit with the lm function in R, where the left-hand side of the model comprises a matrix of response variables, and the right-hand side is specified exactly as for a univariate linear model (i.e., with a single response variable). This paper explains how to use the Anova and linearHypothesis...
We describe a Bayesian learning algorithm for Robust General Linear Models (RGLMs). The noise is modeled as a Mixture of Gaussians rather than the usual single Gaussian. This allows different data points to be associated with different noise levels and effectively provides a robust estimation of regression coefficients. A variational inference framework is used to prevent overfitting and provid...
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