In this paper, we employ a meta-regression analysis approach to synthesize empirical evidence on the average partial effects of eleven adoption determinants that regularly appear in empirical studies examining farmer's adoption behavior worldwide. Our analysis considers a total of 122 studies from the adoption literature using discrete choice models that are published in 24 peer-reviewed journals since 1985, covering farmer's adoption behavior around the world and for a wide variety of agricultural technologies.
This paper develops a novel empirical framework for estimating individual emission levels in a nonpoint source (NPS) pollution problem. For doing so we incorporate into the GME model suggested by Kaplan et al., (2003) a specific theoretical structure describing both crop production technology and nature's residual generating mechanism based on the multiple production relations model suggested by Murty et al, (2012) fitted into a parametric stochastic framework.
We compared maximum likelihood and the k-NN algorithm in the context of discriminant analysis with spherical data.
We present a new model for analyzing compositional data with structural zeros. Inspired by \cite{butler2008} who suggested a model in the presence of zero values in the data we propose a model that treats the zero values in a different manner. Instead of projecting every zero value towards a vertex, we project them onto their corresponding edge and fit a zero-censored multivariate model.
Discriminant analysis for spherical data, or directional data in general, has not been extensively studied, and most papers focus on one distribution, the von Mises-Fisher. In this work, we study more distributions, escaping the rotational symmetry bound of the aforementioned distribution and also include a non parametric classier, the k-NN algorithm.
A folded type model is developed for analyzing compositional data based that provides a new and flexible class of distributions for modeling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation.
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