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非高斯贝叶斯地统计建模AI 解读

Sampling models for geostatistical data are usually based on Gaussian processes. However, real data often display non-Gaussian features, such as heavy tails. In this article we propose a more flexible class of sampling models. We start from the spatial linear model that has a spatial trend plus a stationary Gaussian error process. We extend the sampling model to non-Gaussianity by including a scale parameter at each location. We make sure that we obtain a valid stochastic…

单变量偏斜分布的构造性表示AI 解读

We introduce a general perspective on the introduction of skewness into symmetric distributions. Through inverse probability integral transformations we provide a constructive representation of skewed distributions, where the skewing mechanism and the original symmetric distributions are specified separately. We study the effects of the skewing mechanism on, e.g., modality, tail behavior and the amount of skewness generated. The representation is used to introduce novel…

含非期望产出的多产出生产模型AI 解读

Many production processes yield both good outputs and undesirable ones (e.g., pollutants). In this article we develop a generalization of a stochastic frontier model that is appropriate for such technologies. We discuss efficiency analysis and, in particular, define technical and environmental efficiency in the context of our model. We develop methods for carrying out Bayesian inference and apply them to a panel data set of Dutch dairy farms, where excess nitrogen production…