论文标题
关于固定混合过渡分布模型的构建和估计
On Construction and Estimation of Stationary Mixture Transition Distribution Models
论文作者
论文摘要
混合物过渡分布时间序列模型通过为指定数量的滞后数量的每一个的一阶过渡密度的加权组合建立了高阶依赖性。我们为构造固定过渡混合物分布模型提供了一个框架，该模型超出了线性的高斯动力学。我们研究了一阶严格平稳性的条件，这些条件允许在边缘密度预先指定的家族的一阶过渡密度的连续或离散家族的不同构造中，并具有一般形式的形式，以实现由此产生的条件期望。推论和预测是在贝叶斯框架下开发的，特别强调了混合物重量的柔性，结构化的先验。通过分析和合成数据示例对模型属性进行了研究。最后，通过真实的数据应用程序说明了泊松和lomax示例。
Mixture transition distribution time series models build high-order dependence through a weighted combination of first-order transition densities for each one of a specified number of lags. We present a framework to construct stationary transition mixture distribution models that extend beyond linear, Gaussian dynamics. We study conditions for first-order strict stationarity which allow for different constructions with either continuous or discrete families for the first-order transition densities given a pre-specified family for the marginal density, and with general forms for the resulting conditional expectations. Inference and prediction are developed under the Bayesian framework with particular emphasis on flexible, structured priors for the mixture weights. Model properties are investigated both analytically and through synthetic data examples. Finally, Poisson and Lomax examples are illustrated through real data applications.
