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首页> 外文期刊>Journal of applied statistics >Nonlinear semiparametric autoregressive model with finite mixtures of scale mixtures of skew normal innovations
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Nonlinear semiparametric autoregressive model with finite mixtures of scale mixtures of skew normal innovations

机译：非线性正参有限比例混合的半参数自回归模型

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摘要

We propose data generating structures which can be represented as the nonlinear autoregressive models with single and finite mixtures of scale mixtures of skew normal innovations. This class of models covers symmetric/asymmetric and light/heavy-tailed distributions, so provide a useful generalization of the symmetrical nonlinear autoregressive models. As semiparametric and nonparametric curve estimation are the approaches for exploring the structure of a nonlinear time series data set, in this article the semiparametric estimator for estimating the nonlinear function of the model is investigated based on the conditional least square method and nonparametric kernel approach. Also, an Expectation-Maximization-type algorithm to perform the maximum likelihood (ML) inference of unknown parameters of the model is proposed. Furthermore, some strong and weak consistency of the semiparametric estimator in this class of models are presented. Finally, to illustrate the usefulness of the proposed model, some simulation studies and an application to real data set are considered.
机译：我们提出了数据生成结构，可以将其表示为具有偏态法向创新的比例混合的单个和有限混合的非线性自回归模型。此类模型涵盖对称/不对称和轻/重尾分布，因此可为对称非线性自回归模型提供有用的概括。由于半参数和非参数曲线估计是探索非线性时间序列数据集结构的方法，因此本文基于条件最小二乘法和非参数核方法研究了用于估计模型非线性函数的半参数估计器。此外，提出了一种期望最大类型算法来执行模型未知参数的最大似然（ML）推断。此外，提出了这类模型中半参数估计的强弱一致性。最后，为了说明所提出模型的有效性，考虑了一些仿真研究并将其应用于实际数据集。

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