In this paper, the first-order non-negative integer-valued autoregressive process with Poisson-transmuted exponential innovations is introduced. Three estimation methods, namely, the conditional maximum likelihood, conditional least squares and Yule-Walker estimation methods are discussed to estimate the unknown parameters of the proposed process. Additionally, the simulation study is presented to assess the efficiencies of these estimation methods. Applications to two real-life data sets illustrate the usefulness of the proposed process.

中文翻译：

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使用新型 INAR(1)-PTE 过程建模

本文介绍了具有泊松变换指数创新的一阶非负整数值自回归过程。讨论了三种估计方法，即条件最大似然法、条件最小二乘法和Yule-Walker 估计方法来估计所提出过程的未知参数。此外，还介绍了模拟研究以评估这些估计方法的效率。对两个真实数据集的应用说明了所提议过程的有用性。