Among the most important models for long-range dependent time series is the class of ARFIMA$(p,d,q)$ (Autoregressive Fractionally Integrated Moving Average) models. Estimating the long-range dependence parameter $d$ in ARFIMA models is a well-studied problem, but the literature regarding the estimation of $d$ in the presence of missing data is very sparse. There are two basic approaches to dealing with the problem: missing data can be imputed using some plausible method, and then the estimation can proceed as if no data were missing, or we can use a specially tailored methodology to estimate $d$ in the presence of missing data. In this work, we review some of the methods available for both approaches and compare them through a Monte Carlo simulation study. We present a comparison among 35 different setups to estimate $d$, under tenths of different scenarios, considering percentages of missing data ranging from as few as 10\% up to 70\% and several levels of dependence.

翻译：在长记忆时间序列的最重要模型中，ARFIMA$(p,d,q)$（自回归分数整合移动平均）模型是一类核心模型。估计ARFIMA模型中长记忆参数$d$是一个研究充分的问题，但关于存在缺失数据时估计$d$的文献非常稀少。解决该问题有两种基本方法：采用某种合理方法对缺失数据进行插补，然后像无数据缺失那样进行估计；或者使用专门设计的方法在存在缺失数据时估计$d$。本文回顾了两种方法中可用的部分方法，并通过蒙特卡罗模拟研究进行比较。我们在数十种不同场景下，针对从10%到70%不等的缺失数据比例以及多个依赖水平，比较了35种不同的估计$d$的配置方案。