An important challenge in statistical analysis lies in controlling the estimation bias when handling the ever-increasing data size and model complexity of modern data settings. In this paper, we propose a reliable estimation and inference approach for parametric models based on the Just Identified iNdirect Inference estimator (JINI). The key advantage of our approach is that it allows to construct a consistent estimator in a simple manner, while providing strong bias correction guarantees that lead to accurate inference. Our approach is particularly useful for complex parametric models, as it allows to bypass the analytical and computational difficulties (e.g., due to intractable estimating equation) typically encountered in standard procedures. The properties of JINI (including consistency, asymptotic normality, and its bias correction property) are also studied when the parameter dimension is allowed to diverge, which provide the theoretical foundation to explain the advantageous performance of JINI in increasing dimensional covariates settings. Our simulations and an alcohol consumption data analysis highlight the practical usefulness and excellent performance of JINI when data present features (e.g., misclassification, rounding) as well as in robust estimation.

翻译：统计分析中的一个重要挑战在于处理现代数据场景中日益增长的数据规模和模型复杂性时控制估计偏差。本文基于恰好识别的间接推断估计器（JINI），提出了一种适用于参数模型的可靠估计与推断方法。该方法的关键优势在于以简洁方式构建一致估计量的同时，提供强有力的偏差校正保证以实现精确推断。对于复杂参数模型，该方法尤为实用，可规避标准流程中常见的解析与计算困难（例如因难以求解的估计方程导致的问题）。本文还研究了参数维度发散情形下JINI的性质（包括一致性、渐近正态性及其偏差校正特性），为解释JINI在高维协变量场景中的优越表现提供了理论基础。我们的模拟实验与一项酒精消费数据分析表明，当数据存在错分类、舍入等特征以及进行稳健估计时，JINI具有突出的实用价值和优异性能。