模型选择技术综述
Model Selection Techniques -- An Overview
摘要：
在大数据时代，分析师通常会对观察到的数据探索各种统计模型或机器学习方法，以促进科学发现或获得预测能力。无论采用何种数据和拟合程序，关键的一步是从一组候选者中选择最合适的模型或方法。模型选择是数据分析中的一个关键因素，用于可靠和可重复的统计推断或预测，因此是生态学、经济学、工程学、金融学、政治学、生物学和流行病学等领域科学研究的核心。模型选择技术起源于统计学、信息论和信号处理等领域的研究，有着悠久的历史。已经提出了相当多的方法，遵循不同的理念，表现出不同的性能。本文的目的是从动机、大样本性能和适用性方面对它们进行全面概述。我们提供了综合和实际相关的讨论国家的最先进的模型选择方法的理论属性。我们还分享了我们的想法，对实践中的模型选择的一些有争议的观点。
英文摘要：
In the era of big data, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction, and thus central to scientific studies in fields such as ecology, economics, engineering, finance, political science, biology, and epidemiology. There has been a long history of model selection techniques that arise from researches in statistics, information theory, and signal processing. A considerable number of methods have been proposed, following different philosophies and exhibiting varying performances. The purpose of this article is to bring a comprehensive overview of them, in terms of their motivation, large sample performance, and applicability. We provide integrated and practically relevant discussions on theoretical properties of state-of- the-art model selection approaches. We also share our thoughts on some controversial views on the practice of model selection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
