An Introduction to the Bootstrap
An Introduction to the Bootstrap
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DOI：
10.2307/2983304
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发表时间：
1995-03
期刊：
影响因子：
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通讯作者：
B. Efron;R. Tibshirani
中科院分区：
文献类型：
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作者：
B. Efron;R. Tibshirani
统计学是一门从经验中学习的科学，尤其是那些一次只得到一点点的经验。最早的信息科学是统计学，起源于1650年左右。在这个世纪，统计技术已经成为生物医学、心理学、教育学、经济学、传播理论、社会学、遗传学研究、流行病学和其他领域的分析方法。最近，地质学、物理学和天文学等传统科学开始越来越多地使用统计方法，因为它们专注于需要信息效率的领域，例如研究稀有和奇异粒子或极遥远的星系。大多数人不是天生的统计学家。让我们自己的设备，我们不是很擅长从嘈杂的数据海洋中挑选模式。换句话说，我们都太擅长挑选不存在的模式，而这些模式恰好适合我们的目的。统计理论从两个方面解决了这个问题。它提供了在噪声背景中寻找真实的信号的最佳方法，并且还提供了对随机模式的过度解释的严格检查。
Statistics is the science of learning from experience, especially experience that arrives a little bit at a time. The earliest information science was statistics, originating in about 1650. This century has seen statistical techniques become the analytic methods of choice in biomedical science, psychology, education, economics, communications theory, sociology, genetic studies, epidemiology, and other areas. Recently, traditional sciences like geology, physics, and astronomy have begun to make increasing use of statistical methods as they focus on areas that demand informational efficiency, such as the study of rare and exotic particles or extremely distant galaxies. Most people are not natural-born statisticians. Left to our own devices we are not very good at picking out patterns from a sea of noisy data. To put it another way, we are all too good at picking out non-existent patterns that happen to suit our purposes. Statistical theory attacks the problem from both ends. It provides optimal methods for finding a real signal in a noisy background, and also provides strict checks against the overinterpretation of random patterns.