The method of envelopes to concisely calculate semiparametric efficient scores under parametric restrictions.

IF 1.2 4区 数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Constantine E Frangakis
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Abstract

When addressing semiparametric problems with parametric restrictions (assumptions on the distribution), the efficient score (ES) of a parameter is often important for generating useful estimates. However, usual derivation of ES, although conceptually simple, is often lengthy and with many steps that do not help in understanding why its final form arises. This drawback often casts onto semiparametric estimation a mantle that can turn away otherwise able doctoral students or researchers. Here we show that many ESs can be obtained as a one-step derivation after we characterize those features (envelopes) of the unrestricted problem that are constrained in the restricted problem. We demonstrate our arguments in three problems with known ES but whose usual derivations are lengthy. We show that the envelope-based derivation is dramatically explanatory and compact, needing essentially two lines where the standard approach needs 10 or more pages. This suggests that the envelope method can add useful intuition and exegesis to both teaching and research of semiparametric estimation.

在参数限制下,采用包络法简明地计算半参数有效分数。
当处理具有参数限制(分布假设)的半参数问题时,参数的有效分数(ES)对于生成有用的估计通常很重要。然而,通常的ES推导虽然在概念上很简单,但通常很长,并且有许多步骤,这无助于理解其最终形式产生的原因。这个缺点常常给半参数估计蒙上一层外衣,使其他方面有能力的博士生或研究人员望而却步。在这里,我们证明了在我们描述了限制问题中约束的无限制问题的特征(包络)之后,可以通过一步推导得到许多ESs。我们用已知ES的三个问题来证明我们的论点,但通常的推导都很长。我们展示了基于信封的推导非常具有解释性和紧凑性,基本上只需要两行,而标准方法需要10页或更多页。这表明包络方法可以为半参数估计的教学和研究增加有用的直观和注释。
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY
CiteScore
2.10
自引率
8.30%
发文量
28
审稿时长
>12 weeks
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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