On the Asymptotic Theory of Estimation and Testing Hypotheses

By Lucien Le Cam. 1956

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Citations in the corpus

Metalearners for estimating heterogeneous treatment effects using machine learning (2017, methodology): “We start with a similar decomposition as [24],”
Large sample estimation and hypothesis testing (1986, background): “341n particular, one of the precise results on efficiency of MLE is the HajekkLeCam representation theory, which shows efficiency in a class of reyular estimators. See, e.g. Newey (1990) for a discussion of regularity.”
Estimation of Copula Models for Time Series of Possibly Different Length (2001, unknown): “In the situation that we have the same amount of data available for all three estimators, '̂, °̂ and ·̂, it is well known (see Le Cam, 1956, for example) that the one-stage maximum likelihood estimator is the most e±cient estimator, in that it attains the minimum asymptotic variance bound.”
Higher‐Order Improvements of a Computationally Attractive k‐Step Bootstrap for Extremum Estimators (2002, background): “It is closely related to the one-step and k-step estimators considered by many authors, including Fisher (1925), LeCam (1956) , Pfanzagl (1974), Janssen, Jureckova, and Veraverbeke (1985), and Robinson (1988), among others.”
Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory (2013, unknown): “This is analogous to what happens in classical statistics, where local asymptotic normality (LAN) can be used to characterize an estimator distribution, and hence derive test statistics [55], [56].”