THE FINITE-SAMPLE DISTRIBUTION OF POST-MODEL-SELECTION ESTIMATORS AND UNIFORM VERSUS NONUNIFORM APPROXIMATIONS

By H. Leeb, Benedikt M. Poetscher. Econometric Theory, 2003

149 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eaf121b1d613a236e0ec41c13874a567823e8419

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In Economics and Econometrics

Citations in the corpus

PREDICTION/ESTIMATION WITH SIMPLE LINEAR MODELS: IS IT REALLY THAT SIMPLE? (2006, unknown): “…2004, and references therein!+ The finite-sample distributions of such post-model-selection estimators have been derived also ~see Leeb, 2005; Leeb and Pötscher, 2003!+ It was also shown that an asymptotic optimality property of the Akaike information criterion ~AIC! in a nonparametric…”
Asymptotic post‐selection inference for the Akaike information criterion (2018, unknown): “One limitation of the Akaike information criterion is that the selection of an overspecified model does not happen in a uniform way (Leeb & Pötscher, 2003).”
Distribution Theory of the Least Squares Averaging Estimator (2013, unknown): “4 It is a common technique to analyze the asymptotic and finite sample properties of the model selection and averaging estimator, for example, Leeb and Pötscher (2005), Pötscher (2006), Elliott, Gargano, and Timmermann (2013), and Hansen (2013).”
INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2018, background): “There is a large literature on inference after model selection, including Pötscher (1991), Kabaila (1995, 1998), Pötscher and Leeb (2009), and Leeb and Pötscher (2003, 2005, 2006, 2008, 2017).”
On various confidence intervals post-model-selection (2014, result): “…example, by Brown (1967); Buehler and Feddersen (1963); Dijkstra and Veldkamp (1988); Kabaila (1998, 2009); Kabaila and Leeb (2006); Leeb (2006); Leeb and Pötscher (2003, 2005, 2006a, 2006b, 2008a, 2008b); Olshen (1973); Pötscher (1991, 2006); Pötscher and Leeb (2009); Pötscher and Schneider…”
Exact post-selection inference for the generalized lasso path (2018, unknown): “(2016) improve on the method in Lockhart et al. (2014), and introduce a pivot-based framework for postselection inference. Lee et al. (2016) describe the application to the lasso problem at a fixed tuning parameter λ; Tibshirani et al. (2016) describe the application to the lasso path at a fixed num”
Spike and slab variable selection: Frequentist and Bayesian strategies (2005, methodology): “(Note that this argument shows β Leeb and P¨otscher (2003). b b This will be important in the proof of Theorem 8.)”
On the harm that ignoring pretesting can cause (2004, background): “In fact, even if the observations y1; : : : ; yn are not normal and the data-generating process is unknown, br and ̂ will still be uncorrelated, as long as the {yi} are uncorrelated with constant variance (Leeb and PJotscher (2003), Lemma A.1).”
Spatial weights matrix selection and model averaging for spatial autoregressive models (2017, background): “The distributional properties will be very intricate; see, e.g., Leeb and Pöscher (2003, 2005) for discussions on post-model-selection estimators.”
Uniform asymptotic inference and the bootstrap after model selection (2015, unknown): “In a sequence of papers, Leeb & Potscher (2003, 2006, 2008) prove that in a classical regression setting, it is impossible to find the distribution of a post-selection estimator of the underlying coefficients, even asymptotically.”