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Plug-in principle

In statistics, the plug-in principle is the method of estimation of functionals of a population distribution by evaluating the same functionals at… 
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Related topics

Related topics

3 relations
Bootstrapping (statistics)

Broader (2)

Computational statisticsResampling (statistics)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
A Rotation Approach to Subset Inference in Weakly Identified Linear Structural Models ∗
The paper studies GMM inference for subvector hypotheses in structural models where nuisance parameters may not be identified… 
2017
2017
14.382 Spring 2017 Lecture 6: Nonlinear and Binary Regression, Predictive Effects, and M-Estimation
We begin by formulating nonlinear regression models, where nonlinearity may arise via parameters or via variables. We discuss the… 
2017
2017
Statistical hypotheses testing for random and pseudorandom generators based on statistical estimators of entropy
The topical information security problem of development of statistical tests for hypotheses on the discrete uniform distribution… 
2013
2013
A Decision-Theoretic Approach to Forecasting
Statistical forecasting is prediction of future states of a certain process based on the available stochastic observations as… 
Review
2012
Review
2012
Efficient Estimation of Nonlinear Finite Population Parameters Using Nonparametrics
Currently, the high-precision estimation of nonlinear parameters such as Gini indices, low-income proportions or other measures… 
2003
2003
Fast Bootstrap for Model Structure Selection
In this paper we propose an effective procedure to reduce the computation time of a bootstrap approximation of the generalization… 
1993
1993
The empirical distribution function and the plug-in principle
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