Journal ArticleDOI
Bootstrap, wild bootstrap, and asymptotic normality
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TLDR
In this paper, it was shown that for an i.i.d. sample, wild bootstrap works under the same conditions as bootstrap under the assumption that the normal approximation with estimated variance works.Abstract:
We show for an i.i.d. sample that bootstrap estimates consistently the distribution of a linear statistic if and only if the normal approximation with estimated variance works. An asymptotic approach is used where everything may depend onn. The result is extended to the case of independent, but not necessarily identically distributed random variables. Furthermore it is shown that wild bootstrap works under the same conditions as bootstrap.read more
Citations
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MonographDOI
Modelling nonlinear economic time series
TL;DR: In this article, the authors propose a non-parametric approach for estimating parametric models from state space models and nonlinear and non-stationary models, based on nonparametric models and parametric linearity tests.
Journal ArticleDOI
How do bootstrap and permutation tests work
Arnold Janssen,Thorsten Pauls +1 more
TL;DR: In this article, a comprehensive and unified approach for the conditional and unconditional analysis of linear resampling statistics is presented under fairly mild assumptions and an asymptotic series representation for their weak accumulation points.
Journal ArticleDOI
Calibrating parametric subject-specific risk estimation.
TL;DR: This article developed point and interval estimation procedures for t-year mortality rates conditional on the estimated parametric risk score and illustrated with a dataset from a large clinical trial with post-myocardial infarction patients.
Journal ArticleDOI
Weighted resampling of martingale difference arrays with applications
TL;DR: In this article, the behavior of linear resampling statistics in martingale difference arrays Xn,i,i≤k(n) is studied and it is shown that different bootstrap and permutation procedures work if the array (Xn, i)i fulfils the conditions of a general central limit theorem.
Journal ArticleDOI
Information Ratio Test for Model Misspecification in Quasi-Likelihood Inference
TL;DR: In this paper, an information ratio (IR) statistic is proposed to test for model misspecification of the variance/covariance structure through a comparison between two forms of information matrix: the negative sensitivity matrix and the variability matrix.
References
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Journal ArticleDOI
An Introduction to Probability Theory and Its Applications
David A. Freedman,William Feller +1 more
Book
Limit Distributions for Sums of Independent Random Variables
B. W. Gnedenko,A. N. Kolmogorov +1 more
Journal ArticleDOI
Jackknife, Bootstrap and Other Resampling Methods in Regression Analysis
TL;DR: In this paper, a class of weighted jackknife variance estimators for the least square estimator by deleting any fixed number of observations at a time was proposed, and three bootstrap methods were considered.
Book
Asymptotic methods in statistical decision theory
TL;DR: In this article, the authors present a framework for the analysis of decision spaces in decision theory, including the space of risk functions and the spaces of decision processes, and propose a method for measuring the suitability of a decision space.