• Corpus ID: 19878149

An Introduction to the Bootstrap

  title={An Introduction to the Bootstrap},
  author={Michael G. Kenward},
15 Empirical Bayes Method, 2nd edition J.S. Maritz and T. Lwin (1989) Symmetric Multivariate and Related Distributions K.-T. Fang, S. Kotz and K. Ng (1989) Ieneralized Linear Models, 2nd edition P. McCullagh and J.A. Neider (1989) 38 Cyclic Designs J.A. John (1987) 39 Analog Estimation Methods in Econometrics C.F. Manski (1988) 40 Subset Selection in Regression A.J. Miller (1990) 41 Analysis of Repeated Measures M. Crowder and D .J. Hand (1990) 42 Statistical Reasoning with Imprecise… 

Developing Statistical Software in Fortran 95

The Bootstrap Methods and Their Applications and Elements of Computational Statistics, Cambridge, U.K.: Cambridge University Press, 2001, and Monte Carlo Statistical Methods, New York: Springer-Verlag, 2004, respectively.

Bootstrap and approximation methods for long memory processes

Fractionally integrated processes ARFIMA(p,d,q), introduced by Granger (1980) and Hosking (1981) independently, offer a useful tool to model the second order dependence structure (autocovariance and


The problem of constructing confidence intervals for the ratio of variance components in unbalanced random one-way model is to investigate and several approximate methods, which are easier to compute, are discussed.

Latent Curve Models: A Structural Equation Approach

Davison, A. C., and Hinkley, D. V. (1997), Bootstrap Methods and Their Application, Cambridge, U.K.: Cambridge University Press. Efron, B., and Tibshirani, R. (1993), An Introduction to the

Multinomial processing tree models: A review of the literature.

Multinomial processing tree (MPT) models have become popular in cognitive psychology in the past two decades. In contrast to general-purpose data analysis techniques, such as log-linear models or

Nonparametric curve estimation under monotonicity constraint

A finite sample comparison is carried out for three recent nonparametric methodologies in estimating the monotone regression function F and its inverse F 1 . The methods are (1) the inverse kernel

A comparative study of the bias corrected estimates in logistic regression

This article compares the methods proposed by Cordeiro and McCullagh and by Firth on the basis of their bias to show that the methods suggested work well and work well, though Cordeira and McCullaghan is slightly better in the authors' simulations.

Bootstrap for Regression Week 9 , Lecture 1 1 The General Bootstrap

This is a computer-intensive resampling algorithm for estimating the empirical distribution function (EDF) of a random variable X from a set of observations x = {x1, . . . , xn}. This technique

Bootstrap Asymptotics

  • R. Beran
  • Mathematics
    International Encyclopedia of Statistical Science
  • 2011
The bootstrap, introduced by Efron (1979), merges simulation with formal model-based statistical inference and appeals intellectually when empirically supported probability models for the data are lacking.