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… Expand
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. Expand
Adaptive bootstrap tests and its competitors in the c-sample scale problem
This paper deals with a study of different types of tests for the two-sided c-sample scale problem. We consider the classical parametric test of Bartlett [M.S. Bartlett, Properties of sufficiency andExpand
The problem of constructing confidence intervals for the ratio of variance components in unbalanced random one-way model is to investigate. In this respect, various different exact methods, which areExpand
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 theExpand
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 kernelExpand
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. Expand
Robust Bayesian clustering
This work formalizes the Bayesian Student-t mixture model as a latent variable model in a different way from Svensén and Bishop, and expects that the lower bound on the log-evidence is tighter and the model complexity can be inferred with a higher confidence. Expand
A parameterized approach to modeling and forecasting mortality
A new method is proposed of constructing mortality forecasts. This parameterized approach utilizes Generalized Linear Models (GLMs), based on heteroscedastic Poisson (non-additive) error structures,Expand
Regularized mixture discriminant analysis
The experimental results show that the proposed Gaussian mixture model of the class-conditional densities for plug-in Bayes classification has the potential to produce parameterizations of the covariance matrices of the GMMs which are better than the parameterizations used in other methods. Expand
Computer Intensive Methods for Evaluating Latent Class Model Fit
Assessing Model fit in Latent Class analysis when Asymptotics do not hold is assessed, using goodness-of-fit statistics from the latent class (LC) analysis. Expand