Results 321 to 330 of about 22,191,063 (377)
Some of the next articles are maybe not open access.
Journal of the American Statistical Association, 1996
Abstract In a regression problem, typically there are p explanatory variables possibly related to a response variable, and we wish to select a subset of the p explanatory variables to fit a model between these variables and the response. A bootstrap variable/model selection procedure is to select the subset of variables by minimizing bootstrap ...
J. Shao
semanticscholar +2 more sources
Abstract In a regression problem, typically there are p explanatory variables possibly related to a response variable, and we wish to select a subset of the p explanatory variables to fit a model between these variables and the response. A bootstrap variable/model selection procedure is to select the subset of variables by minimizing bootstrap ...
J. Shao
semanticscholar +2 more sources
Bootstrapping Error Component Models
Computational Statistics, 2001The one-way error component model \(y_{it}=\alpha+x_{it}\beta+\mu_i+\varepsilon_{it}\), \(i=1,\dots,n\), \(t=1,\dots,T\), is considered, where \(y_{it}\) and \(x_{it}\) are observable variables, \(\mu_i\sim(0,\sigma_\mu^2)\), \(\varepsilon_{it}\sim(0,\sigma_\varepsilon^2)\), \(E\varepsilon_{it},\varepsilon_{js}=0\), \(E\mu_i\mu_j=0\) and \(E\mu_i ...
Andersson, Michael K., Karlsson, Sune
openaire +2 more sources
Model Search by Bootstrap “Bumping”
Journal of Computational and Graphical Statistics, 1999Abstract We propose a bootstrap-based method for enhancing a search through a space of models. The technique is well suited to complex, adaptively fitted models—it provides a convenient method for finding better local minima and for resistant fitting. Applications to regression, classification, and density estimation are described.
Robert Tibshirani, Keith Knight
openaire +1 more source
SETAR model selection-A bootstrap approach
Computational Statistics, 2005The authors propose a technique of order selection in the self-exciting threshold autoregressive (SETAR) time series models which is based on the bootstrap estimate of the true prediction power of the model. This approach is compared with some versions of Akaike's information criteria (AIC) via simulations.
OHRVIK J, SCHOIER, GABRIELLA
openaire +3 more sources
BOOTSTRAPPING ECONOMETRIC MODELS [PDF]
, 2007 The bootstrap is a statistical technique used more and more widely in econometrics. While it is capable of yielding very reliable inference, some precautions should be taken in order to ensure this. Two "Golden Rules" are formulated that, if observed, help to obtain the best the bootstrap can offer.
openaire +1 more source
Bootstrapping time series models
Econometric Reviews, 1996This paper surveys recent development in bootstrap methods and the modifications needed for their applicability in time series models. The paper discusses some guidelines for empirical researchers in econometric analysis of time series. Different sampling schemes for bootstrap data generation and different forms of bootstrap test statistics are ...
G. S. Hongyi Li, null Maddala
openaire +1 more source
A bootstrapped metafrontier model
Applied Economics Letters, 2010The major aim of this article is to apply the bootstrapping methodology to the estimation of the metafrotnier model. The article has two parts. The first part deals with the technical details of the metafrontier model, and the second presents the application of the model using cross-sectional input/output data on health care foodservice operations. The
Assaf, Albert, Matawie, Kenan M. (R8147)
openaire +2 more sources
2017
AbstractBootstrap model selection is proposed for the difficult problem of selecting important factors in non-orthogonal linear models when the number of factors, P, is large. In the method, the full model is first fitted to the original data. Then B parametric bootstrap samples are drawn from the fitted model, and the full model fitted to each.
openaire +1 more source
AbstractBootstrap model selection is proposed for the difficult problem of selecting important factors in non-orthogonal linear models when the number of factors, P, is large. In the method, the full model is first fitted to the original data. Then B parametric bootstrap samples are drawn from the fitted model, and the full model fitted to each.
openaire +1 more source
Bootstrap-After-Bootstrap Prediction Intervals for Autoregressive Models
Journal of Business & Economic Statistics, 2001The use of the Bonferroni prediction interval based on the bootstrap-after-bootstrap is proposed for autoregressive (AR) models. Monte Carlo simulations are conducted using a number of AR models including stationary, unit-root, and near-unit-root processes.
openaire +1 more source