I have a question about interpreting and using the bias corrected confidence intervals for logistic regression as produced by SPSS. I understand the rationale for using bootstrapping, but want confirmation that the BCa confidence intervals produced by the bootstrapping cannot be used a...
RIs were estimated using a bias-corrected bootstrapping approach. Electrocardiographic RIs were heart rate: 103 鈥 132 (beats/minute); P: 0.02 - 0.04, T: 0.03 - 0.04 waves durations (seconds); P: 0.13 - 0.23, R: 1.19 - 1.51, T: 0.20 - 0.27 waves amplitudes (mV...
Vector autoregressionIt is well documented that the small-sample accuracy of asymptotic and bootstrap approximations to the pointwise distribution of VAR ... L Kilian,YJ Kim - 《Cepr Discussion Papers》 被引量: 20发表: 2009年 Bootstrapping impulse responses in VAR analyses bootstrapvector autoregre...
bootstrappinginterval forecastingtime seriesThis paper examines small sample properties of alternative bias-corrected bootstrap prediction regions for the vector autoregressive (VAR) model. Bias-corrected bootstrap prediction regions are constructed by combining bias-correction of VAR parameter estimators with...
Half-Life Estimation based on the Bias-Corrected Bootstrap: A Highest Density Region Approach - Kim, Silvapulle, et al. - 2007Kim, J. H., Silvapulle, P. and Hyndman, R. (2007). Half-Life Estimation based on the Bias-Corrected Bootstrap: A Highest Density Region Approach, Computational...
BootstrappingUnit rootThe parameter estimators of autoregressive (AR) models are biased in small samples, and these biases can adversely affect their forecast accuracy. The purpose of this paper is to evaluate the effect of bias-correction for AR parameter estimators on forecast accuracy. The bias...
BootstrappingIn efficiency studies, inputs and outputs are often noisy measures, especially when education data is used. This article complements the conditional efficiency model by correcting for bias within conditional draws, using themout ofnbootstrap procedure. With a unique panel dataset, we ...