Conditional heteroskedasticity can be exploited to identify the structural vector autoregressions (SVAR) but the implications for inference on structural impulse responses have not been investigated in detail yet. We consider the conditionally heteroskedastic SVAR-GARCH model and propose a bootstrap-based inference procedure on structural impulse responses. We compare the finite-sample properti...

Bagging is a method of obtaining more robust predictions when the model class under consideration is unstable with respect to the data, i.e., small changes in the data can cause the predicted values to change significantly. In this paper, we introduce a Bayesian version of bagging based on the Bayesian bootstrap. The Bayesian bootstrap resolves a theoretical problem with ordinary bagging and of...

BACKGROUND This work has investigated under what conditions confidence intervals around the differences in mean costs from a cluster RCT are suitable for estimation using a commonly used cluster-adjusted bootstrap in preference to methods that utilise the Huber-White robust estimator of variance. The bootstrap's main advantage is in dealing with skewed data, which often characterise patient cos...

This paper introduces the confidence interval estimate for measuring the bullwhip effect, which has been observed across most industries. Calculating a confidence interval usually needs the assumption about the underlying distribution. Bootstrapping is a non-parametric, but computer intensive, estimation method. In this paper, a simulation study on the behavior of the 95% bootstrap confidence i...

We study several methods of constructing confidence sets for the coefficient of the single right-hand-side endogenous variable in a linear equation with weak instruments. Two of these are based on conditional likelihood ratio (CLR) tests, and the others are based on inverting t statistics or the bootstrap P values associated with them. We propose a new method for constructing bootstrap confiden...

The multistate life table (MSLT) model is an important demographic method to document life cycle processes. In this paper, we present the SPACE (Stochastic Population Analysis for Complex Events) program to estimate MSLT functions and their sampling variability. It has several advantages over other programs, including the use of micro-simulation and the bootstrap method to estimate the variance...

Bootstrap techniques are widely used today in many other fields such as economics, Business Administration, Physics, Engineering, Chemistry, Meteorological, Biological Sciences and Medicine. This paper is concerned with the estimation of linear regression model parameters in the presence of heteroscedasticity using wild bootstrap approaches of Wu and Liu. The empirical evidence has shown that t...

notion the theoretical distribution on the random variable ) is not available. All we have ICOTS8 (2010) Invited Paper Engel International Association of Statistical Education (IASE) www.stat.auckland.ac.nz/~iase/ to rely on are the data at hand, i.e. the sample or recapture of size n. These data–if drawn by some random mechanism–may well be taken as a good representation of the total fish popu...

We propose a scalable method of assessing the quality of machine learning algorithms over sampled time-series data. While bootstrap provides a simple and powerful means of estimating accuracy, its application to large time-series data still suffers from scalability issues. As an alternative we introduce BOOT-TS, a scalable extension of bootstrap for time-series which utilizes the recent advance...

This paper describes a new method, COMBI-BOOTSTRAP, to exploit existing taggers and lexical resources for the annotation of corpora with new tagsets. COMBI-BOOTSTRAP uses existing resources as features for a second level machine learning module, that is trained to make the mapping to the new tagset on a very small sample of annotated corpus material. Experiments show that COMBI-BOOTSTRAP: i) ca...