According to the IFCC, to determine the population-based reference interval (RI) of a test, 120 reference individuals are required. However, for some age groups such as newborns and preterm babies, it is difficult to obtain enough reference individuals. In this study, we consider both parametric and nonparametric bootstrap methods for estimating RIs and the associated confidence intervals (CIs)...

This paper introduces a bootstrap procedure, accounting for data dependence and parameter estimation error, which facillitates the construction of parametric speci ̄cation tests of di®usion processes. The bootstrap method hinges on a twofold extension of the Politis and Romano (1994) stationary bootstrap. First we provide an empirical process version of this bootstrap, and second, we account for...

In an attempt to free bootstrap theory from the shackles of asymptotic considerations, this paper studies the possibility of justifying, or validating, the bootstrap, not by letting the sample size tend to infinity, but by considering the sequence of bootstrap P values obtained by iterating the bootstrap. The main idea of the paper is that, if this sequence converges to a random variable that f...

We generalize and improve results of Andrews, Gravner, Holroyd, Liggett, and Romik on metastability thresholds for generalized two-dimensional bootstrap percolation models, and answer several of their open problems and conjectures. Specifically, we prove slow convergence and localization bounds for Holroyd, Liggett, and Romik’s k-percolation models, and in the process provide a unified and impr...

We introduce canonical correlation forests (CCFs), a new decision tree ensemble method for classification. Individual canonical correlation trees are binary decision trees with hyperplane splits based on canonical correlation components. Unlike axisaligned alternatives, the decision surfaces of CCFs are not restricted to the coordinate system of the input features and therefore more naturally r...

Bagging forms a committee of classijiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, disjoint partitions result in betterperformance than bootstrap aggregates (bags). Many applications (e.g., protein struc...

10 Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A 11 simple alternative to bagging is to partition the data into disjoint subsets. Experiments with decision tree and neural 12 network classifiers on various datasets show that, given the same size partitions and bags, disjoint partitions result in 13 performance equivalent to, o...

The process capability indices are widely used by quality professionals as an estimate of process capability. The lower confidence limits of PCIs are difficult to be estimated by parametric methods for some non-normal distributed processes. The non-parametric but computer intensive Bootstrap techniques are utilized for these cases. The Percentile-t Bootstrap (PTB) method is used to estimate the...

Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments with decision tree and neural network classifiers on various datasets show that, given the same size partitions and bags, disjoint partitions result in performance equivalent to, or better tha...

• There is a vast literature on robust estimators, but in some situations it is still not easy to make inferences, such as confidence regions and hypothesis testing. This is mainly due to the following facts. On one hand, in most situations, it is difficult to derive the exact distribution of the estimator. On the other one, even if its asymptotic behaviour is known, in many cases, the converge...