Because the stationary bootstrap resamples data blocks of random length, this method has been thought to have the largest asymptotic variance among block bootstraps Lahiri [Ann. Statist. 27 (1999) 386–404]. It is shown here that the variance of the stationary bootstrap surprisingly matches that of a block bootstrap based on nonrandom, nonoverlapping blocks. This argument translates the variance...

We propose a novel method for estimating population-level and subject-specific effects of covariates on the variability of functional data. We extend the functional principal components analysis framework by modeling the variance of principal component scores as a function of covariates and subject-specific random effects. In a setting where principal components are largely invariant across sub...

The one-way random-effect ANOVA model is presented, and two simulated datasets are analyzed. and discussed from three points of view: (1) The standard ANOVA table, F test, and method-of-moments estimates of variance components, which can lead to negative estimates. (2) Maximum likelihood estimates of variance components. (3) Bayesian probability intervals for variance components based on flat p...

Our previous results showed that while amblyopes can efficiently integrate visual signals, they are poor at segregating signals in noise. This could be either because integration detectors have broader bandwidths or because of a selective extrastriate segregation anomaly. One consequence of the former would be poorer variance discrimination. Using a two-alternative forced-choice paradigm, obser...

Because the stationary bootstrap resamples data blocks of random length, this method has been thought to have the largest asymptotic variance among block bootstraps (Lahiri, 1999, Ann. Statist.). It is shown here that the variance of the stationary bootstrap surprisingly matches that of a block bootstrap based on non-random, non-overlapping blocks. This argument translates the variance expansio...

Recently, Sargent and Shanthikumar [5] developed an interesting new variance reduction technique designed to exploit the stochastic structure associated with a cyclic regenerative process. Our purpose here is to study precise conditions under which the confidence intervals proposed in [5] are asymptotically valid. This analysis will provide us with the side benefit of obtaining an optimal way o...

In describing the two dietary patterns identified in this manuscript, the authors reported that the variation in food intake explained by these dietary patterns amounted to 84 % (paragraph 2 of Results, p4). This was the common variance shared by all foods entered into the dietary pattern analysis; whereas the total variance in food intakes explained by the patterns was 13 % (Western dietary pa...

In this work we study variance in the results of neural network training on a wide variety of configurations in automatic speech recognition. Although this variance itself is well known, this is, to the best of our knowledge, the first paper that performs an extensive empirical study on its effects in speech recognition. We view training as sampling from a distribution and show that these distr...

This study contributes to passive portfolio management by comparing four ways for asset allocation. Index investing, mean-variance optimization, equal-weighting and semi-variance optimization are compared as part of the investment strategy aimed at outperforming Dow Jones Industrial Average (DJIA). The best way allocate assets was portfolios looking minimum semi-variance. Yield spreads below pe...

Abstract In this paper we introduce a new criterion in order to measure the variance and covariance risks in financial markets. In an asset allocation framework with stochastic (co)variances, we consider the possibility to invest also in variance swaps, that are assets which span the volatility as well as the co-volatility risks. We provide explicit solutions for the portfolio optimization prob...