Joseph P. Romano

The Stationary Bootstrap

Abstract This article introduces a resampling procedure called the stationary bootstrap as a means of calculating standard errors of estimators and constructing confidence regions for parameters based on weakly dependent stationary observations. Previously, a technique based on resampling blocks of consecutive observations was introduced to construct confidence intervals for a parameter of the m-dimensional joint distribution of m consecutive observations, where m is fixed. This procedure has been generalized by constructing a “blocks of blocks” resampling scheme that yields asymptotically valid procedures even for a multivariate parameter of the whole (i.e., infinite-dimensional) joint distribution of the stationary sequence of observations. These methods share the construction of resampling blocks of observations to form a pseudo-time series, so that the statistic of interest may be recalculated based on the resampled data set. But in the context of applying this method to stationary data, it is natural...

Inference for Autocorrelations under Weak Assumptions

Abstract In this article we consider the large-sample behavior of estimates of autocorrelations and autoregressive moving average (ARMA) coefficients, as well as their distributions, under weak conditions. Specifically, the usual text book formulas for variances of these estimates are based on strong assumptions and should not be routinely applied without careful consideration. Such is the case when the time series follows an ARMA process with uncorrelated innovations that may not be assumed to be independent and identically distributed. As a specific case, it is well known that if the process is independent and identically distributed, then the sample autocorrelation estimates, scaled by the square root of the sample size, are asymptotically standard normal. This result is used extensively as a diagnostic check on the residuals of a fitted model, or as an initial test on the observed time series to determine whether further model fitting is warranted. In this article we show that this result can be quite...

Bootstrap technology and applications

Bootstrap resampling methods have emerged as powerful tools for constructing inferential procedures in modern statistical data analysis. Although these methods depend on the availability of fast, inexpensive computing, they offer the potential for highly accurate methods of inference. Moreover, they can even eliminate the need to impose a convenient statistical model that does not have a strong scientific basis. In this article, we review some bootstrap methods, emphasizing applications through illustrations with some real data. Special attention is given to regression, problems with dependent data, and choosing tuning parameters for optimal performance.

On the Behavior of Randomization Tests without a Group Invariance Assumption

Abstract Fishers randomization construction of hypothesis tests is a powerful tool to yield tests that are nonparametric in nature in that their level is exactly equal to the nominal level in finite samples over a wide range of distributional assumptions. For example, the usual permutation t test to test equality of means is valid without a normality assumption of the underlying populations. On the other hand, Fishers randomization construction is not applicable in this example unless the underlying populations differ only in location. In general, the basis for the randomization construction is invariance of the probability distribution of the data under a transformation group. It is the goal of this article to understand the robustness properties of randomization tests by studying their asymptotic validity in situations where the basis for their construction breaks down. Here, asymptotic validity refers to whether the probability of a Type I error tends asymptotically to the nominal level. In particula...

Subsampling for heteroskedastic time series

Abstract In this article, a general theory for the construction of confidence intervals or regions in the context of heteroskedastic-dependent data is presented. The basic idea is to approximate the sampling distribution of a statistic based on the values of the statistic computed over smaller subsets of the data. This method was first proposed by Politis and Romano (1994b) for stationary observations. We extend their results to heteroskedastic observations, and prove a general asymptotic validity result under minimal conditions. In contrast, the usual bootstrap and moving blocks bootstrap are typically valid only for asymptotically linear statistics and their justification requires a case-by-case analysis. Our general asymptotic results are applied to a regression setting with dependent heteroskedastic errors.

A Bootstrap Revival of Some Nonparametric Distance Tests

Abstract Several tests based on the empirical measure have been proposed to test independence of variables, goodness of fit, equality of distributions, rotational invariance, and so forth. These tests have excellent power properties, but critical values are difficult, if not impossible, to obtain. Furthermore, these tests usually assume that the data are real-valued with continuous distributions. Here, critical values are determined by bootstrapping and the resulting tests are shown to have the correct asymptotic level under minimal assumptions. For example, given data Xi = (X i,1, …, Xi,d ), i = 1, …, n, it may be desired to test independence of the d components. The proposed test compares the empirical measure and the product of its marginals by taking a supremum over an appropriate Vapnik-Cervonenkis class of sets. No assumptions are made on the probability distribution of the data or on the space in which it lives; indeed, some components may be discrete, some continuous, and others categorical. Simil...

Exact and asymptotically robust permutation tests

Given independent samples from P and Q, two-sample permutation tests allow one to construct exact level tests when the null hypothesis is P=Q. On the other hand, when comparing or testing particular parameters

On Subsampling Estimators with Unknown Rate of Convergence

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A more general central limit theorem for m-dependent random variables with unbounded m

of P and Q, such as their means or medians, permutation tests need not be level

On the uniform asymptotic validity of subsampling and the bootstrap

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