Journal ArticleDOI
The Dependent Wild Bootstrap
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TLDR
In this article, a new resampling procedure, the dependent wild bootstrap, was proposed for stationary time series, which can be easily extended to irregularly spaced time series with no implementational difficulty.Abstract:
We propose a new resampling procedure, the dependent wild bootstrap, for stationary time series. As a natural extension of the traditional wild bootstrap to time series setting, the dependent wild bootstrap offers a viable alternative to the existing block-based bootstrap methods, whose properties have been extensively studied over the last two decades. Unlike all of the block-based bootstrap methods, the dependent wild bootstrap can be easily extended to irregularly spaced time series with no implementational difficulty. Furthermore, it preserves the favorable bias and mean squared error property of the tapered block bootstrap, which is the state-of-the-art block-based method in terms of asymptotic accuracy of variance estimation and distribution approximation. The consistency of the dependent wild bootstrap in distribution approximation is established under the framework of the smooth function model. In addition, we obtain the bias and variance expansions of the dependent wild bootstrap variance estimat...read more
Citations
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Journal ArticleDOI
Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
TL;DR: This work presents a technique for the bootstrap aggregation (bagging) of exponential smoothing methods, which results in significant improvements in the forecasts, and demonstrates that it outperforms the originalonential smoothing models consistently.
Proceedings Article
A kernel test of goodness of fit
TL;DR: A nonparametric statistical test for goodness-of-fit is proposed: given a set of samples, the test determines how likely it is that these were generated from a target density function, taking the form of a V-statistic in terms of the log gradients of the target density and the kernel.
Book
An Introduction to Bootstrap Methods with Applications to R
TL;DR: This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics and serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of boot strap methods.
Journal ArticleDOI
Bootstrap methods for dependent data: A review
TL;DR: In this article, a review on a variety of bootstrap methods for dependent data is given, focusing on general principles which should be taken into account when selecting a particular bootstrap procedure in order to approximate the (properly standardized) distribution of a statistic of interest.
References
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ReportDOI
A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix
Whitney K. Newey,Kenneth D. West +1 more
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
Journal Article
Spectral Analysis and Time Series
TL;DR: In this article, the authors introduce the concept of Stationary Random Processes and Spectral Analysis in the Time Domain and Frequency Domain, and present an analysis of Processes with Mixed Spectra.
Journal ArticleDOI
Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
TL;DR: Using these results, data-dependent automatic bandwidth/lag truncation parameters are introduced and asymptotically optimal kernel/weighting scheme and bandwidth/agreement parameters are obtained.
Book
Robust statistics: the approach based on influence functions
TL;DR: This paper presents a meta-modelling framework for estimating the values of Covariance Matrices and Multivariate Location using one-Dimensional and Multidimensional Estimators.
Book
Time Series: Data Analysis and Theory
TL;DR: This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical.