In recent years, many exciting developments have taken place in bootstrapping time series.
Advances have proceeded along a number of distinct paths. Some authors have focused on …

The bootstrap is a method for estimating the distribution of an estimator or test statistic by
resampling one's data or a model estimated from the data. Under conditions that hold in a …

Presence‐only records may provide data on the distributions of rare species, but commonly
suffer from large, unknown biases due to their typically haphazard collection schemes …

This book was written to summarize and describe the state of the art of statistical methods
developed to address questions of estimation and inference for dynamic treatment regimes …

Data snooping occurs when a given set of data is used more than once for purposes of
inference or model selection. When such data reuse occurs, there is always the possibility …

This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …

This is a book on bootstrap and related resampling methods for temporal and spatial data
exhibiting various forms of dependence. Like the resam pling methods for independent data …

This is the second edition of Climate Time Series Analysis which was first published in 2010.
In this digital age, a second edition means not that the first edition of a book has been sold …

This paper develops a simple technique that controls for “false discoveries,” or mutual funds
that exhibit significant alphas by luck alone. Our approach precisely separates funds into (1) …

We review the different block bootstrap methods for time series, and present them in a
unified framework. We then revisit a recent result of Lahiri [Lahiri, SN (1999b). Theoretical …