Resampling

About: Resampling is a research topic. Over the lifetime, 5428 publications have been published within this topic receiving 242291 citations.

Computational time-lapse video

Abstract: We present methods for generating novel time-lapse videos that address the inherent sampling issues that arise with traditional photographic techniques. Starting with video-rate footage as input, our post-process downsamples the source material into a time-lapse video and provides user controls for retaining, removing, and resampling events. We employ two techniques for selecting and combining source frames to form the output. First, we present a non-uniform sampling method, based on dynamic programming, which optimizes the sampling of the input video to match the user's desired duration and visual objectives. We present multiple error metrics for this optimization, each resulting in different sampling characteristics. To complement the non-uniform sampling, we present the virtual shutter, a non-linear filtering technique that synthetically extends the exposure time of time-lapse frames.

Bootstrap-based bias correction and inference for dynamic panels with fixed effects

Abstract: In this article, we describe a new command, xtbcfe, that performs the iterative bootstrap-based bias correction for the fixed-effects estimator in dynamic panels proposed by Everaert and Pozzi (2007, Journal of Economic Dynamics and Control 31: 1160–1184). We first simplify the core of their algorithm by using the invariance principle and subsequently extend it to allow for unbalanced and higher order dynamic panels. We implement various bootstrap error resampling schemes to account for general heteroskedasticity and contemporaneous cross-sectional dependence. Inference can be performed using a bootstrapped variance–covariance matrix or percentile intervals. Monte Carlo simulations show that the simplification of the original algorithm results in a further bias reduction for very small T. The Monte Carlo results also support the bootstrap-based bias correction in higher order dynamic panels and panels with cross-sectional dependence. We illustrate the command with an empirical example estimating a dynamic labor–demand function. (Less)

Position weight matrix, gibbs sampler, and the associated significance tests in motif characterization and prediction.

Abstract: Position weight matrix (PWM) is not only one of the most widely used bioinformatic methods, but also a key component in more advanced computational algorithms (e.g., Gibbs sampler) for characterizing and discovering motifs in nucleotide or amino acid sequences. However, few generally applicable statistical tests are available for evaluating the significance of site patterns, PWM, and PWM scores (PWMS) of putative motifs. Statistical significance tests of the PWM output, that is, site-specific frequencies, PWM itself, and PWMS, are in disparate sources and have never been collected in a single paper, with the consequence that many implementations of PWM do not include any significance test. Here I review PWM-based methods used in motif characterization and prediction (including a detailed illustration of the Gibbs sampler for de novo motif discovery), present statistical and probabilistic rationales behind statistical significance tests relevant to PWM, and illustrate their application with real data. The multiple comparison problem associated with the test of site-specific frequencies is best handled by false discovery rate methods. The test of PWM, due to the use of pseudocounts, is best done by resampling methods. The test of individual PWMS for each sequence segment should be based on the extreme value distribution.

A Permutation Test for the Regression Kink Design

Abstract: The regression kink (RK) design is an increasingly popular empirical method for estimating causal effects of policies, such as the effect of unemployment benefits on unemployment duration. Using si...