Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Testing for correlation between two time series using a parametric bootstrap.

ABSTRACT: We study the problem of determining if two time series are correlated in the mean and variance. Several test statistics, originally designed for determining the correlation between two mean processes or goodness-of-fit testing, are explored and formally introduced for determining cross-correlation in variance. Simulations demonstrate the theoretical asymptotic distribution can be ineffective in finite samples. Parametric bootstrapping is shown to be an effective tool in such an enterprise. A large simulation study is provided demonstrating the efficacy of the bootstrapping method. Lastly, an empirical example explores a correlation between the Standard & Poor's 500 index and the Euro/US dollar exchange rate while also demonstrating a level of robustness for the proposed method.

SUBMITTER: Sun Z

PROVIDER: S-EPMC9041796 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Testing for correlation between two time series using a parametric bootstrap.

Sun Zequn Z Fisher Thomas J TJ

Journal of applied statistics 20200623 11

We study the problem of determining if two time series are correlated in the mean and variance. Several test statistics, originally designed for determining the correlation between two mean processes or goodness-of-fit testing, are explored and formally introduced for determining cross-correlation in variance. Simulations demonstrate the theoretical asymptotic distribution can be ineffective in finite samples. Parametric bootstrapping is shown to be an effective tool in such an enterprise. A lar ...[more]

PMID: 35706437

Similar Datasets

Noise Is Not Error: Detecting Parametric Heterogeneity Between Epidemiologic Time Series.

Project description:Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa.

| S-EPMC6052138 | biostudies-literature

Adaptive evolution of chloroplast genome structure inferred using a parametric bootstrap approach.

Project description:BackgroundGenome rearrangements influence gene order and configuration of gene clusters in all genomes. Most land plant chloroplast DNAs (cpDNAs) share a highly conserved gene content and with notable exceptions, a largely co-linear gene order. Conserved gene orders may reflect a slow intrinsic rate of neutral chromosomal rearrangements, or selective constraint. It is unknown to what extent observed changes in gene order are random or adaptive. We investigate the influence of natural selection on gene order in association with increased rate of chromosomal rearrangement. We use a novel parametric bootstrap approach to test if directional selection is responsible for the clustering of functionally related genes observed in the highly rearranged chloroplast genome of the unicellular green alga Chlamydomonas reinhardtii, relative to ancestral chloroplast genomes.ResultsAncestral gene orders were inferred and then subjected to simulated rearrangement events under the random breakage model with varying ratios of inversions and transpositions. We found that adjacent chloroplast genes in C. reinhardtii were located on the same strand much more frequently than in simulated genomes that were generated under a random rearrangement processes (increased sidedness; p < 0.0001). In addition, functionally related genes were found to be more clustered than those evolved under random rearrangements (p < 0.0001). We report evidence of co-transcription of neighboring genes, which may be responsible for the observed gene clusters in C. reinhardtii cpDNA.ConclusionSimulations and experimental evidence suggest that both selective maintenance and directional selection for gene clusters are determinants of chloroplast gene order.

| S-EPMC1421436 | biostudies-literature

Periodically correlated time series and the Variable Bandpass Periodic Block Bootstrap.

Project description:This research introduces a novel approach to resampling periodically correlated time series using bandpass filters for frequency separation called the Variable Bandpass Periodic Block Bootstrap and then examines the significant advantages of this new method. While bootstrapping allows estimation of a statistic's sampling distribution by resampling the original data with replacement, and block bootstrapping is a model-free resampling strategy for correlated time series data, both fail to preserve correlations in periodically correlated time series. Existing extensions of the block bootstrap help preserve the correlation structures of periodically correlated processes but suffer from flaws and inefficiencies. Analyses of time series data containing cyclic, seasonal, or periodically correlated principal components often seen in annual, daily, or other cyclostationary processes benefit from separating these components. The Variable Bandpass Periodic Block Bootstrap uses bandpass filters to separate a periodically correlated component from interference such as noise at other uncorrelated frequencies. A simulation study is presented, demonstrating near universal improvements obtained from the Variable Bandpass Periodic Block Bootstrap when compared with prior block bootstrapping methods for periodically correlated time series.

| S-EPMC11407666 | biostudies-literature

Robust bootstrap prediction intervals for univariate and multivariate autoregressive time series models.

Project description:The bootstrap procedure has emerged as a general framework to construct prediction intervals for future observations in autoregressive time series models. Such models with outlying data points are standard in real data applications, especially in the field of econometrics. These outlying data points tend to produce high forecast errors, which reduce the forecasting performances of the existing bootstrap prediction intervals calculated based on non-robust estimators. In the univariate and multivariate autoregressive time series, we propose a robust bootstrap algorithm for constructing prediction intervals and forecast regions. The proposed procedure is based on the weighted likelihood estimates and weighted residuals. Its finite sample properties are examined via a series of Monte Carlo studies and two empirical data examples.

| S-EPMC9042141 | biostudies-literature

A Non-parametric Approach to the Overall Estimate of Cognitive Load Using NIRS Time Series.

Project description:We present a non-parametric approach to prediction of the n-back n ∈ {1, 2} task as a proxy measure of mental workload using Near Infrared Spectroscopy (NIRS) data. In particular, we focus on measuring the mental workload through hemodynamic responses in the brain induced by these tasks, thereby realizing the potential that they can offer for their detection in real world scenarios (e.g., difficulty of a conversation). Our approach takes advantage of intrinsic linearity that is inherent in the components of the NIRS time series to adopt a one-step regression strategy. We demonstrate the correctness of our approach through its mathematical analysis. Furthermore, we study the performance of our model in an inter-subject setting in contrast with state-of-the-art techniques in the literature to show a significant improvement on prediction of these tasks (82.50 and 86.40% for female and male participants, respectively). Moreover, our empirical analysis suggest a gender difference effect on the performance of the classifiers (with male data exhibiting a higher non-linearity) along with the left-lateralized activation in both genders with higher specificity in females.

| S-EPMC5290219 | biostudies-literature

A parametric bootstrap control chart for Lindley Geometric percentiles.

Project description:Control charts are vital for quality control and process monitoring, helping businesses identify variations in production. Traditional control charts, like Shewhart charts, may not work well for skewed distributions, such as the Lindley geometric distribution (LG). This study introduces a new control chart that uses parametric bootstrap techniques to monitor percentiles of the LG distribution, providing a more effective quality control method. The LG distribution is useful for modeling material strength and failures, especially in structural design, where lower percentiles indicate reduced tensile strength. We conducted extensive simulations to assess the proposed control chart's effectiveness, considering various distribution parameters, percentile values, Type I error rates, and sample sizes. Our findings highlight how subgroup size, percentiles, and significance levels affect control limits, stressing the need for careful parameter selection in monitoring processes. The results show that the new control chart is highly sensitive to changes in LG distribution parameters and performs consistently across different percentiles. This suggests its practical relevance and robustness for industrial applications in quality control. Future research should explore its performance in real-world production settings to confirm its efficiency and reliability.

| S-EPMC11801588 | biostudies-literature

An Iterative Parametric Bootstrap Approach to Evaluating Rater Fit.

Project description:When analysts evaluate performance assessments, they often use modern measurement theory models to identify raters who frequently give ratings that are different from what would be expected, given the quality of the performance. To detect problematic scoring patterns, two rater fit statistics, the infit and outfit mean square error (MSE) statistics are routinely used. However, the interpretation of these statistics is not straightforward. A common practice is that researchers employ established rule-of-thumb critical values to interpret infit and outfit MSE statistics. Unfortunately, prior studies have shown that these rule-of-thumb values may not be appropriate in many empirical situations. Parametric bootstrapped critical values for infit and outfit MSE statistics provide a promising alternative approach to identifying item and person misfit in item response theory (IRT) analyses. However, researchers have not examined the performance of this approach for detecting rater misfit. In this study, we illustrate a bootstrap procedure that researchers can use to identify critical values for infit and outfit MSE statistics, and we used a simulation study to assess the false-positive and true-positive rates of these two statistics. We observed that the false-positive rates were highly inflated, and the true-positive rates were relatively low. Thus, we proposed an iterative parametric bootstrap procedure to overcome these limitations. The results indicated that using the iterative procedure to establish 95% critical values of infit and outfit MSE statistics had better-controlled false-positive rates and higher true-positive rates compared to using traditional parametric bootstrap procedure and rule-of-thumb critical values.

| S-EPMC8361373 | biostudies-literature

Inference of Selection from Genetic Time Series Using Various Parametric Approximations to the Wright-Fisher Model.

Project description:Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markov chain. Using the Wright-Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and their estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta with spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluated this model for population sizes not accessible to the Wright-Fisher model and illustrated its performance on a dataset of two divergently selected chicken populations.

| S-EPMC6893182 | biostudies-literature

Testing for the Presence of Correlation Changes in a Multivariate Time Series: A Permutation Based Approach.

Project description:Detecting abrupt correlation changes in multivariate time series is crucial in many application fields such as signal processing, functional neuroimaging, climate studies, and financial analysis. To detect such changes, several promising correlation change tests exist, but they may suffer from severe loss of power when there is actually more than one change point underlying the data. To deal with this drawback, we propose a permutation based significance test for Kernel Change Point (KCP) detection on the running correlations. Given a requested number of change points K, KCP divides the time series into K + 1 phases by minimizing the within-phase variance. The new permutation test looks at how the average within-phase variance decreases when K increases and compares this to the results for permuted data. The results of an extensive simulation study and applications to several real data sets show that, depending on the setting, the new test performs either at par or better than the state-of-the art significance tests for detecting the presence of correlation changes, implying that its use can be generally recommended.

| S-EPMC5768740 | biostudies-literature

Faster family-wise error control for neuroimaging with a parametric bootstrap.

Project description:In neuroimaging, hundreds to hundreds of thousands of tests are performed across a set of brain regions or all locations in an image. Recent studies have shown that the most common family-wise error (FWE) controlling procedures in imaging, which rely on classical mathematical inequalities or Gaussian random field theory, yield FWE rates (FWER) that are far from the nominal level. Depending on the approach used, the FWER can be exceedingly small or grossly inflated. Given the widespread use of neuroimaging as a tool for understanding neurological and psychiatric disorders, it is imperative that reliable multiple testing procedures are available. To our knowledge, only permutation joint testing procedures have been shown to reliably control the FWER at the nominal level. However, these procedures are computationally intensive due to the increasingly available large sample sizes and dimensionality of the images, and analyses can take days to complete. Here, we develop a parametric bootstrap joint testing procedure. The parametric bootstrap procedure works directly with the test statistics, which leads to much faster estimation of adjusted p-values than resampling-based procedures while reliably controlling the FWER in sample sizes available in many neuroimaging studies. We demonstrate that the procedure controls the FWER in finite samples using simulations, and present region- and voxel-wise analyses to test for sex differences in developmental trajectories of cerebral blood flow.

| S-EPMC6232833 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data