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Related Concept Videos

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

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Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques.

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  • 1Department of Psychology, Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health, and Guangdong Provincial Key Laboratory of Brain Function and Disease, Sun Yat-sen University, Guangzhou, Guangdong, China.

The British Journal of Mathematical and Statistical Psychology
|September 13, 2017
PubMed
Summary
This summary is machine-generated.

The surrogate data method with percentile intervals is the best approach for testing autocorrelation and partial autocorrelation in time series data, outperforming traditional asymptotic methods in simulations.

Keywords:
R package pautocorrnonnormalitytests of autocorrelationstests of partial autocorrelationsthe surrogate data methodvectorized moving block bootstrap

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Area of Science:

  • Statistics
  • Time Series Analysis

Background:

  • Autocorrelation and partial autocorrelation are key for time series model identification.
  • Asymptotic methods for testing these can be unreliable in finite samples and with non-normal data.

Purpose of the Study:

  • Compare the performance of asymptotic methods against resampling techniques for autocorrelation and partial autocorrelation testing.
  • Evaluate interval construction methods including percentile and bias-corrected and accelerated (BCa) methods.

Main Methods:

  • Monte Carlo simulation study.
  • Real data example analysis.
  • Comparison of moving block bootstrap and surrogate data methods with percentile and BCa intervals.

Main Results:

  • The surrogate data method with percentile intervals demonstrated superior performance compared to other evaluated methods.
  • Asymptotic methods showed limitations in finite sample performance and robustness to non-normality.

Conclusions:

  • Resampling techniques, particularly the surrogate data method with percentile intervals, offer a more reliable approach for autocorrelation and partial autocorrelation testing.
  • The R package pautocorr facilitates the practical application of these advanced statistical tests.