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Related Concept Videos

Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

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When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Wald-Wolfowitz Runs Test I01:17

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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
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Wald-Wolfowitz Runs Test II01:17

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
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Area Computation by the Alternative Coordinate Method01:24

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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Updated: Jun 5, 2025

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DART: Distance Assisted Recursive Testing.

Xuechan Li1, Anthony D Sung2, Jichun Xie1

  • 1Department of Biostatistics, Duke University, Durham, NC 27705, USA.

Journal of Machine Learning Research : JMLR
|December 13, 2024
PubMed
Summary
This summary is machine-generated.

We developed Distance Assisted Recursive Testing (DART), a novel framework that integrates artificial intelligence and statistics to enhance multiple testing power by utilizing hypothesis distance information. DART provides valid, robust, and powerful results, improving data analysis in complex scientific studies.

Keywords:
aggregation treeauxiliary informationfalse discovery ratehierarchical testingmultiple testing

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Area of Science:

  • Statistics
  • Artificial Intelligence
  • Bioinformatics

Background:

  • Multiple testing is crucial in data science but often overlooks inherent relationships between hypotheses.
  • Hypothesis distances can reveal co-null/co-alternative patterns, information that can improve statistical power.

Purpose of the Study:

  • To introduce a novel multiple testing framework, Distance Assisted Recursive Testing (DART), that leverages hypothesis distance information.
  • To enhance statistical power in hypothesis testing by integrating artificial intelligence and statistical modeling.

Main Methods:

  • DART employs a two-stage approach: AI models construct an aggregation tree reflecting distance information, followed by statistical models for testing and false discovery rate control.
  • The framework combines machine learning for tree construction and statistical methods for hypothesis testing.

Main Results:

  • Theoretical analysis and numerical experiments confirm DART's validity, robustness, and power.
  • DART successfully identified gut microbiota impacted by post-transplant care in an allogeneic stem cell transplantation clinical trial.

Conclusions:

  • DART offers a powerful and valid approach to multiple testing by incorporating hypothesis distance information.
  • The framework demonstrates significant potential for applications in clinical trials and other complex data analysis scenarios.