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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
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What is Natural Selection?01:32

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Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Updated: Feb 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Learning zero-cost portfolio selection with pattern matching.

Fayyaaz Loonat1, Tim Gebbie1,2

  • 1School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, WITS 2050, South Africa.

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|September 26, 2018
PubMed
Summary
This summary is machine-generated.

This study extends adversarial expert learning for zero-cost portfolio selection using mutual fund theorems on Johannesburg Stock Exchange data. Findings suggest exploitable, persistent patterns in financial time-series, though profitability remains challenging due to costs.

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Area of Science:

  • Quantitative Finance
  • Machine Learning in Finance
  • Algorithmic Trading

Background:

  • The study builds upon adversarial expert-based learning approaches.
  • It addresses the challenge of zero-cost portfolio selection.
  • Mutual fund separation theorems provide a theoretical basis for quadratic approximations.

Purpose of the Study:

  • To replicate and extend adversarial expert learning for zero-cost portfolio selection.
  • To apply and test these algorithms on real-world financial data from the Johannesburg Stock Exchange (JSE).
  • To investigate the potential for systematic exploitation of financial time-series patterns.

Main Methods:

  • Implementation of an adversarial expert-based learning algorithm with quadratic approximation derived from mutual fund separation theorems.
  • Application to daily and 5-minute intraday Open-High-Low-Close data from the JSE.
  • Parameter selection for experts using a nearest-neighbor search algorithm for pattern matching.

Main Results:

  • Demonstrated a speed advantage using an analytic solution of mutual fund separation theorems.
  • Identified persistent and systematically exploitable patterns in JSE financial time-series.
  • Strategies were found to be on the boundary of profitability for intraday quantitative trading.

Conclusions:

  • While direct profitable implementation is not suggested due to structural or cost reasons, the findings indicate the existence of exploitable patterns in financial time-series.
  • The study validates the potential of adversarial expert learning in financial applications.
  • Further research may explore methods to overcome implementation barriers for profitability.