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Related Concept Videos

Ranks01:02

Ranks

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Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Relative Frequency Histogram01:14

Relative Frequency Histogram

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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On rank distribution classifiers for high-dimensional data.

Olusola Samuel Makinde1

  • 1Department of Statistics, Federal University of Technology, Akure, Nigeria.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

This study introduces a new nonparametric classification method for high-dimensional and functional data. The rank-based approach demonstrates effective performance compared to existing classifiers.

Keywords:
60E0562H1062H30Distribution functionhigh-dimensional datarank distribution classifierspatial outlyingness function

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Spatial sign and rank-based methods are increasingly studied for high-dimensional data.
  • Existing methods often require dimensionalities smaller than sample sizes.

Purpose of the Study:

  • To develop and evaluate a novel nonparametric classification method for high-dimensional and functional data.
  • To extend rank-based classification techniques to handle complex data structures.

Main Methods:

  • A classification method is proposed based on the distribution of rank functions.
  • The approach is fully nonparametric, requiring no distributional assumptions.
  • The method is extended to accommodate functional data analysis.

Main Results:

  • The classification method's performance was evaluated using simulated and real datasets.
  • Comparisons were made against several other established classification algorithms.
  • The proposed method showed competitive or superior performance in tested scenarios.

Conclusions:

  • The developed rank-based classification method is effective for high-dimensional and functional data.
  • The nonparametric nature of the method enhances its applicability across diverse datasets.
  • Open-source R code is provided to facilitate adoption and further research.