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  2. Matrix completion - Wikipedia

    en.wikipedia.org/wiki/Matrix_completion

    Alternating minimization represents a widely applicable and empirically successful approach for finding low-rank matrices that best fit the given data. For example, for the problem of low-rank matrix completion, this method is believed to be one of the most accurate and efficient, and formed a major component of the winning entry in the Netflix ...

  3. List of analyses of categorical data - Wikipedia

    en.wikipedia.org/wiki/List_of_analyses_of...

    This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables.

  4. NaN - Wikipedia

    en.wikipedia.org/wiki/NaN

    In computing, NaN (/ n æ n /), standing for Not a Number, is a particular value of a numeric data type (often a floating-point number) which is undefined as a number, such as the result of 0/0.

  5. Statistical data type - Wikipedia

    en.wikipedia.org/wiki/Statistical_data_type

    The concept of data type is similar to the concept of level of measurement, but more specific. For example, count data requires a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale).

  6. Categorical variable - Wikipedia

    en.wikipedia.org/wiki/Categorical_variable

    Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. More specifically, categorical data may derive from observations made of qualitative data that are summarised as counts or cross tabulations , or from observations of quantitative data ...

  7. Missing data - Wikipedia

    en.wikipedia.org/wiki/Missing_data

    Generally speaking, there are three main approaches to handle missing data: (1) Imputation—where values are filled in the place of missing data, (2) omission—where samples with invalid data are discarded from further analysis and (3) analysis—by directly applying methods unaffected by the missing values. One systematic review addressing ...

  8. Dummy variable (statistics) - Wikipedia

    en.wikipedia.org/wiki/Dummy_variable_(statistics)

    Dummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels, such as education level or occupation. In this case, multiple dummy variables would be created to represent each level of the variable, and only one dummy variable would take on a value of 1 for each observation.

  9. Categorical data analysis - Wikipedia

    en.wikipedia.org/?title=Categorical_data...

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