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Showing 1 to 3 of 3 for “"Error Assumptions"”.

  1. Quantile Regression Approach For Analyzing Gene Expression Data

    … mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical to analyze gene data. In this research, we use linear quantile mixed model to analyze gene expression data. One of the notable advantages for quantile re- gression is that models …

    regina Repository record for Quantile Regression Approach For Analyzing Gene Expression Data (opens in a new tab)

  2. A New Nonparametric Procedure for the k-sample Problem

    The k-sample data setting is one of the most common data settings used today. The null hypothesis that is most generally of interest for these methods is that the k-samples have the same location. Currently there are several procedures available for the individual who has data of this type. The …

    vt Repository record for A New Nonparametric Procedure for the k-sample Problem (opens in a new tab)

  3. Fractional Polynomial Response Surfaces and Tests for "Bumpature"

    … adequate fit, there are often problems with error assumptions and, with quadratic models, perhaps a spurious minimum or maximum in the fitted surface. With limited data, nonparametric regression is not a realistic option; non-linear models may provide a better fit than polynomials, but in the …

    south-carolina Repository record for Fractional Polynomial Response Surfaces and Tests for "Bumpature" (opens in a new tab)