Linear vs. Quantile Regression

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  • Опубликовано: 23 ноя 2024

Комментарии • 7

  • @Herbal_Rōnin
    @Herbal_Rōnin 9 месяцев назад +1

    Super helpful explanation Meerkat, thank you!

  • @tankisolefeta5798
    @tankisolefeta5798 28 дней назад

    Could you provide an example of how to run Recentred Influence functions in Python to analyze unconditional partial effects on quantiles within a regression analysis framework (i.e., unconditional quantile regressions)?

  • @Data_Scientist99
    @Data_Scientist99 11 месяцев назад +2

    Thank you sir

  • @maburwanemokoena7117
    @maburwanemokoena7117 3 месяца назад

    My question is why is it necessary though? The prediction from an OLS give you the conditional mean of the normal distribution, we also already have a constant variance, from there we can then calculate any quantile we need.

    • @MeerkatStatistics
      @MeerkatStatistics  3 месяца назад +1

      Good question. If your distribution is known or assumed (e.g. Normal) you are right. OLS doesn't have to assume the distribution. If you have an unknown distribution, the first 2 moments won't give you the quantiles. Also, you might want to fit the quantiles directly because you don't trust the normality assumption.

    • @maburwanemokoena7117
      @maburwanemokoena7117 3 месяца назад

      @@MeerkatStatistics Fantastic!

  • @MohammedHAzeez
    @MohammedHAzeez 8 месяцев назад

    🙏🙏🙏 THAX