5/21/2023 0 Comments Lzip in pythonAny model which assumes homoscedastic data or homoscedastic residual errors is vulnerable to these issues. This problem is not restricted to OLSR models. This could mislead you into believing that certain parameter values are significant while they are actually not significant, and vice versa. ![]() This leads to a cascading series of problems: The standard errors of the model’s parameters become incorrect, causing their p-values to go wrong and the confidence intervals to be too narrow or too wide. ![]() If your data contains heteroscedastic variance, an OLSR model is likely to either underestimate or overestimate the variance in the population depending on what sort of variance it has seen in the training sample. It may be possible to construct a different estimator with a better goodness-of-fit. it is not guaranteed to be the best unbiased linear estimator for your data. If the residual errors of a linear regression model such as the Ordinary Least Square Regression model are heteroscedastic, the OLSR model is no longer efficient, i.e. Practical consequences of heteroscedasticity A small amount of heteroscedasticity in the model’s residuals can be tolerated if your model is otherwise performing well.
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