heteroscedasticity वाक्य
उदाहरण वाक्य
मोबाइल
- We reject the null hypothesis of homoscedasticity and conclude that heteroscedasticity is present.
- Thus heteroscedasticity is the absence of homoscedasticity.
- However, it has been said that students in econometrics should not overreact to heteroscedasticity.
- He is known for his contributions to the development of heteroscedasticity-consistent standard errors.
- He is known for his contributions in the development of heteroscedasticity-consistent standard errors.
- The model is the same as normal, which allows any pattern of correlation and heteroscedasticity:
- Generally, when testing for heteroscedasticity in econometric models, the best test is the White test.
- Heteroscedasticity-consistent standard errors are used to allow the fitting of a model that does contain heteroscedastic residuals.
- This validates the use of hypothesis testing using OLS estimators and White's variance-covariance estimator under heteroscedasticity.
- In regression analysis, " heteroscedasticity " refers to unequal variances of the random error terms ? i, such that
- In summary, to ensure efficient inference of the regression parameters and the regression function, the heteroscedasticity must be accounted for.
- It might also reveal outliers, heteroscedasticity, and other aspects of the data that may complicate the interpretation of a fitted regression model.
- Equivalently, heteroscedasticity refers to unequal conditional variances in the response variables " Y " " i ", such that
- Most of the methods of detecting heteroscedasticity outlined above can be modified for use even when the data do not come from a normal distribution.
- This provides White's ( 1980 ) estimator, often referred to as " HCE " ( heteroscedasticity-consistent estimator ):
- In the case of a linear model with a working independence variance structure, these are known as " heteroscedasticity consistent standard error " estimators.
- While WLS assumes independence of observations it does not assume equal variance and is therefore a solution for parameter estimation in the presence of heteroscedasticity.
- Some authors refer to this as conditional heteroscedasticity to emphasize the fact that it is the sequence of conditional variances that changes and not the unconditional variance.
- An initial scatter plot of the data indicates that there is heteroscedasticity in the data as the variance is not constant at each level of the predictor.
- For instance, while the ordinary least squares estimator is still unbiased in the presence of heteroscedasticity, it is inefficient because the true variance and covariance are underestimated.
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