There may be more than one pattern of data across a single dataset, and variables are likely to cluster according to these patterns. The structure of such data is constructed via unknown latent groups would lead to unknown regression models. Consequently, the residuals of these models are assumed to be a mixture of normal distribution.
In the context of robustness, the main distribution of random errors is assumed to be a normal distribution that is contaminated by another mechanism or distribution. So, there are two ways to tackle such a problem, either trimming or using the weights function to reduce the impact of the random errors that follow the contaminated distribution.
Mixture regression has the same conceptual approximately, but it has another method to estimate the model parameters such as EM.
My question is what are the differences between mixture regression and robust multiple regression, and how can I take the decision which one I have to use with my data? Please, I'm not talking about the percentage of another distribution is 50%.
Mixture regression and robust multiple regression are both techniques used to handle data with non-standard error structures, but they have different approaches and assumptions. Here are the key differences between them:
In summary, the choice between mixture regression and robust multiple regression depends on the specific characteristics of your data and the nature of the errors or deviations you are dealing with. Careful data exploration and model evaluation are crucial steps in making an informed decision.
Thank you so much for your consideration and accurate response @ Hugo Assunção
Hassan Uraibi, Mixture regression and robust multiple regression are two distinct statistical models used for different types of data.
- Mixture Regression is employed to model data that consists of a mixture of multiple populations. In this approach, data is grouped into different clusters, and each cluster has its own regression model. Mixture regression is particularly useful when dealing with heterogeneous or non-uniform data.
- Robust Multiple Regression, on the other hand, is designed to model data that is resilient to outliers. Outliers are data points that significantly differ from the majority of the data. Robust multiple regression is valuable when working with data prone to outliers, such as survey data or observational studies.
Here are the key differences between these two regression methods:
- Modeling Approach: Mixture regression models the data as a mixture of multiple populations, each with its own regression model. Robust multiple regression models the data as a single population but incorporates resistance to outliers into the model.
- Data Characteristics: Mixture regression is typically used for modeling heterogeneous data.
The choice between these models should be based on the nature of your data. If you believe that your data comprises a mixture of multiple populations, opt for mixture regression. Conversely, if you expect your data to contain outliers, robust multiple regression is the more suitable choice.
In your case, you've described your data as 'heterogeneous' and 'clustered according to unknown latent groups.' This suggests that mixture regression might be well-suited for your data. However, it's crucial to acknowledge that mixture regression is a more complex model compared to robust multiple regression. Adequate data availability is essential to accurately estimate the mixture regression model. For any issues further, which you would like to discuss with me personally, you can discuss with me on my WA; https://wa.me/+923440907874
Hassan Uraibi, Mixture regression and robust multiple regression are two distinct statistical models used for different types of data.
- Mixture Regression is employed to model data that consists of a mixture of multiple populations. In this approach, data is grouped into different clusters, and each cluster has its own regression model. Mixture regression is particularly useful when dealing with heterogeneous or non-uniform data.
- Robust Multiple Regression, on the other hand, is designed to model data that is resilient to outliers. Outliers are data points that significantly differ from the majority of the data. Robust multiple regression is valuable when working with data prone to outliers, such as survey data or observational studies.
Here are the key differences between these two regression methods:
- Modeling Approach: Mixture regression models the data as a mixture of multiple populations, each with its own regression model. Robust multiple regression models the data as a single population but incorporates resistance to outliers into the model.
- Data Characteristics: Mixture regression is typically used for modeling heterogeneous data.
The choice between these models should be based on the nature of your data. If you believe that your data comprises a mixture of multiple populations, opt for mixture regression. Conversely, if you expect your data to contain outliers, robust multiple regression is the more suitable choice.
In your case, you've described your data as 'heterogeneous' and 'clustered according to unknown latent groups.' This suggests that mixture regression might be well-suited for your data. However, it's crucial to acknowledge that mixture regression is a more complex model compared to robust multiple regression. Adequate data availability is essential to accurately estimate the mixture regression model. For any issues further, which you would like to discuss with me personally, you can discuss with me on my WA; https://wa.me/+923440907874
- Badges
- Science topic
- Similar topics
- Correction