Joint mean and variance modeling in SAS: any suggestions on which PROCs should I use?

[Season]

I am trying to build a joint mean and variance model (also called double generalized linear model). This is a model that can simultaneously model the mean and variance of the dependent variable by specifying potentially different predictors for the mean and variance of the dependent variable and then maximize the joint likelihood to achieve goals like accounting for heteroscedasticity. I am not sure on what procedure(s) I should use in SAS to build such models. Any suggestions?

[Ksharp]

Here is an example for COUNT and BINARY data.

[Season]

Thank you for your rapid help! Unfortunately, this is not I am looking for. The information you provided concerns the model that can simulataneously model two distributions while for the time being, I am looking for a model that can simulataneously model two parameters of a single distribution, namely its mean and variance.

[PaigeMiller · Accepted solution]

https://support.sas.com/resources/papers/proceedings15/3381-2015.pdf

[Season]

Thank you so much, Paige!

[StatDave]

Also, for the ordinary linear regression model, you can use the HETERO statement in PROC QLIM or PROC HPQLIM in SAS/ETS to separately model the variance from the mean model. Various link functions for the variance can be specified. Also available in SAS Viya in PROC CQLIM. See the discussion of heterogeneity in the Details section of the procedure documentation.

[Season]

Thank you for your additional information! But aren't PROC QLIM and PROC HPQLIM used for modeling limited dependent variables like zero-inflated data? Can they build joint mean and variance models for uncensored data?