

GMM with skew t distribution 

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Siny Tsang posted on Monday, April 03, 2017  9:01 am



Hello, I am trying to modify the Mplus code in Muthen & Asparouhov (2015) to fit some longitudinal BMI data, but I'm a bit confused with the Mplus codes provided in the Appendix. The data is positively skewed, so we're looking to use DISTRIBUTION = SKEWT. With time varying covariates (i.e., age), I am assuming that we should use the codes in Tables B1 and B2 as an example. Instead of using the AT command to specify timevarying covariates, I see that the ON command is used. However, I don't quite understand why the regression paths are constrained to be equal for the different time points? Is there a way to get the equivalent of the slope estimates as in GMM with normal distribution? Thanks. 

Siny Tsang posted on Monday, April 03, 2017  10:03 am



Follow up to my previous message, I am guessing that the estimated coefficient for the ON path is the same as the estimated means for the slope if we use the usually GMM script: i s  w1 w2 w3 w4 AT age1 age2 age3 age4; So what happened to the variances of S if we use the ON method like this? i BY w1w4@1; w1 ON age1; w2 ON age2; w3 ON age3; w4 ON age4; Is this essentially a fixed slope within class (but vary between class)? If so, can we model a classvarying slope effect as well? Thanks. 


Which Tables B1 and B2 are you referring to  in which document? For the second question, say s  w1 ON age1; s  w2 ON age2; s  w3 ON age3; s  w4 ON age4; 

Siny Tsang posted on Monday, April 10, 2017  6:54 am



The Tables B1 and B2 were in the "Growth mixture modeling with nonnormal distributions" paper (Muthen & Asparuhov, 2014). Is there a way to get the classvarying slope effect with DISTRIBUTION = SKEWT? Thanks! 


I'm sorry but I don't see such tables in that paper  can you email me what you are looking at? Classvarying slopes can be handled. If not directly, then indirectly using a factor with classvarying loadings. 

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