BJ Data Tech Solution

Specialized on Data processing, Data management Implementation plan, Data Collection tools - electronic and paper base, Data cleaning specifications, Data extraction, Data transformation, Data load, Analytical Datasets, and Data analysis. BJ Data Tech Solutions teaches on design and developing Electronic Data Collection Tools using CSPro, and STATA commands for data manipulation. Setting up Data Management systems using modern data technologies such as Relational Databases, C#, PHP and Android.

multinomial logistic regression with greatest correspondence between predicted and observed values: why no lasso, stepwise, or elastic net?
multinomial logistic regression with greatest correspondence between predicted and observed values: why no lasso, stepwise, or elastic net?

Hi all,

I normally include covariates in my regression models based on theoretical considerations, but in this case there is no research or theory to suggest which covariates to use and I am only concerned with predicting values that best match the observed values. My goal is to use the predicted values for observations that are missing values on the dependent variable (i.e., imputation by linear regression). I looked into Stata's lasso, stepwise, and elasticnet and none allow for a mlogit model. I'm sure there is a good reason for this, but I don't know what it is. Can anyone suggest a method for a data-driven prediction model for mlogit?

Thank you.
Tom

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