Data Scientists’ Biggest Trouble, Dealing with Tobit Models

Tobit models have become a limitation for data scientists who wish to sort complex problems

Data science has become an essential part of any industry today. It's a process of transforming business data into assets that help organizations improve revenue, reduce costs, seize business opportunities, improve customer experience, and much more. Data science has also become of the most debated topics in the industry these days. Its popularity has grown intensely over the past couple of years as more and more companies have started implementing data science techniques to grow their business and increase customer satisfaction. But everything in this world has its pros and cons. Likewise, data science also has its own cons. The primary concern for almost all data scientists is using Tobit models.

The use of Tobit models has significantly increased to study censored and limited dependent variables in applied social science research over the past two decades. Tobit models basically constitute two primary components, which are mainly the process that determines whether the outcome variable is fully observed or not, and the process that determines the score on the dependent variable for individuals whose outcome is fully observed. But one limitation that mostly discourages data scientists from using this process is the assumption that the processes in both regimes of the outcome are equal up to a constant proportionality.

So, what are basically Tobit models?

The Tobit models are a family of statistical regression models that describe the relationship between censored dependent variables and independent variables. Tobit models are also known as censored regression models. They are primarily designed to estimate linear relationships between variables when there is either left-or-right censoring in the dependent variable. The term 'Tobit model' was initially coined by Arthur Goldberger about James Tobin. Tobin's idea was to modify the likelihood function so that it reflects the unequal sampling probability for each observation. But unfortunately, despite its advantages, Tobit models may lead to imprecise estimates leading scientists to misguided conclusions.

Data reveals that despite its shortcomings, the Tobit regression model is a frequently used tool for modeling censored variables to analyze measures of health status. The freelance economy industry is also increasingly using this technique. They integrate this technique to know how much to offer workers to increase their engagement in their platforms.