A censored sample involves observations on the dependent variable that are missing or reported as a single value, often due to some known set of values of independent variables. This situation commonly arises in scenarios such as sold-out concert ticket sales, where the true demand is not observed. The Tobit model is frequently employed to address such challenges.
Detailed exploration of imputation, a crucial technique in data science, involving the replacement of missing data with substituted values to ensure data completeness and accuracy.
The Missing at Random (MAR) assumption is a key concept in statistical analysis that implies missing data is related to the observed data but not the missing data itself.
An in-depth exploration of the Missing Completely at Random (MCAR) assumption in statistical analysis, including historical context, types, key events, and comprehensive explanations.
An in-depth exploration of Missing Not at Random (MNAR), a type of missing data in statistics where the probability of data being missing depends on the unobserved data itself.
