Which methods provide tailored imputations for each case with missing values?

The method that provides tailored imputations for each case with missing values is the estimate method. This technique uses the observed relationships within the data to create specific imputations based on individual characteristics of each case. By leveraging other available variables, the estimate method can generate values that reflect the underlying patterns present in the dataset, allowing for more nuanced and contextually appropriate imputations.

In contrast, methods like flexible may incorporate some level of customization but typically do not focus exclusively on the unique context of each missing value. Similarly, equal imputation methods might apply a single value (like the mean) across all cases, failing to take individual case differences into account. Synthetic imputations, while useful in certain contexts, often generate values based on statistical models or distributions rather than individual observations, leading to a less personalized approach. This distinction is crucial for ensuring that the imputed values maintain the integrity and predictive power of the dataset.